Abstract

   Recent large-scale genomic association studies found evidence for a
   genetic link between increased risk of type 2 diabetes and decreased
   risk for adiposity-related traits, reminiscent of metabolically obese
   normal weight (MONW) association signatures. However, the target genes
   and cellular mechanisms driving such MONW associations remain to be
   identified. Here, we systematically identify the cellular programmes of
   one of the top-scoring MONW risk loci, the 2q24.3 risk locus, in
   subcutaneous adipocytes. We identify a causal genetic variant,
   rs6712203, an intronic single-nucleotide polymorphism in the COBLL1
   gene, which changes the conserved transcription factor motif of POU
   domain, class 2, transcription factor 2, and leads to differential
   COBLL1 gene expression by altering the enhancer activity at the locus
   in subcutaneous adipocytes. We then establish the cellular programme
   under the genetic control of the 2q24.3 MONW risk locus and the
   effector gene COBLL1, which is characterized by impaired actin
   cytoskeleton remodelling in differentiating subcutaneous adipocytes and
   subsequent failure of these cells to accumulate lipids and develop into
   metabolically active and insulin-sensitive adipocytes. Finally, we show
   that perturbations of the effector gene Cobll1 in a mouse model result
   in organismal phenotypes matching the MONW association signature,
   including decreased subcutaneous body fat mass and body weight along
   with impaired glucose tolerance. Taken together, our results provide a
   mechanistic link between the genetic risk for insulin resistance and
   low adiposity, providing a potential therapeutic hypothesis and a
   framework for future identification of causal relationships between
   genome associations and cellular programmes in other disorders.
     __________________________________________________________________

   Obesity and type 2 diabetes (T2D)-related traits are intimately linked
   by both environmental and genetic factors. The global prevalence of
   obesity and T2D has risen dramatically over the past century and both
   diseases constitute a serious increasing public health concern
   worldwide, with T2D predicted to rise in prevalence from 451 to 693
   million people between 2017 and 2045 ([75]ref. 1). Most epidemiological
   and genetic studies have linked obesity to the pathogenesis of T2D
   through positive phenotypic correlations between adiposity and T2D.
   However, a small number of loci have been identified that do not follow
   this pattern or even correlate in the opposite phenotypic
   direction^[76]2. Also, up to 45% of individuals with obesity do not
   present with poor glycaemic or lipid profiles, commonly called the
   metabolically healthy obese (MHO). Concurrently, up to 30% of
   normal-weight individuals present with significant cardiometabolic risk
   factors, known as the metabolically obese normal weight
   (MONW)^[77]3-[78]10.

   Genome-wide association studies (GWAS) identified more than 700 genomic
   loci associated with glycaemic traits and T2D and more than 1,000 loci
   associated with adiposity-related traits, including body mass index
   (BMI) as a proxy of overall obesity, waist:hip ratio (WHR) as a proxy
   of body fat distribution, body fat percentage and direct measures of
   subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT)
   mass^[79]2,[80]11-[81]20. A small proportion of T2D genetic risk loci
   have been associated with decreased body fat percentage and a decreased
   SAT:VAT ratio^[82]2,[83]21-[84]23. Notably, these MONW/MHO genetic risk
   loci are characterized by distinct association signatures implying
   locus-specific cellular programmes^[85]2,[86]21-[87]23.

   The metabolic risk haplotype at 2q24.3 displays cross-phenotype
   association signatures that are reminiscent of the MONW phenotype and
   are associated with an increased risk of T2D, increased homeostatic
   model assessment for insulin resistance, increased WHR adjusted for BMI
   and decreased body fat percentage, decreased estimated SAT mass and
   cardiometabolic trait risk^[88]2,[89]15,[90]24-[91]26. Consistent with
   these associations, the 2q24.3 locus falls into the lipodystrophy
   cluster of T2D loci^[92]27, suggesting adipocytes as the mediating cell
   type at this locus. Notably, among the 20 loci identified in the T2D
   lipodystrophy process-specific cluster^[93]27, the 2q24.3 locus is the
   top-scoring one, suggesting the strongest contribution to a
   lipodystrophic-like phenotype among the T2D GWAS loci.

   However, like the vast majority of genetic risk loci identified through
   GWAS, the mechanism of the 2q24.3 metabolic risk locus is currently
   unknown. There are multiple factors that still confound the goal of
   converting genetic associations into specific knowledge, that is,
   pinpointing the causal variant(s), target gene(s) and mechanism(s).
   First, the resolution of association mapping is inherently limited by
   the haplotype structure of the human genome because a common variant in
   the population is usually strongly genetically linked to many
   neighbouring variants. Second, the genetic architecture of common
   diseases is fundamentally different to that of rare diseases, as over
   80% of associated regions do not contain any protein-altering common
   variants^[94]28. Importantly, genetics and descriptive catalogues of
   molecular activity alone are, for non-coding signals at least,
   insufficient to build compelling proof for a chain of causation that
   stretches from variant to phenotype. In this study, we set out to
   combine statistical and experimental methods to identify the mechanism
   by which 2q24.3 confers decreased adiposity and increased risk of T2D
   ([95]Extended Data Fig. 1a), possibly enabling further development of
   therapeutic strategies to alter glycaemic control.

Results

The 2q24.3 risk locus maps to adipocyte enhancer signatures

   To identify diseases and traits associated with the 2q24.3 locus tagged
   by the variant rs3923113, we visualized large-scale phenome-wide
   associations from the UK Biobank (UKB) and from a meta-analysis of a
   series of metabolic traits ([96]Fig. 1a and [97]Extended Data Fig. 1b).
   We observed that the 2q24.3 locus was associated with increased T2D
   risk, increased fasting insulin levels and a series of body fat-related
   traits, including increased WHR adjusted for BMI, but decreased trunk
   fat percentage, arm fat percentage, hip circumference and whole-body
   fat mass, suggesting a complex pleiotropic risk locus consistent with a
   MONW association signature, that is, a lean, metabolically unhealthy
   phenotype.

Fig. 1 ∣. The pleiotropic 2q24.3 MONW locus is associated with increased risk
for T2D and decreased adiposity-related traits, and maps to sparse enhancer
signatures in adipocytes.

   Fig. 1 ∣
   [98]Open in a new tab

   a, Phenome-wide association studies of trait associations at the
   rs3923113-tagged haplotype in the UKB^[99]87. The colours represent
   trait classes while individual rs3923113 variant association P values
   are shown on the y axis. The direction of effect is indicated by the
   orientation of the triangles: upward, increase; downward, decrease. b,
   Chromatin state annotations for the 55-kb-long MONW risk locus. Genomic
   intervals are shown across 127 human cell types and tissues reference
   epigenomes profiled by the Roadmap Epigenomics project, based on a
   25-state chromatin state model (see [100]Extended Data Fig. 1d for the
   color code of the chromatin states) learned from 12 epigenomic marks
   using imputed signal tracks at 25-nucleotide resolution^[101]88.
   Chromatin states considered included Polycomb repressed states (grey,
   H3K27me3), weak enhancers (yellow, H3K4me1 only), strong enhancers
   (orange, also H3K27ac) and transcribed enhancers (green, also H3K36me3)
   ([102]https://www.freepatentsonline.com/y2022/0243178.html). ES,
   embryonic stem; ESC, embryonic stem cell; GI, gastrointestinal; iPSC,
   induced pluripotent stem cell; MSC, mesenchymal stem cell.

   The 2q24.3 locus tagged by rs3923113 encompasses 55 kb, spanning from
   the COBLL1 intronic regions to the intergenic region between GRB14 and
   COBLL1 ([103]Extended Data Fig. 1c). The MONW locus harbours 20
   non-coding single0nucleotide polymorphisms (SNPs) in high linkage
   disequilibrium (LD) (r^2 > 0.8, 1000 Genomes Project Phase 1 EUR).
   These single-nucleotide variants (referred to as the candidate
   regulatory variants) define two alternative haplotypes: the ancestral
   haplotype 1 (frequency 38% in European individuals), associated with a
   decreased risk for the MONW association pattern; and haplotype 2
   (frequency 62%), associated with an increased MONW risk. To connect
   genetic variants at the 2q24.3 locus to relevant cell types and cell
   states, we examined chromatin state maps across 127 reference
   epigenomes from the Roadmap Epigenomics and the ENCODE consortium
   ([104]Fig. 1b and [105]Extended Data Fig. 1d). We found that the locus
   maps to multiple enhancer signatures, including active enhancers in
   mesenchymal stem cells, adipocyte progenitors and adipocytes ([106]Fig.
   1b). Several of the 20 non-coding variants map within or in the
   vicinity of regions with active enhancer chromatin states, suggesting
   that the 2q24.3 locus acts in adipocytes through gene regulatory
   mechanisms.

   Next, we examined whether the two haplotypes (risk compared to
   non-risk) showed differences in chromatin structure during adipocyte
   differentiation. Specifically, we performed assays for enhancer
   activity (H3K27 acetylation (H3K27ac) chromatin immunoprecipitation
   followed by sequencing (ChIP–seq)) and chromatin accessibility (assay
   for transposase-accessible chromatin with sequencing (ATAC–seq)) on
   adipose-derived mesenchymal stem cells (AMSCs) from heterozygous
   individuals across a time course of differentiation (before induction
   (day 0), early differentiation (day 2), intermediate differentiation
   (day 6) and terminal differentiation (day 14)), and compared the
   numbers of reads from the two haplotypes ([107]Extended Data Fig. 1e).
   The MONW risk haplotype (haplotype 2) was associated with a decrease in
   H3K27ac, a proxy of enhancer activity, and chromatin accessibility,
   with the MONW risk haplotype enriched by roughly 1.5-fold. The
   allele-specific difference in chromatin accessibility was reproducible
   across three heterozygous lines ([108]Supplementary Tables 1 and
   [109]2), was most pronounced at day 0 of differentiation and declined
   after induction of differentiation ([110]Extended Data Fig. 1f). These
   results indicate that haplotype 1 is associated with an active enhancer
   state, whereas haplotype 2 is associated with a weak enhancer state
   primarily in adipocyte progenitors.

The rs6712203 variant affects adipocyte COBLL1 expression

   To identify which of the 20 candidate regulatory variants is likely to
   mediate the differential enhancer activity in adipocyte progenitors, we
   used two orthogonal computational approaches to prioritize variants,
   phylogenetic module complexity analysis (PMCA)^[111]28-[112]30 and
   Bassett^[113]31 ([114]Fig. 2a-[115]c and [116]Supplementary Tables 3
   and [117]4). PMCA assesses evolutionary conservation of sequence, order
   and distance (in human and at least one other vertebrate species) of
   groups of at least three transcription factor binding motifs within a
   120-bp region. Basset uses a sequence-based deep convolutional neural
   network (CNN) approach to predict the effects of non-coding variants on
   regulatory activity by training on the sequence content of a given
   epigenomic mark in a tissue or cell type of interest. After training on
   genome-wide chromatin accessibility (ATAC–seq) data in numerous cell
   types, including AMSC progenitors, before induction (day 0), one
   variant, rs6712203, stood out as consistently showing the highest score
   for PMCA and Bassett ([118]Fig. 2a). Bassett predicted that the T
   allele on the protective haplotype increases chromatin accessibility
   relative to the C allele on the risk haplotype in adipocyte
   progenitors. These sequence-based estimates of rs6712203 C>T
   single-nucleotide change importance are consistent with the variant
   overlapping an active enhancer associated with H3K27ac and H3K4
   mono-methylation in adipocyte progenitors. In line with the variant
   importance at rs6712203, conditional analyses of anthropometric and
   glycaemic traits defining MONW in the UKB confirmed an rs6712203 C/T
   association consistent with a primary effect in female participants for
   fat mass (β = 0.022, P = 4.1 × 10^−08 in females and β = 0.0084, P =
   0.052 in males, difference P = 0.019), hip circumference (β = 0.037, P
   =4.3 × 10^−15 in females and β = 0.0097, P = 0.008 in males, difference
   P = 4.3 × 10^−06) and T2D in both females and males (females β = −0.11,
   P = 1.4 × 10^−07 and males β = −0.068, P = 2.4 × 10^−05, difference P =
   0.098, [119]Supplementary Table 5 and [120]Extended Data Figs. 1g and
   [121]2). Furthermore, we observed that the rs6712203 association with
   T2D was dependent on BMI (β = −0.0028, P = 0.70 in normal weight and
   underweight and β = −0.093, P = 2.92 × 10^−06 in obese, difference P =
   2.0 × 10^−05, [122]Supplementary Table 6).

Fig. 2 ∣. rs6712203 is a functional variant at the 2q24.3 MONW locus.

   Fig. 2 ∣
   [123]Open in a new tab

   a, Phylogenetic conservation analysis and CNN-based prediction of
   chromatin accessibility for 20 highly linked (LD = r^2 > 0.8) variants
   at the 2q24.3 locus (tag SNP rs3923113). Phylogenetic conservation
   scores of jointly conserved motifs using PMCA are shown on the x
   axis^[124]29. PMCA was used to identify orthologous regions in 20
   vertebrates and to scan the 120-bp sequence context for groups of
   transcription factor binding site motifs whose sequence, order and
   distance range is conserved across species. Scores indicate the count
   of non-overlappingjointly conserved transcription factor binding site
   motifs whose relative positions within the window are conserved.
   Predicted relative change in chromatin accessibility (SNP accessibility
   difference SAD scores) in preadipocytes (day 0 of differentiation) for
   each SNP comparing alleles on haplotype 1 and haplotype 2 is shown on
   the y axis. A deep CNN Basset^[125]31 was trained on genome-wide
   ATAC–seq data assayed in preadipocytes. Alleles were assigned to each
   SNP in the haplotype and evaluated for predicted accessibility. b, Both
   PMCA and Basset predicted rs6712203 as a functional variant. For the C
   allele, there are no nucleotide variants that reduce binding in the
   rs6712203 region. Each position on the x axis represents a nucleotide
   and the four values in the heatmap correspond to all possible
   substitutions. c, For the T allele, in silico saturation mutagenesis
   suggests that binding loss of the POU2F motif that overlaps rs6712203,
   including the C allele itself, results in reduced predicted chromatin
   accessibility. d, Intragenomic replicates^[126]35 predict a higher
   binding affinity of POU2 family transcription factors for the T than C
   allele to both strands. Offsets from instances of the given k-mer
   sequence are shown on the x axis. Estimated affinity of binding
   ([127]https://www.freepatentsonline.com/y2022/0243178.html) is shown on
   the y axis. A model with 8-mers is shown; alternatives with 6-mers
   through to 9-mers are shown in [128]Extended Data Fig. 3b. e,
   Generation of rs6712203 CRISPR–Cas9 engineered lines starting from SGBS
   preadipocytes (heterozygous for rs6712203) edited to the homozygous
   risk (CC, yellow) and non-risk (TT, blue) alleles and qPCR-based gene
   expression measurement of COBLL1 conditional on the regulator POU2F2. n
   = 3 biologically independent experiments. f, Schematic model of the
   regulatory circuitry under the genetic control of rs6712203.

   We next used three-dimensional genome conformation data from Hi-C
   assays in normal human epidermal keratinocytes and human
   fibroblasts^[129]32 to define the physical boundaries of potential
   proximal and long-distant target genes. We found that the locus lies in
   a well-defined contact domain containing only two genes, cordon-bleu
   WH2 repeat protein like 1 (COBLL1) and growth factor receptor bound
   protein 14 (GRB14) ([130]Extended Data Fig. 3a), without any evidence
   for long-range chromatin interactions. To dissect which of these two
   genes are targeted by rs6712203, we undertook a CRISPR interference
   (CRISPRi) approach by engineering immortalized human preadipocytes (in
   human white adipose tissue (hWAT))^[131]33 to stably express a
   catalytically dead Cas9 fused to a Krüppel-associated box (KRAB)
   domain^[132]34 ([133]Extended Data Fig. 3b). Transduction of Cas9
   Endonuclease Dead (dCas9)-hWAT preadipocytes with increasing amounts of
   lentiviral particles carrying single-guide RNAs (sgRNAs) targeting the
   transcriptional start sites (TSS) of COBLL1 and GRB14 genes led to
   dose-dependent downregulation of mRNA expression by 72% and 96%
   compared to non-targeting negative controls ([134]Extended Data Fig.
   3c). At the highest dose, COBLL1 protein was reduced by 56% without
   effects of sgRNAs targeting the GRB14 TSS on COBLL1 expression
   ([135]Extended Data Fig. 3d). After confirming the knockdown efficiency
   and specificity of our system, we next designed a panel of six sgRNAs
   surrounding the rs6712203 variant ([136]Extended Data Fig. 3e) and
   additional non-targeting and COBLL1/GRB14 TSS controls to assess its
   effects on COBLL1 and GRB14 mRNA expression. All TSS sgRNAs led to a
   reduction in COBLL1 and GRB14 mRNA content by 64% and 94% without any
   interference on each other ([137]Extended Data Fig. 3f,[138]g). More
   importantly, all tested sgRNAs, with exception of sgRNA 1, led to
   significant downregulation of COBLL1 over a range of 20–40% compared to
   negative controls ([139]Extended Data Fig. 3f), while no effects were
   observed on GRB14 ([140]Extended Data Fig. 3g), thus pointing to COBLL1
   as the primary target of rs6712203.

   We next performed in silico saturation mutagenesis to evaluate the
   predicted change in chromatin accessibility from mutation at every
   position into each alternative nucleotide within a 30-bp region
   surrounding rs6712203 using ATAC–seq data during AMSC differentiation.
   We found that the rs6712203 T allele is critical for a POU2F2 motif
   ([141]Fig. 2d). The C allele of this SNP converts the chromatin in this
   site into a less accessible chromatin, supporting a model in which a
   transcription factor, possibly POU2F2, differentially binds to these
   allelic variants of rs6712203. To estimate the preferential binding
   affinity of POU2F2 to the C risk compared to the T non-risk allele, we
   used the intragenomic replicate (IGR) method^[142]35 on publicly
   available POU2F2 ChIP–seq data from the ENCODE project. By comparing
   the frequency of k-mers matching the rs6712203 T allele versus the C
   allele, we confirmed that POU2F2 preferentially binds to the T allele
   (9-mer change in affinity −0.38, two-tailed permutation P < 0.0034)
   ([143]Fig. 2d and [144]Extended Data Fig. 3h). These data suggest an
   increased POU2F2 binding to the rs6712203 T non-risk allele and suggest
   POU2F2 as the upstream regulator of variant action at this locus.

   We next sought to establish rs6712203 causality by directly confirming
   that the haplotype-specific effects on enhancer activity and POU2F2
   binding are mediated by rs6712203 using CRISPR-based genome editing at
   this SNP. We edited the human Simpson–Golabi–Behmel syndrome (SGBS)
   preadipocytes (n = 5) that are heterozygous at rs6712203 to create
   isogenic lines for the homozygous TT (non-risk genotype) and homozygous
   CC (risk genotype) alleles. In line with the CRISPRi rs6712203 enhancer
   repression experiments outlined above, we observed that cells
   harbouring the CC homozygous risk showed 2.4-fold lower COBLL1
   expression levels compared to the TT non-risk genotype ([145]Fig. 2e),
   pointing towards COBLL1 as a target gene of the rs6712203 regulatory
   circuitry. To test a cis/trans-conditional effect of the rs6712203
   variant and the upstream regulator POU2F2 on target gene expression, we
   performed targeted small interfering RNA (siRNA)-mediated ablation of
   POU2F2 in SGBS cells and found that silencing of POU2F2 in TT allele
   carriers reduces COBLL1 gene expression to the level of CC allele
   carriers in preadipocytes ([146]Fig. 2e), confirming POU2F2 as a
   functional regulator at the locus.

   Together, our computational and experimental approaches support a model
   where the causal MONW variant rs6712203 regulates COBLL1 gene
   expression in a POU2F2-dependent manner ([147]Fig. 2f).

COBLL1 affects actin remodelling in subcutaneous adipocytes

   To understand the role of COBLL1 in adipocyte cellular programmes, we
   examined the gene expression and cellular localization of COBLL1 in
   differentiating adipocytes. We observed that COBLL1 is expressed at all
   stages of adipocyte differentiation with an increase in mRNA over the
   course of differentiation ([148]Extended Data Fig. 4a). We observed
   consistently higher COBLL1 mRNA levels in subcutaneous compared to
   visceral adipocytes across the adipocyte differentiation process
   ([149]Extended Data Fig. 4a). Overall, we found an enrichment of COBLL1
   gene expression in adipose tissue compared to 146 other tissues and
   cell types^[150]36 ([151]Extended Data Fig. 4b).

   To connect the 2q24.3 locus to cellular functions in adipose tissue, we
   used genome-wide coexpression matrices in adipocytes matched with a
   series of cellular assays. We identified COBLL1 coregulated genes in
   genome-wide expression data from primary human AMSCs in a cohort of 12
   healthy, non-obese individuals. COBLL1 coexpressed genes were highly
   enriched in biological processes related to regulation of actin
   cytoskeleton and regulation of lipolysis in adipocytes, including
   integrin subunit alpha M (ITGAM), phosphatidylinositol
   −4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA),
   rho-associated protein kinase 2 (ROCK2), integrin alpha-1 (ITGA1), and
   rho guanine nucleotide exchange factor 7 (ARHGEF7), proto-oncogene,
   adaptor protein (CRK), fibroblast growth factor receptor 2 (FGFR2) and
   rho guanine nucleotide exchange factor 6 (ARHGEF6) ([152]Fig. 3a,
   [153]Extended Data Fig. 4c,[154]d and [155]Supplementary Tables
   7-[156]10), which are implicated in actin remodelling processes and
   insulin responsiveness^[157]37-[158]40. This is consistent with recent
   studies showing that COBLL1 possesses a Wiskott–Aldrich syndrome
   protein homology 2 actin monomer-binding domain, and promotes
   filamentous actin (F-actin) formation in Cos-7, neuronal and prostate
   cancer cells^[159]41,[160]42. Finally, we found a significantly
   decreased relative COBLL1 gene expression after a weight loss of 6.9 ±
   1.9 kg in 18 women with obesity (BMI = 34.9 ± 3.8 kg m^−2)
   ([161]Extended Data Fig. 4e).

Fig. 3 ∣. The 2q24.3 effector gene COBLL1 affects actin remodelling processes
in differentiating adipocytes.

   Fig. 3 ∣
   [162]Open in a new tab

   a, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of
   genes correlated with COBLL1 in adipocytes using
   Enrichr^[163]89,[164]90. n = 30 donors, P values were derived from a
   hypergeometric test. b, Schematic of siCOBLL1 experiments in AMSCs
   across differentiation. AMSCs from a normal-weight female donor were
   silenced 3 d before induction and LipocyteProfiling (days 0, 3, 8 and
   14 of differentiation), n = 3. c, Image-based profiles of
   siCOBLL1-treated compared to non-targeting siRNA-treated AMSCs on day
   14; two-sided t-test, significance level FDR < 5%, n = 3. d, Pie chart
   illustrating differential features per channel and measurement class
   comparing siCOBLL1 and the non-targeting siRNA control on day 14. e,f,
   Spatial intensity distribution of AGP informative for the actin
   cytoskeleton in the centre of the cytoplasm:
   Cytoplasm_RadialDistribution_FracAtD_AGP_1of4 (e) and juxtaposed to the
   plasma membrane (Cytoplasm_RadialDistribution_RadialCV_AGP_4of4) (f).
   Two-sided t-test, n = 3 biologically independent experiments, median ±
   95% confidence interval (CI). g, Representative images of COBLL1
   knockdown and non-targeting siRNA control at days 0 and 14 of
   differentiation. COBLL1 staining: anti-COBLL1 primary and donkey
   anti-rabbit IgG H&L secondary antibodies. Actin staining:
   phalloidin–Atto 565. Olympus FLUOVIEW FV1000 CLSM Inverse microscope
   (40× magnification). Images were processed with Image J. Dotted square:
   image zoom-in. h,i, Texture of BODIPY stain
   (Cells_Texture_Correlation_Lipid_10_01) (h) and granularity measures of
   the BODIPY stain (Cells_Granularity_3_BODIPY) (i) in siCOBLL1 knockdown
   and non-targeting siRNA; two-sided t-test, n = 3 biologically
   independent experiments, median ± 95% CI.j, Oil Red O staining in SGBS
   adipocytes after stable lentiviral COBLL1 knockdown (shCOBLL1) versus
   empty vector control (shEV); scale bar, 15 mm. k, GPDH metabolic
   activity in differentiated shCOBLL1 compared to non-targeting siRNA
   adipocytes. Paired Student’s t-test, median ± 95% CI, n = 3. l, Basal
   and insulin-stimulated ^3H-2-DG uptake in differentiated shCOBLL1
   compared to shEV adipocytes. One-way analysis of variance (ANOVA) with
   Tukey’s honestly significant difference (HSD) test, median ± 95% CI, n
   = 3 biologically independent experiments. m, Basal and
   isoproterenol-stimulated lipolysis rate measured using glycerol release
   in differentiated shCOBLL1 compared to shEV adipocytes. One-way ANOVA
   with Tukey’s HSD test, median ± 95% CI, n = 3 biologically independent
   experiments. n, Western blots for lipolysis-relevant proteins assayed
   in basal or isoproterenol/IBMX-stimulated shCOBLL1 versus shEV
   adipocytes (n = 2).

   To identify morphological and cellular traits associated with altered
   COBLL1 expression, we used siRNA-mediated knockdown of COBLL1 in AMSCs
   coupled with a high-content imaging assay that we recently developed,
   LipocyteProfiler^[165]43. LipocyteProfiler reports on general and
   adipocyte-specific cellular traits for preadipocytes before
   differentiation (day 0), and at three time points of adipocyte
   differentiation (3 d (day 3), 9 d (day 9) and 14 d (day 14) after
   adipogenic induction) ([166]Fig. 3b). We examined 1,175 quantitative
   features, spread across two cellular compartments (whole-cell and
   cytoplasm only) and five dyes informative for morphological and
   adipocyte cellular traits (BODIPY, phalloidin, wheat germ agglutinin
   (WGA), SYTO14 and MitoTracker ([167]Methods) imaged in four
   fluorescence channels ([168]Fig. 3b). We observed that COBLL1 knockdown
   in proliferating preadipocytes with 80% knockdown efficiency
   ([169]Extended Data Fig. 5a) results in changes of diverse
   morphological and cellular features across adipocyte differentiation
   with a peak at the later stages of differentiation ([170]Fig. 3c and
   [171]Extended Data Fig. 5b-[172]d). On day 14 of differentiation, 156
   features differed significantly (false discovery rate (FDR) < 5%)
   between COBLL1 knockdown and non-targeting control, spread across lipid
   (23.1%), actin, Golgi, plasma membrane (AGP)-related (33.3%) and
   mitochondrial (16.0%) channels ([173]Fig. 3d and [174]Supplementary
   Table 11). For AGP-related cellular processes, we observed that COBLL1
   knockdown alters the spatial intensity distribution of AGP across the
   cytoplasm. We previously observed that these AGP-related features can
   be indicative of actin cytoskeleton remodelling during
   differentiation^[175]43. After COBLL1 silencing, we observed increased
   actin-associated intensity in the centre of the cell (day 9 P = 0.037;
   [176]Fig. 3e) and decreased actin-associated intensity at the cell
   cortex (day 9 P = 0.013, day 14 P = 0.026; [177]Fig. 3f) in
   differentiated subcutaneous adipocytes. This indicates that COBLL1 has
   a role in remodelling the actin cytoskeleton during adipocyte
   maturation, as reduced levels of COBLL1 disturb the disassembly of
   F-actin stress fibres across the cytoplasm and the reassembling to
   cortical F-actin (F-actin juxtaposed to the plasma membrane).

   The disturbed actin remodelling process was accompanied by lowered
   differentiation capacity as shown by decreased lipid droplet formation.
   More specifically, we confirmed that COBLL1 knockdown was associated
   with a decreased disassembly of stiff F-actin stress fibres reaching in
   the middle of the cell body, at the expense of F-actin structure
   assembly at the cell cortex in differentiated cells ([178]Fig. 3g and
   [179]Extended Data Fig. 5e). Consistent with the notion that
   remodelling of F-actin stress fibres into cortical actin is linked to
   adipocyte differentiation, COBLL1-ablated adipocytes showed significant
   changes in both texture of lipid-related pixels
   (Cells_Texture_Correlation_Lipid; pixel intensities of BODIPY stain
   within the cell are more similar, day 3 P = 0.017 and day 14 P= 0.039;
   [180]Fig. 3h) and lipid-related granularity measures (a class of
   metrics that captures the typical sizes of bright spots for the BODIPY
   stain) within the cell compared to adipocytes expressing COBLL1. More
   specifically, we observed that silencing of COBLL1 results in
   adipocytes with fewer smaller-sized lipid droplets (Cells_Granularity_
   Lipid 3, day 14 P = 0.0483; [181]Fig. 3i) and more
   medium-to-large-sized lipid droplets in mature adipocytes
   (Cells_Granularity_Lipid 11, day 3 P = 0.0412 and day 14 P = 0.0486;
   [182]Fig. 3i), and an overall decrease in lipid droplet numbers
   (Cellls_Children_LargeLipidObjects_Count, P = 0.006; [183]Extended Data
   Fig. 5f). These data indicate a disturbed lipid droplet formation and
   adipogenic differentiation in COBLL1-ablated cells, driven by an
   altered cytoskeleton remodelling.

   To investigate if the COBLL1 effect on actin remodelling in adipocytes
   impacts adipocyte cellular programmes related to metabolic disease, we
   performed stable ablation of COBLL1 using lentivirus (shCOBLL1) in
   differentiating adipocytes. We observed that ablation of COBLL1
   resulted in a decreased capacity to differentiate into metabolically
   active, round-shaped, lipid-filled mature adipocytes, as shown by
   decreased Oil Red O staining of accumulated triglycerides ([184]Fig.
   3j), adipocyte differentiation marker gene expression ([185]Extended
   Data Fig. 5g) and glyceraldehyde-3-phosphate dehydrogenase (GPDH)
   activity measurements (2.1-fold, P = 3 × 10^−3; [186]Fig. 3k). We
   further found a correlation between the mRNA levels of COBLL1 and
   leptin, an adipokine produced in proportion to the size of fat
   depots^[187]44 in SAT (r = 0.74, P = 5 × 10^−5) ([188]Extended Data
   Fig. 5h,[189]i). This effect on leptin is consistent with GWAS of serum
   leptin levels (rs6712203 C allele β = 0.0308, P = 9 × 10^−6 and β =
   0.0236, P = 1 × 10^−5 (BMI-adjusted)) in Kilpeläinen et al.^[190]45;
   and β = 0.0285, P = 0.005889 in Folkersen et al.^[191]46. We further
   found a 3.4-fold (P = 2 × 10^−7) decrease of insulin-responsive glucose
   uptake in shCOBLL1 adipocytes compared to non-targeting control, as
   measured by radiolabelled ^3H-2-deoxyglucose (^3H-2-DG) uptake assays
   ([192]Fig. 3l). The failure of shCOBLL1 adipocytes to respond to
   insulin may have resulted from both lowered differentiation efficiency
   and failure of the cortical actin remodelling that mediates GLUT4
   vesicle trafficking. Finally, we observed a failure of shCOBLL1
   adipocytes to break down triglycerides to free fatty acids and glycerol
   through lipolysis after β-adrenergic stimulation using isoproterenol
   and the phosphodiesterase inhibitor IBMX compared to their control
   cells ([193]Fig. 3m). This was accompanied with decreased protein
   levels of the lipolytic enzymes adipocyte triglyceride lipase,
   hormone-sensitive lipase (HSL), protein kinase A-dependent serine
   phosphorylated HSL (pHSL660, pHSL563) and on the lipid
   droplet-associated protein perilipin-1 (PLIN) ([194]Fig. 3n). Notably,
   we did not observe an effect on cellular and morphological features
   when silencing COBLL1 after induction of differentiation ([195]Extended
   Data Fig. 5j,[196]k), suggesting that COBLL1 acts early in
   differentiation, with phenotypic effects primarily manifesting in
   mature adipocytes. We also did not observe an effect when COBLL1 was
   ablated in visceral AMSCs ([197]Extended Data Fig. 5l), indicating that
   COBLL1 is critically involved in actin remodelling processes in
   subcutaneous adipocytes.

   We additionally examined the effect of GRB14 stable knockdown in AMSCs
   and observed that GRB14 ablation (knockdown efficiency of 61%) did not
   significantly decrease in adipocyte differentiation capacity as
   measured by Oil Red O staining, GPDH activity ([198]Extended Data Fig.
   5m,[199]n) or insulin-responsive glucose uptake and GLUT4 gene
   expression ([200]Extended Data Fig. 5o,[201]p), supporting COBLL1 as
   the effector gene at this locus.

The MONW haplotype affects the adipocyte actin cytoskeleton

   To confirm that the changes on the actin cytoskeleton and subsequent
   effects on adipocyte functions are under the genetic control of the
   rs6712203 MONW risk haplotype, we used LipocyteProfiler^[202]43 to
   phenotypically profile primary human adipocytes differentiation
   throughout from individuals carrying the risk haplotype (n = 6)
   compared the non-risk haplotype (n = 7) ([203]Fig. 4a). The data
   revealed that AGP and lipid features informative for the actin
   cytoskeleton and lipid accumulation differed in subcutaneous adipocytes
   from rs6712203 metabolic risk versus non-risk haplotype carriers
   ([204]Fig. 4b,[205]c and [206]Extended Data Fig. 6a-[207]c). More
   specifically, we found that 77 morphological features, spread across
   lipid (16.9%), actin-associated AGP (45.5%) and mitochondrial (26.0%)
   channels, significantly differed between the haplotypes on day 14 of
   differentiation (FDR = 5%; [208]Supplementary Table 12). We did not
   observe any significant difference in visceral adipocytes ([209]Fig. 4d
   and [210]Extended Data Fig. 6d-[211]f), which is consistent with the
   depot-specific effect of COBLL1 knockdown ([212]Extended Data Fig. 5k).
   Notably, we found that the risk haplotype associates with increased
   actin-associated intensity in the centre of the cell (day 0 P = 0.018,
   day 3 P = 0.042, day 9 P = 0.011 day 14 P = 0.009; [213]Fig. 4e) and
   decreased actin-associated intensity at the cell cortex (day 9 P =
   0.024, day 14 P = 0.009; [214]Fig. 4f). These data recapitulate our
   findings after COBLL1 knockdown and confirms that adipocytes from risk
   allele carriers are characterized by less cortical actin, which is
   required for insulin-stimulated glucose uptake in those cells and
   therefore directly relevant to fasting insulin levels and T2D.
   Furthermore, we observed a risk haplotype association with higher lipid
   object counts (day 8 P = 0.043, day 14 P = 0.034; [215]Fig. 4g), which
   is representative of several lipid droplets, and higher lipid-related
   intensity (day 8 P = 0.001; [216]Fig. 4h), which is indicative of
   dysfunctional lipid droplet formation. These genetic effects on the
   actin cytoskeleton dynamics and lipid accumulation in AMSCs are
   coherent with the effects we observed after the COBLL1 knockdown
   experiments ([217]Fig. 3b-[218]f,[219]h,[220]i), indicating that
   altered COBLL1 expression in the risk haplotype underlies the observed
   phenotypic effects in adipocytes. Summarized, these data show that the
   rs6712203 MONW risk locus, by altering COBLL1 mRNA expression levels,
   impacts actin remodelling in differentiating adipocytes, thereby
   strongly affecting fat mass-relevant and T2D-relevant cellular
   programmes including adipocyte differentiation, lipid droplet
   formation, stimulated lipolysis rate and insulin-stimulated glucose
   uptake.

Fig. 4 ∣. The rs6712203 MONW risk haplotype affects actin remodelling in
adipocytes and adipocyte lipid storage capacity.

   Fig. 4 ∣
   [221]Open in a new tab

   a, Schematic of adipocyte differentiation and LipocyteProfiling of
   subcutaneous AMSCs derived from TT (n = 7) and CC (n = 6) allele
   carriers of rs6712203 using LipocyteProfiler^[222]43. b,d, Differences
   in morphological profiles between TT (n = 7) and CC (n = 6) allele
   carriers at day 14 in subcutaneous (b) and visceral (d) AMSCs
   (multi-way ANOVA, significance level FDR < 5%). c, Pie chart
   illustrating non-redundant differential features per channel and class
   of measurement at day 14 of subcutaneous adipocyte differentiation in
   rs6712003 homozygous risk carriers compared to non-risk carriers. e,f,
   Spatial intensity distribution of AGP in the centre of the cytoplasm
   near the nucleus in subcutaneous adipocytes derived from TT (n = 7), TC
   (n = 14) and CC (n = 6) carriers of rs6712203
   (Cytoplasm_RadialDistribution_ FracAtD_AGP_1of4) (e) andjuxtaposed to
   the plasma membrane (Cytoplasm_ RadialDistribution_RadialCV_AGP_4of4)
   (f) throughout differentiation. Multi-way ANOVA; data represent the
   median ± 95% CI. g,h, Lipid droplet count
   (Cells_Children_LargeLipidObjects_Count) (g) and intensity of BODIPY
   stain (Cells_Intensity_IntegratedIntensity_Lipid) (h) throughout
   differentiation. Multi-way ANOVA; data represent the median ± 95% CI.

Cobll1-deficient mice are metabolically obese normal weight

   We generated a CRISPR-engineered Cobll1 knockout (Cobll1^−/−) mouse
   model to determine a potential role for Cobll1 in the regulation of
   metabolic function in vivo ([223]Extended Data Fig. 7a,[224]b). First,
   we sought to assess the effect of Cobll1 knockout on morphological and
   cellular profiles in differentiating murine SAT AMSCs by
   LipocyteProfiler (days 0, 2 and 10 of differentiation; [225]Fig. 5a).
   We found that mostly lipid features significantly (FDR < 5%) differed
   between knockout and control at day 10 of differentiation ([226]Fig.
   5b,[227]c and [228]Extended Data Fig. 7c-[229]e). More specifically,
   the data revealed that the AMSCs of Cobll1 knockout mice showed fewer
   and smaller lipid droplets (Lipid object_count P = 0.0017,
   Lipid_Granularity size measure 3 P = 0.0003, [230]Fig. 5d,[231]e),
   lower lipid intensity (P = 0.0073; [232]Fig. 5f) and, in line with the
   lipid-related observation, decreased actin cytoskeleton-related
   heterogeneity across the cytoplasm (Cytoplasm_Texture_Entropy_AGP, P =
   0.0200; [233]Fig. 5g). These findings indicate that Cobll1 knockout in
   mice affects actin cytoskeleton remodelling and lipid accumulation
   during in vitro adipocyte differentiation, mimicking our observations
   in human adipocytes. Indeed, when examining the effect of Cobll1
   knockout adipocytes on lipid accumulation using Oil Red O, we observed
   fewer differentiated adipocytes in Cobll1^−/− compared to wild-type
   (WT) cells ([234]Fig. 5h). We also observed significantly lower GPDH
   activity, an indicator of adipocyte differentiation, in Cobll1^−/− mice
   compared to their WT littermates (P = 0.004) ([235]Fig. 5i), suggesting
   that the ablation of Cobll1^−/− leads to impaired adipogenesis,
   supporting our finding in human adipocytes.

Fig. 5 ∣. Cobll1-deficient mice are leaner and display metabolically
dysfunctional phenotypes.

   Fig. 5 ∣
   [236]Open in a new tab

   a, Schematic of differentiation and LipocyteProfiling at three time
   points (days 0, 2 and 10) of AMSCs derived from Cobll1^−/− mice (n = 3)
   and WT mice (n = 4). b, Morphological profiles of the AMSCs of
   Cobll1^−/− mice compared to the AMSCs of WT mice at day 10; two-sided
   t-test, significance level FDR ≤ 5%. c, Pie chart illustrating
   non-redundant differential features per channel and class of
   measurement comparing the AMSCs of Cobll1^−/− and WT mice at day 10 of
   differentiation. d–g, Lipid droplet count
   (Cells_Children_LargeLipidObjects_Count) (d), granularity of BODIPY
   stain (Cells_Granularity_3_BODIPY) (e), intensity of BODIPY stain
   (Cells_Intensity_IntegratedIntensity_Lipid) (f) and texture of actin
   cytoskeleton (Cytoplasm_Texture_Entropy_AGP) (g) at day 10 of
   differentiation. Two-sided t-test; data represent the median ± 95% CI.
   h, Oil Red O staining of differentiated murine AMSCs. i, GPDH activity
   of differentiated murine AMSCs was assessed by measuring the decrease
   in NADH at 340 nm. Data represent the mean ± s.e.m. *P < 0.05 compared
   to the WT group. j, Representative photograph of 14-week-old WT and
   Cobll1^−/− mice fed a normal chow. The yellow dotted lines delineate
   perigonadal WAT. k, Mouse body weight across time. Data are expressed
   as the mean ± s.e.m. *P < 0.05, **P < 0.01 and ***P < 0.001 compared to
   the WT group. l, Body composition (fat mass/body weight). Data are
   expressed as the mean ± s.e.m. *P < 0.05 compared to the WT group. m,
   Body length measurements of WT and Cobll1^−/− mice (n = 6). Data are
   expressed as the mean ± s.e.m. *P < 0.05 compared to the WT group. NS,
   not significant. n, BMD analysed by dual energy X-ray absorptiometry
   (DEXA). Data are expressed as the mean ± s.e.m. *P < 0.05 compared to
   the WT group. o, IPGTT in WT and Cobll1^−/− and Cobll1^+/− mice. The
   inset graph shows the area under the curve (AUC) of the blood glucose
   concentration levels measured during IPGTT. Data represent the mean ±
   s.e.m. **P < 0.01 compared to the WT group. In i,k–o statistical
   significance was determined by Student’s t-test. In l–o the experiment
   was repeated independently three times with similar results.

   To assess the impact of the 2q24.3 MONW locus effector COBLL1 on
   organismal processes, we assayed for growth and body composition
   phenotypes in Cobll1^−/− mice. At 10 weeks of age, Cobll1^−/−
   homozygous animals displayed 20–25% less weight gain compared to the WT
   control and Cobll1 heterozygous (Cobll1^+/−) littermates ([237]Fig.
   5j,[238]k), reflecting a significant reduction in total fat mass
   percentage (3–5%), but with no difference in body length or in bone
   mineral density (BMD), suggesting that the phenotype of Cobll1^−/− is
   due to reduced fat mass ([239]Fig. 5l-[240]n). Next, we examined
   glucose homeostasis by performing intraperitoneal glucose tolerance
   tests (IPGTTs). Cobll1^−/− mice displayed impaired glucose tolerance
   compared to WT and heterozygous littermates ([241]Fig. 5o). In
   conclusion, the phenotypic characteristics of the Cobll1 knockout mouse
   model recapitulate the MONW association patterns observed in humans and
   demonstrate how abrogation of Cobll1 links molecular and cellular
   phenotypes to organismal-level metabolic phenotypes associated with
   genetic variation in the 2q24.3 locus in humans.

Discussion

   The 2q24.3 locus is pleiotropic in nature and, intriguingly, is
   associated with increased risk of T2D and simultaneously with decreased
   body fat percentage, reminiscent of a MONW phenotype association
   signature. In this study, we applied a series of experimental and
   computational approaches to systematically dissect the 2q24.3 metabolic
   risk locus and link it to a causal variant (rs6712203), its effector
   gene (COBLL1), its causal cell type, cell context (developmental time
   point, adipose depot) and cellular mechanisms (actin remodelling).
   These altered cellular functions, that is, adipocyte differentiation
   into metabolically active subcutaneous adipocytes, lipid metabolism and
   insulin-responsive glucose uptake, are relevant for T2D, and body fat
   percentage and distribution. When ablating Cobll1 in mice, we showed a
   lipodystrophy-like phenotype, recapitulating the pleiotropic
   association with T2D and decreased body fat mass in humans. Thus, we
   provide genetic and mechanistic evidence that a common genetic variant
   limits peripheral energy storage capacity and simultaneously impairs
   insulin responsiveness.

   The results of this study lend support to the common hypothesis that
   the individual risk of T2D and fasting insulin is modified by changes
   to the mass, distribution and function of adipose
   tissue^[242]23,[243]47, and that a metabolically healthy state is
   largely dependent on SAT expandability. Inherited and acquired
   lipodystrophies, as characterized by the selective or global
   perturbation of adipose tissue function, mass and distribution, result
   in severe forms of insulin resistance and diabetes; shared molecular
   mechanisms between rare familial partial lipodystrophy type 1 and
   common forms of insulin resistance at the genetic level have been
   suggested previously^[244]23. Several common metabolic risk loci are
   characterized by a MONW and MHO association; distinct association
   signatures suggest that multiple mechanisms are involved, most of which
   are to be identified^[245]21,[246]22,[247]48. Previous work has
   convincingly implicated variants at the FAM13A locus to affect
   metabolic disease risk by affecting subcutaneous adipocyte
   differentiation^[248]48. In this work, we implicate for the first time
   actin cytoskeleton remodelling as a critical factor for subcutaneous
   adipocyte function and as causally involved in metabolic disease
   progression in humans, stressing the notion that MONW and MHO
   predisposing loci control distinct cellular programmes.

   We observed evidence of sex-dimorphic effects when conditioning MONW
   traits on rs6712203, which is in line with a reported sexual dimorphism
   for WHR consistently conveying stronger effects in
   women^[249]15,[250]26,[251]49,[252]50 and a sex-independent effect on
   T2D ([253]refs. 11-[254]14), and with a sex-dimorphic effect on gene
   expression for COBLL1 but not for GRB14 ([255]ref. 19). While we did
   not observe a phenotypic effect of Cobll1 knockout in female mice, we
   cannot exclude the possibility that this is due to well-known sexual
   dimorphism in mouse metabolic phenotypes^[256]51.

   The COBLL1 protein has been introduced as a biomarker of high
   prognostic value for different types of cancer, a modulator of cell
   morphology in prostate cancer^[257]42, and lipid metabolism and insulin
   signalling in adipocytes^[258]52. In this study, we established chain
   of causation linking the 2q24.3 locus to its functional variant, its
   adipocyte cell type-specific and context-specific effects, its
   regulatory element, its effector gene COBLL1 and finally its causal
   cellular function, that is, actin remodelling in differentiating
   adipocytes, which is under the genetic control of both locus and target
   gene. These findings are in line with recent reports linking actin
   dynamics, regulated by the F-actin:G-actin ratio, and
   insulin-stimulated trafficking and fusion of GLUT4
   vesicles^[259]53-[260]55. Actin remodelling also occurs in
   hypertrophically differentiated adipocytes^[261]55,[262]56, an
   essential adaptive mechanism of post-mitotic cells, such as mature
   adipocytes, and was recently linked to cellular senescence in states of
   obesity and insulin resistance^[263]57. An unresolved question to be
   addressed in future work is whether COBLL1-mediated actin dynamics
   contributes to metabolic functions of certain adipocyte subpopulations
   with different degrees of insulin sensitivity as recently identified by
   single-cell and spatial transcriptomics^[264]58,[265]59. Furthermore,
   while we did not observe a haplotype-dependent effect on actin
   cytoskeleton remodelling in visceral adipocytes, further studies are
   warranted to elucidate how actin depolymerization and repolymerization
   during the differentiation process may differ in subcutaneous compared
   to visceral adipocytes.

   Besides COBLL1, the insulin receptor adaptor protein GRB14 is an
   intuitive effector target gene at the 2q24.3 locus^[266]60. GRB14 is a
   negative regulator of insulin signalling^[267]61,[268]62. In line with
   our findings, physiological studies in GRB14 knockout mice show
   increased glucose tolerance and insulin sensitivity linked to higher
   IRS1 and Akt phosphorylation in liver and skeletal muscle but not
   adipose tissue^[269]63, which may rely more on regulation of proximal
   insulin signalling by GRB10 ([270]refs. 63-[271]65). Somewhat
   contradictory to these findings and to the results reported in this
   present work, a recent study reported that CRISPR–Cas9-mediated
   ablation of GRB14 in human SGBS preadipocytes resulted in decreased
   preadipocyte proliferation, adipocyte differentiation and adipocyte
   insulin responsiveness^[272]52. This contradiction might be explained
   by the fact that different perturbation schemes were applied, that is,
   small hairpin RNA (shRNA)-mediated silencing of GRB14 as described in
   this study compared to CRISPR–Cas9 knockout of GRB14, which may be
   confounded by the number of cells in a population that do not produce
   knockout phenotypes^[273]66 (González et al.^[274]67); also partial
   loss-of-function or gain-of-function phenotypes can be generated by
   Cas9-induced in-frame insertion and deletions, and hypomorphic
   alleles^[275]67, which can obscure the readout. COBLL1 as the effector
   gene that underlies the T2D association is further corroborated by
   recent exome chip and exome sequencing studies linking an rs7607980
   coding variant in the COBLL1 gene to T2D and glycaemic traits (minor
   allele frequency (MAF) = 0.12, P = 4.7 × 10^−11) (refs.
   [276]68-[277]70). Furthermore, recent rare variant aggregation analyses
   at COBLL1 revealed nominal association with WHR^[278]71 but not with
   GRB14, which agrees with our findings that COBLL1 drives at least part
   of the 2q24.3 genetic risk. Our sequence-based predictive models scored
   rs6712203 highest across all 2q24.3 haplotype variants; our
   experimental data provide evidence that the SNP rs6712203 acts as a
   causal regulatory variant which, at least in part, drives effects at
   the 2q24.3 MONW locus tagged by rs3923113. Notably, our sequence-based
   models predict additional variants at the locus to affect regulatory
   activity; at least two additional variants, rs12692737 and rs10179126,
   are associated with allelic chromatin accessibility bias. Therefore,
   while beyond the scope of our study, we note that our work does not
   preclude multiple variants acting in concert at this locus in a cell
   state-dependent manner, potentially implicating GRB14 along with COBLL1
   as effector genes. Furthermore, besides rs3923113, recent large-scale
   genetic association studies identified multiple additional signals
   tagged by other lead SNPs in reasonably high LD that associate with
   metabolic disease^[279]13,[280]25; also, the effect of indels should be
   evaluated in future studies.

   In conclusion, the 2q24.3 locus is a prime example of a common genetic
   locus that predisposes to limited peripheral adipose storage capacity
   and insulin resistance, driven by an impairment of dynamic actin
   cytoskeleton remodelling of the differentiating subcutaneous adipocyte.
   Our study epitomizes the potential of translating pleiotropic genetic
   loci into mechanistic understanding towards therapeutic hypotheses.

Methods

Human primary AMSCs isolation and needle biopsies

   We obtained AMSCs from the adipose tissue of patients undergoing a
   range of abdominal laparoscopic surgeries (sleeve gastrectomy,
   fundoplication or appendectomy). VAT was derived from the proximity of
   the angle of His and SAT was obtained from beneath the skin at the site
   of surgical incision. Human liposuction material was obtained from a
   private plastic surgery clinic. Isolation of AMSCs was performed as
   described previously^[281]72,[282]73. Abdominal specimens from SAT were
   obtained by needle aspiration before and 4 weeks after a
   very-low-calorie diet (800 kcal). Study design and the abdominal needle
   aspiration procedure were previously described in Ott et al.^[283]74.

   Each participant gave written informed consent before inclusion and the
   study protocol was approved by the ethics committee of the Technical
   University of Munich (147 study no. 5716/13).

Differentiation of human AMSCs

   For imaging, cells were seeded at 10,000 cells per well in 96-well
   plates (high-content imaging; CellCarrier plates, catalogue no.
   6005550; PerkinElmer) or seeded at 18,000 cells per well in collagen
   IV-coated 8-well μ-slides (higher-resolution imaging; catalogue no.
   80822, ibidi) and induced 4 d after seeding. For RNA sequencing
   (RNA-seq), cells were seeded at 40,000 cells per well in 12-well dishes
   (Corning). Before induction, cells were cultured in proliferation
   medium (DMEM/F-12, 1% penicillin-streptomycin, 33 μM biotin, 17 μM
   pantothenate supplemented with 0.13 μM insulin, 0.01 μg ml^−1 epidermal
   growth factor, 0.001 μg ml^−1 fibroblast growth factor, 2.5% FCS).
   Subcutaneous and visceral adipogenic differentiation was induced by
   changing culture medium to induction medium (basic medium supplemented
   with 0.861 μM insulin, 1 nM T3, 0.1 μM cortisol, 0.01 mg ml^−1
   transferrin, 1 μM rosiglitazone, 25 nM dexamethasone and 2.5 nM IBMX;
   for visceral AMSCs, 0.1 mM oleic and linoleic acid and 2% FCS were also
   used). On day 3 of adipogenic differentiation, culture medium was
   changed to differentiation medium (basic medium supplemented with 0.861
   μM insulin, 1 nM T3, 0.1 μM cortisol, 0.01 mg ml^−1 transferrin; for
   visceral AMSCs, 0.1 mM oleic and linoleic acid and 2% FCS were also
   used.

Genotyping

   Genotyping was performed using the Illumina Global Screening bead-chip
   array. DNA was extracted using QIAGEN’s DNeasy Blood and Tissue Kit
   (catalogue no. 69504) and sent to the Oxford Genotyping Centre for
   genotyping on the Infinium HTS assay on Global Screening Array
   bead-chips (Illumina). Genotype quality control (QC) was done using
   GenomeStudio and genotypes were converted into PLINK format. We checked
   sample missingness but found no sample with missingness greater than
   5%. For the remaining sample QC steps, we reduced the genotyping data
   down to a set of high-quality SNPs. These SNPs were: (1) common (MAF >
   0%); (2) missingness < 0.1%; (3) independent, pruned at an LD (R^2)
   threshold of 0.2; (4) autosomal only; (5) outside the lactase locus
   (chromosome 2), the major histocompatibility complex (chromosome 6),
   and outside the inversions on chromosomes 8 and 17; and (6) in
   Hardy–Weinberg equilibrium (P > 1 × 10^−3). Using the remaining
   approximately 65,000 SNPs, we checked samples for inbreeding (--het in
   PLINK), but found no samples with excess homozygosity or heterozygosity
   (no sample more than 6 s.d. from the mean). We also checked for
   relatedness (--genome in PLINK) and found one pair of samples to be
   identical; we kept the sample with the higher overall genotyping rate.
   Finally, we performed principal component analysis using EIGENSTRAT and
   projected the samples onto data from HapMap 3, which includes samples
   from 11 global populations. Six samples had some non-European ancestral
   background, while most samples were of European descent. We did not
   remove any samples at this step, selecting to adjust for principal
   components (PCs) in genome-wide testing. Adjustment for PCs failed to
   eliminate population stratification; therefore, we restricted the
   process to samples of European descent only, defined as samples falling
   within ±10 s.d. of the first and second PCs of the CEU (Utah residents
   with Northern and Western European ancestry) and TSI (Tuscans in Italy)
   samples included in the HapMap 3 dataset. Finally, sex information was
   received after the initial sample QC was complete. One sample with
   potentially mismatching sex information (comparing genotype and
   phenotype information) was discovered after analyses were complete and
   therefore remained in the analysis.

SNP QC.

   We removed monomorphic SNPs and all SNPs with missingness greater than
   5% and out of Hardy–Weinberg equilibrium (P < 1 × 10^−6). We set
   heterozygous haploid sites to missing to enable downstream imputation.
   The final cleaned dataset included 190 samples and approximately
   700,000 SNPs.

Genotype imputation.

   We performed imputation via the Michigan Imputation Server as described
   in Glastonbury et al.^[284]75. Briefly, we aligned SNPs to the positive
   strand and then uploaded the data (in VCF format) to the server. We
   imputed the data using the Haplotype Reference Consortium panel. We
   selected EAGLE as the phasing tool to phase the data. To impute
   chromosome X, we followed the server protocol for imputing this
   chromosome (including using SHAPEIT to perform the phasing step).

ATAC–seq in immortalized AMSCs

   ATAC–seq was performed by adapting the protocol from Buenrostro et
   al.^[285]76 by adding a nucleus preparation step. Differentiating cells
   were lysed directly in the culture plate before adipogenesis was
   induced (days 0, 3, 6 and 14). Ice-cold lysis buffer was added directly
   onto cells grown in a 12-well plate. Plates were incubated on ice for
   10 min until cells were permeabilized and nuclei released. Cells in
   lysis buffer were gently scraped off the well and transferred into a
   chilled 1.5-ml tube to create crude nuclei. Nuclei were spun down at
   600g for 10 min at 4 °C. Nuclei pellets were then resuspended in 40 μl
   Tagmentation DNA Buffer (catalogue no. FC-121-1031, Nextera) and the
   quality of the nuclei was assessed using trypan blue. The volume of
   50,000 nuclei was determined using a haemocytometer. The transposition
   reaction was performed as described previously^[286]76. All tagmented
   DNA was PCR-amplified for eight cycles using the following PCR
   conditions: 72 °C for 5 min, 98 °C for 30 s, followed by thermocycling
   at 98 °C for 10 s, 63 °C for 30 s and 72 °C for 1 min. The quality of
   the ATAC–seq libraries was assessed using a Bioanalyzer High
   Sensitivity Chip (Applied Biosystems). All libraries had a mean
   fragment size of approximately 200 bp and characteristic nucleosome
   patterning, indicating good quality. Libraries were pooled and
   sequenced on a HiSeq 4000 system (Illumina), generating 50 million
   75-bp paired-end reads per sample. To reduce bias due to PCR
   amplification of libraries, duplicate reads were removed. Sequencing
   reads were aligned to the hs37d5 reference genome; the Burrows–Wheeler
   Aligner-MEM was used for mapping. All experiments were performed in
   technical duplicates.

LipocyteProfiling in human and mouse AMSCs

   The full LipocyteProfiler protocol is described in Laber et
   al.^[287]43. Briefly, human and mouse AMSCs were plated and
   differentiated in 96-well CellCarrier plates for high-content imaging
   at days 0, 3, 8 and 14 of differentiation. On the respective day of the
   assay, cell culture medium was removed and replaced with 0.5 μM
   MitoTracker staining solution (1 mM MitoTracker Deep Red stock
   (catalogue no. [288]M22426, Invitrogen) diluted in culture medium) to
   each well followed by 30-min incubation at 37 °C protected from light.
   After 30 min, the MitoTracker staining solution was removed and cells
   were washed twice with Dulbecco’s PBS (1×) (catalogue no. 21-030-CV,
   Corning) and 2.9 μM BODIPY staining solution (3.8 mM BODIPY 505/515
   stock (catalogue no. D3921, Thermo Fisher Scientific) diluted in
   Dulbecco’s PBS) was added followed by 15-min incubation at 37 °C
   protected from light. Subsequently, cells were fixed by adding 16%
   methanol-free paraformaldehyde (PFA) (catalogue no. 15710-S, Electron
   Microscopy Sciences) directly to the BODIPY staining solution to a
   final concentration of 3.2% and incubated for 20 min at room
   temperature protected from light. PFA was removed and cells were washed
   once with Hank’s Balanced Salt Solution (HBSS) (1×) (catalogue no.
   14025076, Gibco). To permeabilize cells, 0.1% Triton X-100 (catalogue
   no. X100, Sigma-Aldrich) was added and incubated at room temperature
   for 10 min protected from light. After permeabilization, multi-stain
   solution (10 U Alexa Fluor 568 phalloidin (catalogue no. A12380, Thermo
   Fisher Scientific), 0.01 mg ml^−1 Hoechst 33342 (catalogue no. H3570,
   Invitrogen), 0.0015 mg ml^−1 WGA, Alexa Fluor 555 conjugate (catalogue
   no. [289]W32464, Thermo Fisher Scientific), and 3 μM SYTO 14 Green
   Fluorescent Nucleic Acid Stain (catalogue no. S7576, Invitrogen)
   diluted in HBSS) was added and cells were incubated at room temperature
   for 10 min protected from light. The staining solution was removed and
   cells were washed three times with HBSS. Cells (25 fields per well)
   were imaged with an Opera Phenix High-Content Screening System.

Staining and microscopy

   To stain the actin cytoskeleton, COBLL1 and nuclei, cells were washed
   twice with ice-cold PBS and fixed with PFA Roti-Histofix 4% (Roth) for
   15 min. Cells were washed twice with ice-cold PBS for 5 min and
   incubated with ice-cold 0.1% Triton X-100/PBS (Roth). Cells were washed
   again twice with PBS and incubated for 1 h at room temperature with 4%
   BSA, then incubated with 1:100 primary COBLL1 antibody (catalogue no.
   HPA053344, Atlas Antibodies, lot no. 000010746) overnight at 4 °C,
   followed by 1 h at room temperature (final concentration = 2 μg ml^−1).
   Cells were washed twice with PBS and stained with 0.46% bisBenzimide H
   33258 (Sigma-Aldrich), 1% phalloidin–Atto 565 (Sigma-Aldrich) and a
   secondary antibody against COBLL1 (1:200 Alexa Fluor 488, Abcam) at a
   final concentration of 0.01 mg ml^−1). Cells were incubated for 1 h at
   room temperature in the dark. Afterwards, cells were washed twice with
   PBS for 5 min and kept in PBS at 4 °C until imaging. Images were
   acquired on a Leica DMi8 microscope (Leica Microsystems) using the HC
   PL APO 63×/1.40 oil objective. Images were processed with the Leica
   LASX software.

LipocyteProfiler

   The full LipocyteProfiler protocol is described in Laber et
   al.^[290]43. Quantitation was performed using CellProfiler v.3.1.9.
   Before processing, flat-field illumination correction was performed
   using functions generated from the mean intensity across each plate.
   Nuclei were identified using with 4,6-diamidino-2-phenylindole stain
   and then expanded to identify whole cells using the AGP and BODIPY
   stains. Regions of the cytoplasm were determined by removing the nuclei
   from the cell segmentations. Speckles of BODIPY staining were enhanced
   to assist in the detection of small and large individual BODIPY
   objects. For each object set, measurements were collected representing
   size, shape, intensity, granularity, texture, colocalization and
   distance to neighbouring objects. After the feature extraction, data
   were filtered by applying automated and manual QC steps. First, fields
   with a total cell count less than 50 cells were removed. Second, fields
   that were corrupted by experimentally induced technical artefacts were
   removed by applying a manually defined QC mask. Furthermore,
   blocklisted features known to be noisy and generally unreliable were
   removed. After filtering, data were normalized per plate using a robust
   scaling approach that subtracts the median from each variable and
   divides it by the interquartile range. For each individual, wells were
   aggregated for downstream analysis by cell depot and day of
   differentiation. Subsequent data analyses were performed in R v.3.6.1
   using base packages unless noted. For dimensionality reduction
   visualization, uniform manifold approximation and projection (UMAP)
   maps were created using the UMAP R package
   ([291]https://github.com/lmcinnes/umap) with default settings.

   To test for a difference in morphological profiles at any day of
   differentiation due to COBLL1 knockdown, individuals were analysed
   separately using a t-test. To test for a difference in morphological
   profiles at any day of differentiation between risk and non-risk
   haplotypes, a multi-way ANOVA was performed. Differences in
   morphological profiles between TT (n = 7) and CC (n = 6) allele
   carriers were adjusted for sex, age, BMI and batch. To overcome
   multiple-testing burden, P values were corrected using the FDR
   described in the R package qvalue
   ([292]https://github.com/StoreyLab/qvalue). Features with an FDR < 5%
   were classified as significant and filtered based on redundancy and
   effect size.

COBLL1 silencing using siRNA

   All silencing experiments were performed on four technical replicates.
   One day before silencing, AMSCs were plated into 96-well plates with
   10,000 cells per well or collagen IV-coated 8-well glass μ-slides with
   18,000 cells per well using growth medium. RNA-based silencing of
   COBLL1 was performed using the RNAiMAX Reagent (catalogue no. 13778075,
   Thermo Fisher Scientific) according to the manufacturer’s protocol.
   Briefly, Lipofectamine RNAiMAX Transfection Reagent was diluted in
   Opti-MEM medium (catalogue no. 11058021, Gibco). At the same time,
   siRNA was diluted in Opti-MEM medium. Then, diluted siRNA was added to
   the diluted Lipofectamine RNAiMAX reagent at a 1:1 ratio and incubated
   for 5 min. Coated 8-well glass μ-slides were incubated for 20 min at
   room temperature. The concentration of reagents per well in a 96-well
   plate were 0.5 μl (10 μM) of silencing oligonucleotides (catalogue no.
   4392420, Ambion) or negative control duplex (catalogue no. 4390846,
   Ambion) and 1.5 μl Lipofectamine RNAiMAX Reagent. The plate was gently
   swirled and placed in a 37 °C incubator at 5% CO[2] for 3 d. Cells were
   then induced to differentiate using a standard differentiation cocktail
   or collected for gene expression analysis to assess knockdown
   efficiency.

RNA preparation and quantitative PCR

   Total RNA was extracted with TRIzol (catalogue no. 15596026, Ambion)
   and the Direct-zol RNA MiniPrep Kit (catalogue no. R2052, Zymo
   Research) according to the manufacturer’s instructions. Complementary
   DNA (cDNA) was synthesized with the High-Capacity cDNA Reverse
   Transcription Kit (catalogue no. 4368814, Applied Biosystems) according
   to the manufacturer’s instructions. Quantitative PCR (qPCR) was
   performed using the PCR Master Mix (catalogue no. K0172, Thermo Fisher
   Scientific) and TaqMan probes for the target gene COBLL1 (catalogue no.
   4448892, ID: Hs01117513_m1, Thermo Fisher Scientific) and the
   housekeeping gene CANX (catalogue no. 4448892, ID Hs01558409_m1, Thermo
   Fisher Scientific). Relative gene expression was calculated by the
   2–^ΔΔCt method. Target gene expression was normalized to the expression
   of CANX.

RNA-seq and splicing analysis

   RNA-seq reads were trimmed using SeqPurge with the following command:
   SeqPurge -a1 CTGTCTCTTATACACATCTCCGAGCCCACGAGAC-a2
   CTGTCTCTTATACACATCTGACGCTGCCGACGA.

   For the transcript-level quantification, trimmed reads were analysed
   with Kallisto (with 25 bootstraps) and the transcripts per million
   (TPM) estimates were log-transformed; the top 10 PCs were computed.
   Next, reads were summed across all transcripts of a given gene to
   obtain gene-level estimates of the expression in each sample.

   For the splicing analysis with Leafcutter, reads were mapped with STAR
   using the following parameters: STAR--twopassMode
   Basic--outSAMstrandField intronMotif--readFilesCommand zcat-outSAMtype
   BAM Unsorted.

   They were then processed using Samtools and RegTools to convert to a
   JUNC file with the following command: regtools junctions extract -s 1
   -a 8 -m 50 -M 500000.

   Finally, reads were clustered into splicing events with the following
   command from the Leafcutter project: leafcutter cluster regtools.py -j
   <files> -m 50 −1500000. These clusters were then converted to TPM and
   modelled as a function of the rs6712203 genotype.

RNA pathway enrichment analysis

   Transcript-level (log) RNA expression was compared between COBLL1 and
   all other quantified genes using linear regression. The effect of
   COBLL1 on other genes was compared and adjusted for the expression PCs,
   sample depot source, cell line and day of differentiation. This
   resulted in effect sizes of individual genes in terms of how similar
   they were to COBLL1; those with estimates that had a
   Bonferroni-adjusted P > 1 × 10^−13 and an absolute effect size less
   than 0.1 or greater than 10 were excluded. Similarly expressed genes
   with strong association with COBLL1 were uploaded to Enrichr and
   analysed as a gene list against the KEGG, and the WikiPathways and HCI
   pathways. The full set of tests is available at
   [293]https://maayanlab.cloud/Enrichr/enrich?dataset=1a9a07019bfd8bbddc6
   eb6c26641bfef; the sensitivity evaluation in which very-lowly-expressed
   and highly-expressed genes were not excluded are available at
   [294]https://maayanlab.cloud/Enrichr/enrich?dataset=231b12708d04818007d
   93364e489fab7.

PMCA variant conservation analysis

   The PMCA results were replicated from Claussnitzer et al.^[295]29.
   Briefly, transcription factor binding sites and their cooccurrence
   across species were tallied and classified into complex and non-complex
   regions. Complex regions were counted on the basis of motifs aligned
   across species and were then plotted against Basset scores to discover
   putative causal variants.

Basset variant effect prediction analysis

   Basset models were trained and evaluated as in Sobreira et al.^[296]77.
   Models were trained to capture chromatin regulation relevant to
   adipocyte differentiation and a number of other metabolic tissues;
   these effects were estimated by determining the difference in effect
   between alleles at each variant. The variants with the largest effect
   on accessibility were considered the most important and most likely to
   be causal.

Allele-specific accessibility analysis

   Allele-specific analyses were performed as in Sinnott-Armstrong et
   al.^[297]78. Briefly, reads were aligned from a heterozygous individual
   on the basis of the variant; the number of reads supporting each allele
   were tallied at each time point and across variants on the haplotype.

Conditional and BMI-dependent variant association analysis

   Variants (n = 6,167) within 100 kb of rs6712203 were included in the
   analysis. White British individuals in the UKB were analysed with
   phenotype type 2 diabetes (as described in Eastwood et al.^[298]79),
   log WHR adjusted for BMI, hip circumference and whole-body fat mass.
   Individuals were stratified on the basis of reported sex and filtered
   to White British unrelated individuals as described in
   Sinnott-Armstrong et al.^[299]80. Conditional analyses and all
   associations were performed using PLINK 2.0.

POU2F2 affinity modelling using the IGR method

   The IGR method was used for POU2F2 affinity modelling using the POU2F2
   ChIP–seq data^[300]35. To correct for systematic bias in the sequencing
   depth around particular k-mers, all scores were offset by a baseline
   value, defined as the average signal between the forward and reverse
   complement instances of the k-mer between −200 and −195 and between 195
   and 199 bases away from the k-mer centre. To include only large-effect
   binding differences, the prominence was defined as the maximum score
   across any point in the context of either the forward or reverse
   complement version of the k-mer for both alleles and the maximum
   difference as the maximum absolute difference in scores between the two
   alleles at any point in the window. The baseline ratio was defined as
   the ratio of the maximum difference to the prominence, which varied
   between 0 (if the two alleles were equal at all points) and 2 (if they
   were perfectly complementary at their highest absolute point).

   To map high-quality putative disrupted binding sites, the k-mer
   sequence that gave the highest affinity under the germline was recorded
   as reference and the k-mer sequence that gave the highest affinity
   under the somatic variant as alternate. The quality of a given k-mer
   was defined as the correlation between the average context plot forward
   and the reverse of the average context plot of the reverse complement;
   the symmetry of a given k-mer was the correlation between the average
   context plot forward and the average context plot reverse. Quality was
   high when the antiparallel binding was preserved, and symmetry was high
   when the peak signal was centred with respect to the variant. The
   results were included as passed when the Bonferroni-corrected P value
   for the comparison was less than 0.05, the baseline ratio was greater
   than 0.5, quality and symmetry were both greater than 0.85 for one of
   the alleles, and quality and symmetry were both greater than 0.5 for
   the other allele.

Microarray expression data

   A global gene expression measurement was performed, using Illumina
   HumanRef-8 v.3 BeadChip microarrays from whole abdominal SAT. Signal
   intensities were quantile-normalized before the correlation analysis.

SGBS genome editing

   To edit the rs6712203 heterozygous allele in SGBS preadipocytes to the
   homozygous risk (CC) and non-risk (TT) alleles, we applied the
   CRISPR–Cas9 homology-directed repair genome editing approach. The hCas9
   vector was purchased from Addgene (plasmid no. 41815). The guide
   sequence was selected using the design tool (Zhang Lab, MIT) with a
   predicted number of 228 potential off-target sites, located 211 bp
   upstream of rs6712203. It was cloned in front of the U6 promoter into
   the BbsI cloning site of the sgRNA expression vector (R. Kühn,
   Helmholtz Zentrum München), using the double-stranded oligonucleotides
   5′-CACCGACTCTCCACTACCATTGCCA-3′ and 5′-AAACTGGCAATGGTAGTGGAGAGTC-3′. To
   amplify the 2,009-bp homology region with the risk or non-risk allele
   of rs6712203 at mid-position, the genomic DNA (gDNA) of SGBS cells was
   amplified with the primers 5′-GGTGGTCCCATTAAAAAGAAAGAAGCTTGG-3′ and
   5′-CTTCTCTTTTACCCTGCTGGCTACTGGTTG-3′ using High-Fidelity Q5 DNA
   polymerase (New England Biolabs). The gel-purified PCR product was
   cloned into the blunt end pjet1.2 vector using the CloneJET PCR Cloning
   Kit (Fermentas). A clone with the rs6712203 C allele was selected and
   the corresponding T allele vector was generated using the Q5
   Site-Directed Mutagenesis Kit (New England Biolabs) with the primers
   5′-TCATTCATCATATGCAATTCTGG-3′ and 5′-GGCAAATTAATATTTAGGATTATATC-3′. To
   avoid Cas9 reactivity after genome editing, the NGG guide target
   sequence was mutated to NCG in both homology vectors with the primers
   5′-CCATTGCCAACGGCTGAGTCAG-3′ and 5′-TAGTGGAGAGTTCTCACAAAAC-3′. SGBS
   cells were cotransfected with green fluorescent protein (Lonza), hCas9,
   the respective sgRNA and pMACS v.4.1 (Miltenyi Biotec) plasmids using
   the Amaxa Nucleofector device (programme U-033) (Lonza). Cells were
   sorted using the MACSelect Transfected Cell Selection Kit (Miltenyi
   Biotec). The integrity of each edited vector construct and the SGBS
   cell nucleotide exchange was confirmed by DNA sequencing (Eurofins).

Lentiviral SGBS cell transduction

   The MISSON Lentiviral Packaging Mix (Sigma-Aldrich) was used according
   to the manufacturer’s instructions. Packaging HEK 293T cells were grown
   in a low-antibiotic growth medium (DMEM, 10% FCS, 0.1%
   penicillin-streptomycin). When cells were approximately 70% confluent,
   they were cotransfected using X-treme GENE HP (Roche), with the
   packaging plasmid pCMVdeltaR8.91, the envelope plasmid pMD2.G and the
   pLKO-based plasmid containing shRNA against the human target gene
   COBLL1 ([301]NM_014900.2-3071s1c1), COBLL1 ([302]NM_014900.2-4440s1c1),
   GRB14 ([303]NM_004490.1-1243s1c1) or empty vector MISSION TRC2
   pLKO.5-puro plasmid (Sigma-Aldrich). Cells were incubated for 24 h, and
   the medium was discarded and replaced with a serum-rich medium (30%
   FCS). The supernatant containing the viable virus particles was
   collected 48 and 72 h after transfection, centrifuged to remove
   cellular debris and stored at −80 °C.

   SGBS cells were seeded at a concentration of 2.6 × 10^4 cells per
   6-well plate and grown in normal growth medium. After 24 h the medium
   was replaced and supplemented with 8 μg ml^−1 Polybrene (Sigma-Aldrich)
   and virus supernatant with a multiplicity of infection of 2. On
   consecutive days, cells were washed with PBS and medium was replaced to
   remove the virus. The medium was supplemented with 0.5 μg ml^−1
   puromycin 96 h after infection to select stable clones. When cells were
   grown confluent, puromycin was removed from the medium and cells were
   differentiated until day 16. Target gene silencing was confirmed by
   qPCR with reverse transcription (RT–qPCR).

Generation of dCas-hWAT preadipocyte cell line

   We generated an hWAT cell line, an established white adipocyte
   line^[304]33, stably expressing dCas9-KRAB. Lentivirus was delivered to
   preadipocytes using spinfection. For single-copy integration, the virus
   titre was optimized to reach an integration efficiency of 30-40%. Four
   days after infection, we positively selected for cells expressing the
   integrated transgene using blasticidin. We validated the generated line
   by assessing dCas9-KRAB activity delivering a sgRNA targeting CD81,
   stably expressed in adipocytes, and measuring CD81 gene expression.

CRISPRi

   sgRNAs targeting TSS were designed using CRISPick
   ([305]https://portals.broadinstitute.org/gppx/crispick/public) and
   sgRNAs targeting non-coding regions were designed with assistance from
   the Genetic Perturbation Platform at the Broad Institute. Guide
   sequences are as follows: non-targeting control 1:
   AAAAAGCTTCCGCCTGATGG; non-targeting control 2: CGTCCCTTCGTCTCTGCTTA;
   non-targeting control 3: TCACCTCCGAACGAACACCT; non-targeting control 4:
   GGACGC ACCATTCCGGGTGA; COBLL1_1: AGCTCGCCTGTTCTCCCTCG; COB LL1_2:
   GAGTAGGAGAGGAAGCCGCG; COBLL1_3: AGAGCCGAGCGGC AAGAGCG; COBLL1_4:
   GAGCCGAGCGGCAAGAGCGC; GRB14_1: AC CACACCTGCAGAGCGCTC; GRB14_2:
   GCCCGAGCGCTCTGCAGGTG; GRB14_3: CGAGCGCTCTGCAGGTGTGG; GRB14_4:
   CACCACACCTGC AGAGCGCT; RS4: AGAGTTCTCACAAAACTCCA; RS9: CTCTCCACTA
   CCATTGCCAA; RS20: CCATTGCCAAGGGCTGAGTC; RS1: CATCACA TGCAATTCTGGCA;
   RS8: AATACAAATAGAGCAAGTAT; RS11: TTGT AAGTGGAAAAAAAGCT. sgRNAs were
   cloned into the BsmBI site of a lentiviral pXPR_050 vector (plasmid no.
   96925, Addgene)^[306]81 and verified using Sanger sequencing.

   Lentiviral particles were generated by transfecting HEK 293T cells (8 ×
   10^5 per well of a 6-well plate seeded the day before transfection)
   with 1,250 ng sgRNA, 1,250 ng psPAX2 and 250 ng vesicular stomatitis
   virus G vectors using a TransIT-LT1 transfection reagent at 8.5 μl per
   well (Mirus Bio). Viral particles were collected 72 h after
   transfection, passed through a 0.45-μm filter, aliquoted and stored at
   −80 °C until use. For lentiviral transduction by spinfection, cells
   were seeded at 1 × 10^5 per well of 12-well plates in the presence of
   50, 250 or 500 μl viral supernatant and Polybrene (4 μg ml^−1) and spun
   at 930 g for 2 h. Cells were selected with 1 μg ml^−1 puromycin for 3
   d, split once and collected 6 d after transduction for RNA extraction
   and gene expression by qPCR. Cas9 expression and COBLL1 knockdown were
   verified by western blot using specific antibodies (Cas9: catalogue no.
   C15310258, Diagenode; COBLL1: catalogue no. HPA053344, Sigma-Aldrich).
   Ponceau-S staining or β-actin (catalogue no. 4970, Cell Signaling
   Technology).

Measurement of GPDH activity

   Cells were grown to confluence and differentiated until day 16 in
   6-well plates. Cells were collected in a GPDH buffer with 0.05 M
   Tris-HCl (pH 7.4), 1 mM EDTA and 1 mM mercaptoethanol before they were
   stored at −80 °C until further use. Samples were gently defrosted at 4
   °C and sonified for 7 s at 29%, and centrifuged for 10 min at 10,000g
   at 4 °C. GPDH activity was measured as described previously^[307]82.
   Briefly, GPDH activity was assessed, measuring the conversion of
   dihydroxyacetone phosphate (Sigma-Aldrich) in the presence of the
   coenzyme nicotinamide adenine dinucleotide (NADH) (Omnilab) at a
   wavelength of 340 nm, using the Infinite 200 (Tecan). Protein
   concentrations were assessed using the BCA-reducing agent compatible
   protein assay kit (Thermo Fisher Scientific) with BSA standard samples
   in GPDH buffer for quantification. The value for each condition was
   calculated using the ratio between GPDH activity and protein
   concentration.

Glucose uptake, lipolysis and western blot analysis

   For glucose, glycerol and western blot analysis, shRNA COBLL1, shRNA
   empty vector and shRNA GRB14 SGBS cells were differentiated until day
   16 in 6-well plates.

   The insulin-stimulated 2-DG uptake experiment was performed as
   described previously^[308]83. Briefly, cells were incubated in
   glucose-free DMEM and F-12 (1:1) containing 1% penicillin-streptomycin,
   16 μM biotin, 36 μM pantothenic acid, 14.3 mM NaHCO[3] and 0.5 mM
   sodium pyruvate(Sigma-Aldrich) for 12 h. The medium was replaced with
   118 mM NaCl, 1.2 mM KH[2]PO[4], 4.8 mM KCl, 1.2 mM MgSO[4], 2.5 mM
   CaCl[2], 10 mM HEPES, 2.5 mM sodium pyruvate (Sigma-Aldrich) and 0.5%
   BSA (Sigma-Aldrich), pH 7,35. After 1.5 h the same buffer was added
   fresh either without supplement or with 1 μM insulin for 30 min.
   Radioactive uptake was started by adding KRH ^3H-2-DG at an activity of
   1 μCi ml^−1 and 50 μM 2-DG. Cells were incubated for 30 min and then
   washed with PBS. Cells were scraped off after adding 200 μl IGEPAL and
   150 μM phloretin. Radioactivity was measured using liquid scintillation
   counting with an external standard. Protein concentrations to normalize
   2-DG uptake were measured.

   To measure glycerol release, cells were washed with PBS and incubated
   for 3 h in phenol red-free DMEM containing 2% free fatty acid-free BSA
   (Roth). The medium was changed and cells were incubated for 1 h without
   supplement for basal lipolysis or the addition of 10 μM isoproterenol
   (Sigma-Aldrich) and 0.5 mM IBMX for stimulated lipolysis. The
   supernatant was collected for spectrophotometric glycerol measurement
   in a Sirius tube luminometer (Berthold Technologies) using glycerol
   kinase (Sigma-Aldrich) and the ATP Kit SL (BioThema). Remaining cells
   were collected for protein quantification and western blot analysis in
   radioimmunoprecipitation assay buffer containing 50 mM Tris-HCl (pH 8),
   150 mM NaCl, 0.2% SDS, 1% NP-40, 0.5% deoxycholate, 1 mM
   phenylmethylsulfonyl fluoride, phosphatase and protease inhibitors.
   Western blot analysis was performed using a mouse anti-human GPDH IgG
   (Ambion) and the Lipolysis Activation Antibody Sampler Kit (catalogue
   no. 8334, Cell Signaling Technology) according to the manufacturer’s
   protocol. Secondary IRDye IgG (LI-COR) was used to generate the
   fluorescence, detected by the Odyssey scanner (LI-COR).

Relative gene expression RT–qPCR

   Primer pairs were designed using published nucleotide sequences from
   the human genome (GenBank NCBI/UCSC) and Ensembl, primer3input^[309]84,
   net primer (PREMIER Biosoft) and primer blast (NCBI GenBank)^[310]85.
   Primers against the human target genes COBLL1 (forward
   TGATCCTGACTCAGCCCTT, reverse CAACTACCTTCTTTCCATTGC) LEFTIN (forward
   TGGGAAGGAAAATGCATTGGG, reverse ATAAGGT CAGGATGGGGTGG) and GLUT4
   (forward CTGTGCCATCCTGATGACTG, reverse CCAGGGCCAATCTCAAAA) and the
   reference genes HPRT1 (forward TGAAAAGGACCCCACGAAG, reverse
   AAGCAGATGGCCACAGAACTAG), PPIA (forward TGGTTCCCAGTTTTTCATC, reverse
   CGAGTTGTCCACAGTCAGC) and IPO8 (forward CGGATTATAGTCTCTGACCATGTG,
   reverse TGTGTCACCATGTTCTTCAGG) were synthesized by Eurofins.

   Total RNA was extracted using the RNeasy Mini Kit (QIAGEN) and 0.5 μg
   was reverse-transcribed using the High-Capacity cDNA Reverse
   Transcription Kit. RT–qPCR was performed using 96-well plates (black
   frame, white wells) with heat-sealing films, fixed by the 4s2 Automated
   Heat Sealer (all from 4titude). The Maxima SYBR Green Mix (Thermo
   Fisher Scientific) was used for amplification in a RT–qPCR Mastercycler
   ep realplex (Eppendorf), with a denaturation step of 95 °C for 10 min
   and 40 cycles of 95 °C for 15 s and 60 °C for 40 s, followed by a
   melting curve. Relative gene expression was calculated by the 2–^ΔΔCt
   method^[311]86 with a reference gene index of HPRT, PPIA and IPO8 or
   the reference gene HPRT (weight loss study).

Mice

   All mice (C57BL/6J) were originally obtained from the Charles River
   Laboratories. To genetically engineer a Cobll1 whole-body knockout
   (Cobll1^−/−) model, we used CRISPR–Cas9 genome editing system. Male
   mice were weaned at 4 weeks of age and body weight was measured every
   week from 4 to 14 weeks of age. All mice were housed in a
   temperature-controlled room (22 °C with approximately 50–65% humidity)
   on a 12-h light–dark cycle with ad libitum access to food (normal diet:
   14% fat, 64.8% carbohydrate and 21.2% protein, catalogue no. 2920X,
   Harlan Teklad) and under pathogen-free conditions. To analyse body fat
   mass (%), body length (cm) and BMD (g cm^−2), we used the DEXA scan.
   Before scanning, animals were anaesthetized with ketamine. All
   procedures were conducted with approval from the Institutional Animal
   Care and Use Committee of the University of Chicago (no. ACUP-71656;
   IBC0934).

CRISPR–Cas9-mediated generation of a Cobll1 knockout mouse model.

   To confirm directly that ablation of Cobll1 affects T2D-related
   phenotypes in vivo, we applied the CRISPR–Cas9 system to genetically
   engineer a Cobll1 whole-body knockout (Cobll1^−/−) model. Using
   specific sgRNAs, we targeted the Cobll1 gene in the C57BL/6 genetic
   background. We used guides with the following sequences: gRNA (exon 2)
   5′-TTGCTCACTAGTGGGGTCGCAGG-3′ and gRNA (exon 6)
   5′-CTTCCTCCGGCCGAGACGAAGGG-3′.

Genotyping.

   The genotypes of Cobll1 mutant mice were determined by PCR
   amplification of gDNA extracted from tails. PCR was performed for 30
   cycles at 95 °C for 30 s, 60 °C for 15 s and 72 °C for 30 s, with a
   final extension at 72 °C for 5 min. PCR amplification was performed
   using the following primer sets: forward 5′-AAAAGTTTCCTGATGTGAAAGTCA-3′
   and reverse 5′-AAAAACAGATGCTCCCCAGA-3′. The PCR products were
   size-separated by electrophoresis on a 4% agarose gel for 1 h.

In vivo glucose tolerance test.

   At 16 weeks old, mice were tested for glucose sensitivity by IPGTT.
   Before IPGTT, mice were fasted for 4 h and an initial blood glucose
   reading was taken. This fast was followed by intraperitoneal injection
   of 2 mg kg^−1 dextrose (catalogue no. CAS 50-99-7, Merck Millipore) and
   subsequent blood glucose checks using an Accu-Chek Aviva glucometer
   (Roche). Blood glucose readings were taken at 15, 30, 60 and 120 min
   after dextrose injection. After IPGTT, mice resumed a high-fat diet. An
   unpaired, two-sided Student’s t-test was used to test for significance.

Real-time qPCR.

   After establishment of stable Cobll1 knockout mice, ablation of Cobll1
   expression was confirmed by RT–qPCR in relevant tissues, which showed
   significant decrease in the mRNA fold change of Cobll1 knockout mice
   compared to WT and heterozygous littermates. Total RNA was isolated
   from the inguinal white fat pad, kidney and liver using the RNA
   extraction reagent RNeasy Mini Kit (catalogue no. 74104, QIAGEN). cDNA
   synthesis was performed using the Superscript III First-Strand
   Synthesis System (catalogue no. 18080-044, Thermo Fisher Scientific).
   Real-time qPCR reactions were performed using the SsoAdvanced Universal
   SYBR Green Supermix (catalogue no. 1725270, Bio-Rad Laboratories).
   Real-time qPCR amplification was performed using the primer sets: qPcrF
   5′-CGTCACAGAGCAACAAGACA-3′ and qPcrR5′-ACTGAGCACAGAGGAACACG-3′.

Isolation, culture and differentiation of mouse preadipocytes.

   Primary adipocytes were isolated from dissected SAT of 6-week-old mice
   and digested in 1 g ml^−1 type I collagenase solution (catalogue no.
   [312]LS004174, Worthington Biochemical Corporation) solution
   (containing 3.5% BSA v/v (catalogue no. A9418, Merck Millipore)) in a
   37 °C water bath with shaking at 120 rpm for 45 min. The suspension was
   centrifuged at 250g for 5 min; then, the cell pellet was resuspended in
   culture medium (high-glucose DMEM, catalogue no. D5796, Merck
   Millipore), 20% FCS (catalogue no. A31605-01, Thermo Fisher
   Scientific), 100 U ml^−1 penicillin and 0.1 mg ml^−1 streptomycin
   (catalogue no. 15140-122, Thermo Fisher Scientific), was filtered
   through a 45-μm strainer and was seeded in 25-cm^2 flasks. Confluent
   preadipocytes were induced for 2 d with an adipogenic medium
   (high-glucose DMEM, 10% FCS, penicillin-streptomycin (10,000 U ml^−1,
   10,000 μg ml^−1), 850 nM insulin (catalogue no. 12585014, Thermo Fisher
   Scientific), 1 nM T3 (catalogue no. T6397, Merck Millipore), 500 μM
   IBMX (catalogue no. 15879, Merck Millipore), 1 μM dexamethasone
   (catalogue no. D4902, Merck Millipore), 125 μM indomethacin (catalogue
   no. 70270, Cayman Chemical) and 1 μM rosiglitazone (catalogue no.
   71740, Cayman Chemical), and then switched to differentiation medium
   (adipogenic medium without IBMX, dexamethasone and indomethacin). Cells
   were collected on the eight day of differentiation and used for further
   analysis.

Oil Red O and GPDH assay of mouse preadipocytes.

   Oil Red O staining was used to assess the presence of lipids in mature
   adipocytes. For Oil Red O staining, cells were washed with PBS
   (catalogue no. [313]P38135, Merck Millipore) and fixed with 4% PFA
   (catalogue no. P6148, Merck Millipore). Fixed cells were then covered
   with 3 mg ml^−1 Oil Red O (catalogue no. O0625, Merck Millipore)
   dissolved in 60% isopropanol (v/v) for 20 min; then the dye was washed
   away with H[2]O. To determine GPDH activity, we used a commercially
   available kit from Takara Bio (catalogue no. MK426), by monitoring the
   dihydroxyacetone phosphate-dependent oxidation of NADH at 340 nm.
   Enzyme activity was calculated by the formula described in the
   manufacturer’s protocol; GPDH activity was expressed as U mg^−1
   protein.

Statistics and reproducibility

   Statistical analyses were performed using a two-tailed Student’s t-test
   or ANOVA to compare the means of two or multiple groups, respectively.
   No data were excluded from the analyses.

Extended Data

Extended data Fig. 1 ∣. The pleiotropic 2q24.3 MONW locus is associated with
increased risk for type 2 diabetes and decreased adiposity related traits and
maps to sparse enhancer signatures in adipocytes.

   Extended data Fig. 1 ∣
   [314]Open in a new tab

   (a) Schematic overview for the 2q24.3 metabolic risk locus dissection.
   Aim of step (top, bold); methods/experiments used (middle); key
   finding/result of each step (bottom). (b) PheWAS of trait associations
   at the rs3923113-tagged haplotype of a meta-analysis
   [315]https://t2d.hugeamp.org/. Colors represent trait classes while
   individual rs3923113 variant association p-values are shown on the Y
   axis. Direction of effect is indicated by orientation of triangles,
   upward: increase, downward: decrease (c) The 2q24.3 MONW locus spans 19
   non-coding SNPs in high linkage disequilibrium with rs3923113 (LD r2 >
   0.8). The region of association localizes to a >55 kb interval in an
   intergenic region between COBLL1 and GRB14. (d) Annotation panel and
   color key for the twenty-five state chromatin model70. Rows represent
   chromatin states abbreviations, columns are emission parameters,
   corresponding to the frequency with which each mark is expected in each
   state (left table) and genome coverage and median enrichments of
   relevant genomic annotations (right panel). TssA: Active TSS, TssAFlnk:
   Flanking Active TSS, TxFlnk: Transcription at gene 5’ and 3’, Tx:
   Strong Transcription, TxWk: Weak Transcription, EnhG: Genic enhancers:
   Enh: Enhancers, ZNF/Rpts: ZNF genes & repeats, Het: Heterochromatin,
   TssBiv: Bivalent/Poised TSS, BivFlnk: Flanking Bivalent TSS/Enhancer,
   EnhBiv: Bivalent Enhancer, ReprPC: Repressed Polycomb, ReprPCWk: Weak
   Repressed Polycomb, Quies: Quiescent/Low. (e) Stranded allele-specific
   chromatin accessibility measures at the haplotype using ATAC-seq data
   in differentiating adipocytes from a heterozygous individual. For each
   day of differentiation of an individual heterozygous, the number of
   reads overlapping with 20 non-coding SNPs in the haplotype, ordered by
   their start position and strand relative to the position of the
   variant, are shown. More reads indicate higher activity in haplotype 1
   (non-risk, blue) compared to haplotype 2 (risk yellow). x-axis: offset
   from SNP position (bp), y-axis: stranded read count. (f) Replication of
   the effect at time 0 (mesenchymal stem cells) with ATAC-seq. (g)
   BMI-dependent variant association analysis. Bar plots represent the
   beta of the rs6712203 association with type 2 diabetes following BMI
   stratification. The cohort analysed is the UK Biobank self-identified
   white British individuals (total N = 327,960; N = 109198 with BMI < 25,
   N = 140539 with BMI between 25 and 30, and N = 78223 with BMI > = 30),
   and overlay of data points is not practical. Betas and 95% confidence
   intervals are shown, derived from a two-sided generalized linear model
   on outcome adjusted for demographic covariates (age, sex, genotyping
   array, 40 PCs).

Extended data Fig. 2 ∣. Conditional ana- lyses implicating rs6712203 in the
genetic control of anthropometric traits and type 2 diabetes.

   Extended data Fig. 2 ∣
   [316]Open in a new tab

   a, Conditional analyses implicating rs6712203 in the genetic control of
   anthropometric traits and type 2 diabetes. Each panel represents a
   different trait/sex/conditional analysis window, and all panels have an
   X axis corresponding to 100 kb on either side of the rs6712203 variant.
   The Y axis shows, for each variant in the window, the association
   strength for the given trait conditioned on the variants noted in White
   British participants in UK Biobank with the sex shown, and red lines
   indicate the significance threshold 5 × 10-8). −log10 p-values are
   shown, derived from a two-sided generalized linear model on outcome
   adjusted for demographic covariates (age, sex, genotyping array, 40
   PCs).

Extended data Fig. 3 ∣. Chromatin inter- actions and CRISPRi of 2q24.3 locus
identify COBLL1 as target genes.

   Extended data Fig. 3 ∣
   [317]Open in a new tab

   (a) Cross-cell type conserved genome-wide higher order chromatin
   interactions for the 2q24.3 locus analyzed by Hi-C assays in human
   fibroblasts (left) and NHEK primary normal human epidermal
   keratinocytes (right), chr2: 163,556,000 - 167,558,000 (hg19), binned
   at 2 kb resolution. (b) Cas9 protein expression in dCas9 hWAT compared
   to the parental hWAT cell line. (c) mRNA expression of COBLL1 and GRB14
   in response to increasing amounts of lentiviral sgRNA vectors (2
   sgRNAs, virus volume 50 μl and 500 μl) targeting TSS regions of each
   gene compared to non-targeting controls (NT, 2 sgRNAs). Columns are
   means of individual sgRNAs indicated by different symbols. (d) COBLL1
   protein expression normalized to b-actin in dCas9 hWATs transduced with
   sgRNAs targeting COBLL1 or GRB14 compared to controls. Top panel: Image
   of gel of representative sgRNA targeting NT, COBLL1 or GRB14. Bottom
   panel: plot of protein expression; 2 sgRNA for each target in 2
   replicates. (e) Representation of 1,181 bp region flanking the COBLL1
   intronic variant rs6712203 at the 2q24.3 MONW locus showing individual
   sgRNAs (n = 6) targeting the rs6712203 flanking regulatory region used
   in the CRISPRi experiments. (f, g) mRNA expression of (f) COBLL1 and
   (g) GRB14 in undifferentiated dCas9-hWAT preadipocytes at 6 days post
   lentiviral transduction with sgRNAs targeting TSS regions (red: COBLL1
   TSS; blue GRB14 TSS) and the rs6712203-flanking regulatory element at
   position 1 to 6 as depicted in (e). Data are mean +/− SEM of 3
   independent experiments. **** P < 0.0001, *** P = 0.0004, ** P = 0.006,
   * P = 0.013 – 0.036, two-tailed Student’s t test. (h) Predicted binding
   of POU2F2 between the two alleles using the Intragenomic Replicate
   Method (Cowper-Sal lari et al. 2012). As in [318]Fig. 2d with different
   kmer counts.

Extended data Fig. 4 ∣. COBLL1 regulates actin cytoskeleton remodeling.

   Extended data Fig. 4 ∣
   [319]Open in a new tab

   (a) COBLL1 expression in subcutaneous and visceral AMSCs throughout
   adipogenic differentiation, N = 4 biologically independent experiments,
   t-test two-sided, data represent median + 95% CI. (b) COBLL1 gene
   expression enrichment across 142 tissues (A-D) from enrichment
   profiler36. COBLL1 probes 203641_s_at and 203642_s_at were used for
   coregulation analysis (E-F). (c) Correlation with COBLL1 probe
   ILMN_1761260 using microarray data from lean and individuals with
   obesity. (d) Enrichment of pathways in the HCI (upper panel) and
   WikiPathways (lower panel) gene set lists from Enrichr, plotted as in
   [320]Fig. 3A (KEGG), with p-value thresholds corresponding to the FDR
   cutoffs in those data. p-values are derived from a hypergeometric test.
   (e) COBLL1 expression in subcutaneous adipose tissue before and after a
   very low caloric diet (VLCD, upper panel, n = 18), corresponding body
   weight (lower panel), Wilcoxon signed-rank test.

Extended data Fig. 5 ∣. Knockdown of COBLL1 affects actin remodeling
processes in differentiating adipocytes along with adipocyte differentiation
insulin sensitivity and lipolysis rate.

   Extended data Fig. 5 ∣
   [321]Open in a new tab

   (a) COBLL1 expression in siCOBLL1 and siNT at day 0, 3 and 14 of
   differentiation, N = 3 biologically independent experiments, t-test
   two-sided. knock-down efficiency 80%, mean values + SEM. (b–d)
   Morphological profiles of siCOBLL compared to siNT AMSCs at day 0 (b)
   day 3 (c) and day 9 (d) of differentiation, t-test two-sided,
   significance level < 5%FDR. (e) Actin and COBLL1 staining in siCOBLL1
   compared to siNT subcutaneous adipocytes at day 9 using phalloidin and
   COBLL1 antibody staining (HPA053344, Alexa-Fluor 488), magnification
   x63/oil. Scale bar = 52.8 um. Representative results from N = 3
   independent experiments. (f) Cells_Children_ LargeBODIPY_objects_count
   in siCOBLL1- and siNT AMSCs at day 3, 9, 14, N = 3 biologically
   independent experiments, t-test two-sided, significance level < 5% FDR.
   (G) qPCR-based gene expression of COBLL1 and adipocyte marker genes
   GLUT4, FASN, LIPE, PPARG, PLIN1, FABP4, CEBPA, ADIPOQ in siCOBLL1 and
   siNT AMSCs at day 14 of differentiation, t-test two-sided, N = 4
   biologically independent experiments, mean values +/− SEM. (h)
   qPCR-based leptin gene expression in shCOBLL1 compared to shEV
   adipocytes. Data are represented as median + 95% CI, one-way ANOVA with
   Tukey’s HSD test, N = 4 biologically independent experiments (i)
   Correlation of COBLL1 mRNA with LEP mRNA in subcutaneous adipose tissue
   from 24 lean individuals measured by Illumina microarrays. The
   pearson’s correlation coefficient r and p-value are depicted (j)
   Schematic of siCOBLL1 KD and AMSCs differentiation. (k) UMAP-based
   dimensionality reduction of LipocyteProfiler features in siCOBLL1 and
   siNT AMSCs. (l) Actin and COBLL1 staining in siCOBLL1 and siNT visceral
   adipocytes at day 14 using phalloidin and COBLL1 antibody staining
   (HPA053344, Alexa-Fluor 488), magnification x63/oil. Representative
   result from N = 2 independent experiments, scale bar = 52,8um (m)
   Representative Oil-Red-O lipid staining in SGBS adipocytes following
   lentiviral COBLL1 knock-down (shCOBLL1, knock-down efficiency 69%) and
   GRB14 (shGRB14, knock-down efficiency 61%) compared to empty vector
   control (shEV), scale bar = 15 mm. (n) GPDH metabolic activity in
   shCOBLL1, shGRB14 and shEV SGBS adipocytes, one-way ANOVA with Tukey’s
   HSD test, mean + 95% CI, N = 4 biologically independent experiments (o)
   Basal and insulin-stimulated 3H-2-deoxyglucose uptake in shCOBLL1,
   shGRB14 and shEV SGBS adipocytes, one-way ANOVA with Tukey’s HSD test,
   mean + 95% CI, N = 4 biologically independent experiments, 1st and 3rd
   quartiles (box) and median (middle line) are indicated, p = 4.3 × 10-8.
   (p) qPCR-based GLUT4 gene expression in shCOBLL1, shGRB14 and shEV
   adipocytes, one-way ANOVA with Tukey’s HSD test, mean + 95% CI, N = 4
   biologically independent experiments.

Extended data Fig. 6 ∣. Lipocyte profiles of risk versus non-risk haplotype
carriers.

   Extended data Fig. 6 ∣
   [322]Open in a new tab

   (a–c) Differences in morphological profiles between TT (n = 7) and CC
   (n = 6) allele carriers at day 0 (a), day 3 (b) and day 8 (c) in
   subcutaneous AMSCs (multi-way ANOVA, significance level < 5% FDR).
   (d–f) Differences in morphological profiles between TT (n = 7) and CC
   (n = 6) allele carriers at (d) day 0, (e) day 3 and (f) day 8 in
   visceral AMSCs (multi-way ANOVA, significance level < 5% FDR).

Extended data Fig. 7 ∣. Generation of COBLL1 mutant mice using CRISPR/Cas9
editing.

   Extended data Fig. 7 ∣
   [323]Open in a new tab

   (a) Overview of the CRISPR/− Cas9 strategy to delete ~20 kb of the
   Cobll1 gene. The gRNA-targeting sequences (gRNAs) are underlined, and
   the PAM sequences are indicated in bold. Exons are represented as thick
   black boxes introns are indicated as black lines with arrows, and the
   yellow boxes indicate the DNA-targeting region. Red hexagon indicates a
   stop codon gen- erating a Cobll1 truncated protein. Agarose gel showing
   the PCR products generated from DNA containing success- fully targeted
   Cobll1 from F0 mouse tail genomic DNA. The 308 bp band corresponds to
   the genomic deletion. (b) A real-time quantitative PCR of levels of
   Cobll1 mRNA in white adipose tissue (WAT), liver and kidney of Cobll1
   WT, Cobll1 heterozygous (+/−) and null knockout Cobll1 (−/−) animals to
   confirm the Cobll1 ablation in knockout animals. Each group was
   analyzed using 5 different mice and the values were expressed as the
   mean ± s.e.m and P values by Student’s t-test the experiment was
   repeated independently two times with similar results. (c) Pie chart
   illustrating non-redundant differential features per channel and class
   of measurement at day 8 of subcutaneous adipocyte differentiation in
   rs6712003 homozygous risk compared to non-risk carriers. (d, e)
   Differences in morphological profiles between AMSCs from Cobll1−/− mice
   (n = 3) and WT (n = 4) at (h) day 0 (i) day 2 (t-test two-sided,
   significance level < 5%FDR).

Supplementary Material

   Supplementary Material
   [324]NIHMS1924044-supplement-Supplementary_Material.pdf^ (138.8MB, pdf)
   st4
   [325]NIHMS1924044-supplement-st4.pdf^ (31.8KB, pdf)
   st3
   [326]NIHMS1924044-supplement-st3.pdf^ (59.8KB, pdf)
   st6
   [327]NIHMS1924044-supplement-st6.pdf^ (32.6KB, pdf)
   st7
   [328]NIHMS1924044-supplement-st7.pdf^ (50.2KB, pdf)
   st9
   [329]NIHMS1924044-supplement-st9.pdf^ (54KB, pdf)
   st8
   [330]NIHMS1924044-supplement-st8.pdf^ (80.3KB, pdf)
   st11
   [331]NIHMS1924044-supplement-st11.pdf^ (57.6KB, pdf)
   st10
   [332]NIHMS1924044-supplement-st10.pdf^ (70.1KB, pdf)
   st12
   [333]NIHMS1924044-supplement-st12.pdf^ (54.6KB, pdf)
   st1
   [334]NIHMS1924044-supplement-st1.pdf^ (28.8KB, pdf)
   st2
   [335]NIHMS1924044-supplement-st2.pdf^ (27.3KB, pdf)
   st5
   [336]NIHMS1924044-supplement-st5.pdf^ (17.8MB, pdf)

Acknowledgements