Abstract
Childhood obesity is a global public health problem. Understanding the
molecular mechanisms that underlie early origins of childhood obesity
can facilitate interventions. Consistent phenotypic and genetic
correlations have been found between childhood obesity traits and birth
weight (a proxy for in-utero growth), suggesting shared genetic
influences (pleiotropy). We aimed to (1) investigate whether there is
significant shared genetic influence between birth weight and childhood
obesity traits, and (2) to identify genetic loci with shared effects.
Using a statistical approach that integrates summary statistics and
functional annotations for paired traits, we found strong evidence of
pleiotropy (P < 3.53 × 10^–127) and enrichment of functional
annotations (P < 1.62 × 10^–39) between birth weight and childhood body
mass index (BMI)/obesity. The pleiotropic loci were enriched for
regulatory features in skeletal muscle, adipose and brain tissues and
in cell lines derived from blood lymphocytes. At 5% false discovery
rate, 6 loci were associated with birth weight and childhood BMI and 13
loci were associated with birth weight and childhood obesity. Out of
these 19 loci, one locus (EBF1) was novel to childhood obesity and one
locus (LMBR1L) was novel to both birth weight and childhood
BMI/obesity. These findings give evidence of substantial shared genetic
effects in the regulation of both fetal growth and childhood obesity.
Subject terms: Genetics, Risk factors
Introduction
Childhood overweight or obesity is a global public health
problem^[28]1–[29]3. Complex interactions of genetic
susceptibility^[30]4–[31]6, environmental exposures^[32]7,[33]8 and
in-utero programming^[34]9–[35]12 contribute to childhood obesity.
Fetal growth and development during the intrauterine period shape
physiological and structural processes that impact risk for childhood
obesity (COB)^[36]9. Birth weight, a marker of fetal growth and
prenatal environment, has been consistently associated with
COB^[37]13–[38]20. Notably, a significant positive genetic correlation
has been found between birth weight and COB/childhood body mass index
(CBMI)^[39]21, suggesting the possibility for genetic links these
phenotypes may share. Insight into the shared genetic underpinnings of
these early life phenotypes facilitates knowledge on the potential
common molecular pathways and intervention targets of abnormal birth
weight and COB/CBMI. However, pleiotropic evidence on these traits
remains scarce.
Recent genome wide association studies (GWAS) have reported that shared
genetic effects (pleiotropy) might explain a substantial proportion of
correlations between complex human traits^[40]22–[41]26. In an analysis
of the GWAS catalog^[42]27 it was found that 16.9% of the reported
genes were associated with multiple traits^[43]28. With the advent of
large scale independent GWASs on multiple early life traits and
functional annotation database^[44]29–[45]31, innovative statistical
approaches can be applied to identify and test for novel pleiotropic
loci.
In addition, accounting for shared genetic effects improves statistical
power for detecting genetic variants that are associated with complex
traits^[46]32. Both birth weight and COB/CBMI are under strong genetic
control^[47]10,[48]11,[49]33–[50]35, with relatively high heritability
estimates of 25–40% for birth weight^[51]35 and 30–70% for
COB/CBMI^[52]34. However, the heritability of the traits explained by
genetic variants identified using published GWAS for birth
weight^[53]36, COB^[54]37 and CBMI^[55]38–[56]40 was less than 10%.
Therefore, detecting novel genetic loci via pleiotropic analysis can
help close the gap in missing heritability.
To date, pleiotropic associations of birth weight and COB/CBMI have not
been exclusively investigated. In the current study, we used an
innovative statistical approach that integrates pleiotropy and
functional annotation data with the following aims: (1) to investigate
whether there is significant shared genetic influence between birth
weight and COB/CBMI (pleiotropy enrichment test), (2) to examine
whether the shared risk variants are functionally more enriched in
birth weight and COB/CBMI as compared to only considering single traits
in models that incorporate a variety of genetic annotations in
different tissues and cell types (annotation enrichment test), and (3)
to identify genetic loci with shared genetic effects on birth weight
and COB/CBMI. Our analysis found abundant evidence of pleiotropy and
significant enrichment of functional annotations for shared risk
variants associated with birth weight and COB/CBMI. We identified
genetic loci with overlapping influence on both birth weight and
COB/CBMI. Most of those loci have been associated with childhood
BMI/obesity in previous GWAS but not with birth weight, whereas one
locus (EBF1) was novel to childhood obesity and one locus (LMBR1L) was
novel to both birth weight and childhood BMI/obesity.
Results
Data on summary statistics including P values and direction of effects
in GWAS meta-analysis of birth weight, CBMI and COB was obtained from
publicly available data of the Early Growth Genetics (EGG) consortium.
Two sets of GWAS summary statistics were available for birth weight,
one involving European ancestry individuals (BW[EU]) and a second from
trans-ancestry meta-analysis (BW[TR]). The GWAS summary statistics for
CBMI and COB were from European ancestry individuals. Therefore, tests
of pleiotropy were conducted on four trait pairs: BW[EU]-CBMI,
BW[EU]-COB, BW[TR]-CBMI, and BW[TR]-COB.
Pleiotropy analysis was performed via the R package GPA^[57]41 which
implements a statistical approach to explore the genetic architecture
of complex traits by integrating pleiotropy and functional annotation
information, including prioritizing risk genetic variants. GPA performs
hypothesis test for evaluating enrichment of pleiotropy and functional
annotations using a likelihood ratio test approach. For each trait
pair, we performed tests of pleiotropy and enrichment of functional
annotations based on 4 functional categories: combined annotation
dependent depletion (CADD)
([58]http://www.cadd.gs.washington.edu)^[59]42, expression quantitative
trait loci (eQTL) ([60]https://gtexportal.org/home/datasets)^[61]43,
transcription factor binding sites (TFBS)
([62]ftp://ccg.epfl.ch/snp2tfbs/mapped_files/annotated)^[63]44, and
DNase I hypersensitivity sites (DHS)
([64]https://github.com/joepickrell/1000-genomes)^[65]45.
Evidence of pleiotropy between birth weight and childhood obesity traits
Under each functional category, we found evidence of significant
pleiotropic genetic effects for all tested trait pairs. Pleiotropic
genetic effects had enrichment fold ranging from 2.6 to 5.4
(1.17 × 10^–277 < P < 3.51 × 10^–127) under CADD annotation, 2.6 to 5.2
(1.18 × 10^–277 < P < 3.51 × 10^–127) under eQTL annotation, 2.6 to 5.1
(2.52 × 10^–276 < P < 6.52 × 10^–127) under DHS annotation, and
2.6 to 5.1 (1.18 × 10^–277 < P < 6.52 × 10^–127) under TFBS annotation
(Table [66]1).
Table 1.
Genetic pleiotropy and enrichment of functional deleteriousness among
genetic loci associated with birth weight and childhood obesity traits.
Trait pair Annotation Genetic pleiotropy Functional annotation
enrichment
Enrichment fold (s.e) P value q11/q00 (s.e) P value
BW[EU] and CBMI CADD 2.683 (0.05) 1.17 × 10^–277 1.69 (0.08)
2.69 × 10^–38
eQTLs 2.679 (0.05) 1.18 × 10^–277 2.10 (0.04) ≤ 1 × 10^–300
DHSs 2.67 (0.05) 2.52 × 10^–276 1.17 (0.01) ≤ 1 × 10^–300
TFBS 2.685 (0.05) 1.18 × 10^–277 1.09 (0.03) 0.03
BW[EU] and COB CADD 5.413 (0.19) 9.79 × 10^–144 1.54 (0.18)
1.42 × 10^–27
eQTLs 5.396 (0.29) 9.79 × 10^–144 1.73 (0.09) ≤ 1 × 10^–300
DHSs 5.379 (0.24) 1.45 × 10^–143 1.15 (0.03) ≤ 1 × 10^–300
TFBS 5.383 (0.24) 9.79 × 10^–144 1.01 (0.06) 0.21
BW[TR] and CBMI CADD 2.653 (0.05) 9.97 × 10^–274 1.79 (0.08)
1.62 × 10^–39
eQTLs 2.648 (0.05) 9.98 × 10^–271 2.15 (0.04) ≤ 1 × 10^–300
DHSs 2.641 (0.05) 2.18 × 10^–269 1.17 (0.02) ≤ 1 × 10^–300
TFBS 2.655 (0.05) 9.98 × 10^–271 1.10 (0.03) 0.01
BW[TR] and COB CADD 5.209 (0.17) 3.51 × 10^–127 1.68 (0.18)
1.94 × 10^–29
eQTLs 5.204 (0.18) 3.51 × 10^–127 1.80 (0.10) ≤ 1 × 10^–300
DHSs 5.186 (0.20) 6.52 × 10^–127 1.12 (0.03) ≤ 1 × 10^–300
TFBS 5.197 (0.18) 3.51 × 10^–127 0.99 (0.07) 0.04
[67]Open in a new tab
q11/q00 is the ratio of the probability of jointly associated SNPs
being functionally annotated to the probability of a null SNP (not
associated with either trait) being functionally annotated.
BW[EU] and CBMI European birth weight and childhood body mass index,
BW[EU] and COB European birth weight and childhood obesity, BW[TR] and
CBMI trans-ethnic birth weight and childhood body mass index, BW[TR]
and COB trans-ethnic birth weight and childhood obesity, CADD combined
annotation dependent depletion, eQTLs expression quantitative loci,
DHSs DNase I hypersensitivity sites, TFBS transcription factor binding
sites.
Enrichment of functional annotations
Next, we assessed whether genetic loci with regulatory functional
annotations had higher likelihood of being associated with both birth
weight and CBMI/COB as compared to genetic loci without functional
annotations. Functional annotation of the 2.4 million single-nucleotide
polymorphisms (SNPs) tested was done using CADD^[68]42, eQTL^[69]43,
TFBS^[70]44 , and DHS^[71]45. Out of the ~ 2.4 million SNPs tested,
approximately 2% of the SNPs were annotated as CADD related, 7% as eQTL
related, 20% as TFBS related and 51% as DHS related (Table [72]S1).
Enrichment of each of the four functional categories was tested for
four trait pairs using the GPA model^[73]41, resulting in 16 tests of
functional enrichment.
To compare the functional deleteriousness of the pleiotropic SNPs with
non-pleiotropic SNPs, each variant in our analysis was annotated to be
deleterious or non-deleterious based on CADD score ≥ 15 or CADD
score < 15, respectively^[74]42. Out of the 16 tests, SNPs associated
with pairs of birth weight and childhood obesity traits were more
likely to be functionally deleterious than SNPs associated with neither
trait in 15 tests (enrichment fold ranging from 0.99 to 2.15 and
Bonferroni corrected P values ranging from
1 × 10^–300 ≤ P < 1.62 × 10^–39); however, 1 test under TFBS category
for BW[EU]-COB pair was not significant (P = 0.21) (Table [75]1).
Enrichment of eQTL annotation was consistently higher for SNPs
associated with both birth weight and COB traits compared to SNPs
associated with only birth weight or CBMI/COB (Fig. [76]1).
Figure 1.
[77]Figure 1
[78]Open in a new tab
Enrichment of four functional annotations among loci jointly associated
with birth weight and childhood obesity traits. (A) European birth
weight and childhood body mass index (BW[EU]-CBMI), (B) European birth
weight and childhood obesity (BW[EU]-COB), (C) Trans-ethnic birth
weight and childhood body mass index (BW[TR]-CBMI), (D) Trans-ethnic
birth weight and childhood obesity (BW[TR]-COB). The bars denote the
enrichment fold for variants that are associated jointly with BW and
childhood obesity traits (red), only with birth weight (green), and
only with childhood obesity traits (blue), respectively.
We further examined the specific tissues, cell types and transcription
factors that had relatively higher functional enrichment among the
common variants between birth weight and childhood obesity traits. For
each trait pair, we performed annotation enrichment tests of 49 tissues
with eQTL annotations^[79]43, 402 tissues/cell lines/cell types with
DHSs annotations^[80]45, and 195 transcription factors in the TFBS
database^[81]44, one at a time. The most significant eQTL enrichments
(P < 0.05 and lowest in the joint trait association) were observed in
tissues from skeletal muscle, adipose, brain, heart, esophagus,
thyroid, adrenal, colon, small intestine and whole blood. The most
significant DHS enrichments were observed in cell lines derived from
blood lymphocytes (e.g. CD4+), skin (e.g. iPS), cancer (e.g. HeLa),
embryo (e.g. embryonic stem cells) and in tissues from fetal brain. The
most significant transcription factor enrichments were Interferon
Regulatory Factor 1, AT-Rich Interaction Domain 3A and Testicular
receptor 4 (Table [82]S2–[83]S3).
At 5% false discovery rate, 19 loci (consisting 509 SNPs, with each
locus having correlated SNPs with linkage disequilibrium r^2 > 0.5)
were jointly associated with birth weight and childhood obesity traits
(Figs. [84]S1–[85]S4). Among these 19 loci, 5 loci were jointly
associated with European BW and COB traits, 1 locus was jointly
associated with transethnic BW and COB traits and 13 loci were jointly
associated with both European and transethnic BW and COB traits (Table
[86]S4–[87]S5). Additionally, out of these 19 loci, 17 loci were novel
to birth weight but not childhood obesity traits at the genome-wide
significance level (P < 5 × 10^–8, in the NHGRI-EBI GWAS catalogue:
[88]www.ebi.ac.uk/gwas/), one locus (rs6887211 in EBF1) was novel to
childhood obesity traits but not birth weight, and one locus (rs7958572
4 Kb upstream to LMBR1L) was novel to both birth weight and childhood
obesity traits (Fig. [89]2). The EBF1 locus was only suggestively
associated with childhood obesity traits (P = 8.44 × 10^–5) and the
LMBR1L locus was only suggestively associated with birth weight
(P = 2.4 × 10^–3) and childhood obesity traits (P = 7.3 × 10^–6) in
previous GWAS^[90]36,[91]39.
Figure 2.
[92]Figure 2
[93]Open in a new tab
Regional plots of novel pleiotropic loci associated with birthweight
and childhood obesity traits. (A) rs6887211 (EBF1) locus association
with both birth weight and childhood body mass index (B) rs7958572
(LMRB1L) locus associated with both birthweight and childhood obesity
or childhood body mass index. The horizontal axes cover a region 500 kb
upstream and downstream from the reference SNP. The vertical axes
denote the joint association probabilities of the SNPs with both
birthweight and childhood obesity traits. The purple triangles denote
the index SNPs (rs6887211 in EBF1 and rs7958572 in LMRB1L). All other
colored points denote the surrounding SNPs in that region, and they are
colored based on their linkage disequilibrium (r^2) with the reference
SNP. The box at the bottom shows genes that fall in the region.
For most of the jointly associated loci, the alleles associated with
decreased birth weight were associated with lower CBMI/COB risk. In
contrast, for three loci (EBF1, NCOA1 and SEC16B), nearly all alleles
associated with decreased birth weight were associated with higher
CBMI/COB risk (Table [94]S4–[95]S5). Furthermore, we evaluated our
findings using the European-ancestry summary statistics in a recent
childhood obesity GWAS^[96]46. Out of the 342 SNPs in our study found
to be associated with both birthweight and childhood obesity, 98.5%
reached > 95% posterior probability when we used this validation
childhood obesity GWAS (Table [97]S6).
Pathway analysis
To further understand the functional relevance of the genetic loci
jointly associated with birth weight and childhood obesity traits, we
performed pathway enrichment analysis of the 25 genes that are near the
pleiotropic SNPs using the Ingenuity Pathway Analysis (IPA) tool
(QIAGEN Inc, [98]https://digitalinsights.qiagen.com/). The top enriched
IPA Canonical Pathways included RAR Activation and Estrogen Receptor
Signaling (Table [99]S7) and the top enriched IPA Disease or Function
annotations included brain size and morphology (Table [100]S8).
Colocalization analysis
To determine whether the signals identified represent horizontal
pleiotropy (influence birthweight and childhood obesity via independent
biological pathways) or vertical pleiotropy (influence via shared
mechanism in a causal pathway) we performed colocalization analysis
using the coloc R package^[101]47. Only the EBF1 locus showed evidence
of colocalization with a posterior probability of 0.87 for being
associated with both traits and share a single causal variant (Tables
[102]S9 and [103]S10).
Discussion
There is growing evidence that several genetic variants can influence
two or more traits^[104]28,[105]48,[106]49. Investigating such effects
in early life traits can facilitate our understanding of their complex
genetic architecture and in developing early life interventions to
promote long-term health^[107]50,[108]51. The present study revealed
substantial genetic pleiotropic effects between birth weight and
childhood obesity traits. We also found that biologically functional
SNPs are more likely to be associated with both birth weight and COB
traits compared to SNPs that are not functional, consistent with
evidence of high evolutionary conservation of pleiotropic genes and
their consequences^[109]52. Lastly, we identified 19 genetic loci with
pleiotropic effects, including loci in EBF1 and LMRBL1 that have not
been associated with birth weight and/or COB phenotypes in previous
GWAS. With the majority (68.4%) of the 19 loci reaching the
significance threshold in analyses involving European-only or
trans-ethnic birthweight GWAS and the remaining loci exhibiting high
posterior probability of association (0.85–0.93), the results suggest
that the effects of many pleiotropic loci are likely shared across
ancestries. In all, these findings facilitate our understanding of the
genetic mechanism that may underlie associations of early life growth
with COB traits.
The birth weight-decreasing allele of the novel pleiotropic SNP
rs6887211 (EBF1) was found to be associated with increased risk of COB
traits. The association of EBF1 with birth weight and gestational
duration is well-recognized^[110]36,[111]53–[112]55. EBF1 is highly
expressed in adipocytes (Fig. [113]S5) and plays a crucial role in
adipogenesis and development of B lymphocytes. Dysregulated expression
of EBF1 is associated with adipose hypertrophy, adipose inflammation,
variation in body fat distribution and altered adipose morphology
through impaired adipogenesis, which in turn has been implicated as
a key factor in the development of obesity related
traits^[114]56–[115]59. EBF1 has a well-known association with
cardiometabolic traits in adults. SNPs near EBF1 have been associated
with blood pressure^[116]60 and hip circumference^[117]61. Notably, a
multi-ethnic GWAS in adults has found an association between rs1650505
(near EBF1) and pericardial adipose tissue volume^[118]56. Previous
studies found substantial overlaps between childhood and adult obesity
GWAS loci^[119]62. While there is relatively weak LD between the lead
SNP showing pleiotropic effect in our study (rs6887211) and rs1650505
(r^2 = 0.06 in CEU), we acknowledge that the novelty of the locus in
childhood BMI may be in part due to the small study power of the source
GWAS used for childhood BMI. Furthermore, our novel SNP and its LD
proxies (r^2 ≥ 0.8) overlapped with DNase I hypersensitivity sites of
blood lymphocytes and fetal muscle further indicating that many
components of the metabolic and inflammatory pathways are positively
and directly regulated by EBF1 as emphasized in a prior study^[120]63.
These findings collectively indicate EBF1’s role in fetal growth and
general adiposity which highlights that it should be considered as a
key player in understanding the association of early life growth with
the development of COB.
The birth weight-decreasing allele of the novel pleiotropic SNP in
LMBR1L (rs7958572) was found to be associated with decreased risk of
COB. LMBR1L has regulatory effects on the canonical Wnt signaling
pathway. It stabilizes the beta-catenin destruction complex^[121]64
that is required for regulating CTNNB1 expression, and is responsible
in making the beta-catenin protein which plays a key role in the
canonical Wnt signaling pathway^[122]65–[123]68. The Wnt signaling
pathway is conserved in various organisms and plays important roles in
development, cellular proliferation, and differentiation^[124]69.
Previous studies have demonstrated that the Wnt signaling pathway
regulates adipogenesis and maintains the undifferentiated state of
pre-adipocytes by inhibiting adipogenic gene expression
^[125]70,[126]71. In addition, the Wnt signaling cascade plays
fundamental roles in placental development by regulating trophoblast
differentiation and invasion, and aberrant Wnt signaling activation can
have downstream consequences on fetal growth ^[127]72–[128]74. Our
finding here for LMBR1L, together with the regulatory impact of LMBR1L
on Wnt signaling and the functional relevance of the Wnt pathway in
placental function, suggests a novel hypothetical mechanism by which
LMBR1L contributes to the link between fetal growth and development of
COB. Future studies such as eQTLs in tissues relevant to early life
traits can facilitate better inference from the pleiotropic SNP to a
potential functional gene.
In agreement with previous observational studies which reported that
small for gestational age (SGA) or low birth weight is associated with
both lower and higher risk of COB^[129]13–[130]20, we observed that
birth weight-decreasing alleles of pleiotropic SNPs may be associated
with either increased or decreased risk of COB. We found that the birth
weight-reducing alleles of most of the pleiotropic loci we found (e.g.
FTO, FAIM2, TMEM18) have previously been associated with decreased risk
of COB, suggesting that concurrent genetic mechanisms may play an
important role in the well-recognized positive correlation between low
birth weight and low risk of COB. We also found that in 3 out of our 19
pleiotropic loci (i.e., EBF1, SEC16B, NCOA1) the birth
weight-decreasing alleles were associated with increased risk of COB.
Genetic loci near EBF1and SEC16B genes have been associated with birth
weight, SGA and adiposity traits ^[131]53–[132]56,[133]75,[134]76;
however, the association of NCOA1 with birth weight or COB traits
remains elusive. Interestingly, co-activators like NCOA1 are
fundamental in uterine growth by regulating placental morphogenesis,
embryo survival and interacting with estrogen receptors in the human
placenta to enhance estrogen signaling which has downstream
consequences on birth weight^[135]77,[136]78. NCOA1 is also highly
expressed in the hypothalamus region of the brain (Fig. [137]S6) which
is implicated in appetite control, weight loss and shaping the
metabolic landscape of an individual^[138]79 and any disruption of
NCOA1′s function can consequently lead to several metabolic disorders
which may explain its association with COB^[139]79,[140]80. Therefore,
the mixed direction of effects found in our study highlights the
complex relationship of in-utero growth and COB traits and may provide
new scopes to understand the mechanisms of development of COB.
Functional enrichment tests and pathway analysis revealed three key
biological processes that may jointly contribute to the association of
early life growth and development of COB. Our study found that SNPs
associated with both birth weight and COB traits mapped to genes that
were significantly enriched in immune system pathways and regulated the
signaling of immune cells and estrogen. Estrogen plays an essential
role in regulating immune response, through its interactions with the
receptors on the immune cells and their functioning^[141]81. Previous
studies^[142]82,[143]83 have demonstrated that the cell‑mediated immune
response is impaired in obese children along with an
over‑representation of the immune and inflammatory response in adipose
tissues of children. We also found the pleiotropic SNPs to be
significantly enriched in Retinoic Acid (RA) signaling pathway and have
cis-regulatory effects on gene expression in adipose tissue and muscle
skeletal. Multiple studies have documented that RA regulates
adipogenesis^[144]84–[145]86. RA acts as a high affinity ligand for the
nuclear receptor peroxisome proliferation-activated receptor β/δ which
is a master regulator of lipid metabolism and glucose homeostasis.
Deactivation of these receptors decreases lipid catabolism in adipose
tissue and skeletal muscle, increasing the risk of obesity. Lastly, we
found the pleiotropic SNPs to be significantly enriched in
intracerebral signaling pathway by having regulatory effects on several
cerebral tissues. Brain-derived neurotrophic factor (BDNF), a key
neurotrophin with multipotent impact on brain signaling^[146]87 plays
an important role in regulating energy homeostasis and
metabolism^[147]88–[148]90. BDNF acts on hypothalamic PVN and VMH
neurons to suppress appetite and mediate the anorexigenic effects of
MSH acting on the MCH-4 receptor^[149]89. Deficit in BDNF levels or
signaling is attributed to the development of obesity^[150]91,[151]92,
indicating that pathways of intracerebral signaling may be related to
COB. Our results suggest that these same causal biologic pathways
likely influence early life growth, but further research is needed
to understand how these pathways that influence COB also influence
birth weight.
We recognize that our study has limitations. First, despite the large
sample sizes of the consortia-based meta-analysis GWAS studies included
in our study, there were differences in sample sizes and number of SNPs
among the various GWAS studies. These contrasts may contribute to power
differences in identifying pleiotropic loci. Additionally, some of the
observed associations might be due to independent associations of the
locus on birth weight and COB traits, due to the correlation of the
traits in a causal pathway or some unmeasured characteristics. Second,
the inference on joint associations of the variants in our study is
based on estimates from the GPA model which performs optimally under
low to moderate genetic correlation between the traits; however, under
complex and weaker correlations the estimates of the GPA model can be
biased. Third, some cohorts contributed to both childhood BMI and
birthweight GWAS. Since GPA does not account for sample overlaps, we do
not know whether the estimates are biased. Fourth, our analysis did not
find functional enrichment of the pleiotropic loci between birth weight
and COB under the TFBS annotation set, which requires further
investigation in larger samples. An important strength of our study is
the integrated modelling of functional annotations and GWAS summary
statistics data from pairs of traits. This multi-trait approach has
been most conducive in testing for functional enrichment and
identifying novel genetic loci with their shared impacts on multiple
traits, expanding our understanding of the genetic links between fetal
growth and COB traits.
In conclusion, this study found that pleiotropic genetic influences and
enrichment of functional annotations are substantially pervasive in the
genetic architectures of birth weight and COB traits. The novel loci
found in the analysis and the pathways through which the associated
genes act have the potential to unravel the genetic basis that
underlines associations between early growth and development of COB.
Materials and methods
Birth weight, childhood BMI and childhood obesity GWAS summary statistic data
Summary statistic data including P values and direction of effects in
GWAS meta-analysis of birth weight^[152]36, CBMI^[153]39 and
COB^[154]37 was obtained from the publicly available data of EGG (Table
[155]S2). The GWAS meta-analysis of CBMI was performed on 20 combined
studies with a total sample size of 35,668 children. Children aged
2–10 years with European ancestry were included in the study^[156]39.
The GWAS meta-analysis of COB combined 14 studies of European children
aged 2–10 years with a total sample size of 5530 cases and 8318
controls. Children who had a BMI > 95th percentile were considered to
be obese cases while children of BMI < 50th percentile were considered
to be controls^[157]37. The GWAS meta-analysis of birth weight included
neonates of European ancestry (BW[EU], n = 298,142) and transethnic
(African, South Asian, European) meta-analysis (BW[TR,] n = 321,223).
Individuals who were part of multiple births, who reported their birth
weight in multiple visits with the mean difference in their birth
weight being > 1 kg and individuals whose birth weight was < 2.5 kg
or > 4.5 kg were excluded^[158]36. The results from the GWAS of own
birth weight (as opposed to offspring birth weight), and without
adjustment for maternal genotype, were used in this analysis.
Functional annotation data
We used 4 annotation databases CADD^[159]42, eQTL^[160]43, TFBS^[161]44
and DHSs^[162]45 to functionally annotate the SNPs. Under CADD
framework, implemented in CADD v1.2
([163]http://www.cadd.gs.washington.edu)^[164]42, a deleteriousness
score (combined SVM score; c-score) is generated using the integrated
functional and evolutionary importance of each variant from 63
annotation sources. Phred-like scores (ranged 1–99) are further
generated based on the rank of each variant relative to 8.6 billion
substitutions in human reference genome (-10*log[10][rank/total]). Each
variant in our analysis receiving a score ≥ 15 was assigned an
annotation of 1 (deleterious) while scores < 15 were assigned an
annotation of 0 (non-deleterious)^[165]42. In addition to CADD, which
comprises a composite score, we used annotations from a variety of
tissues and cell lines to elucidate the regulatory mechanisms of the
risk variants. The eQTL annotations was obtained from dbGaP accession
number phs000424.vN.pN on 02/11/20
([166]https://gtexportal.org/home/datasets)^[167]43 which consisted of
cis-eQTL files on 49 different tissues. We took the intersection of
these eQTL with the common variants of BW and COB/CBMI and the variants
that overlapped were annotated as 1 and others as 0. The TFBS
annotation
([168]ftp://ccg.epfl.ch/snp2tfbs/mapped_files/annotated/)^[169]44 had
data on 195 different transcription factors. Similar to eQTL, we took
the intersection of the TFBS to annotate the common variants of birth
weight and COB/CBMI. The DHSs annotation data, which has also been used
in other studies^[170]45,[171]93, was downloaded from
([172]https://github.com/joepickrell/1000-genomes)^[173]45 and
comprised of 402 binary annotations that included maps of DNase-I
hypersensitive sites from different primary tissues, cell lines and
cell types^[174]94,[175]95.
Statistical Analysis
We used a unified statistical approach that integrates summary
statistics with functional annotations for paired traits using
probabilistic models implemented in genetic analysis incorporating
pleiotropy and annotation (GPA)^[176]41. For convenience, we briefly
introduce the GPA model and its notations below.
Suppose the P values from two GWAS have been collected in an M × 2
matrix, p = [p[jk]], where p[jk] denotes the P value of the jth SNP in
the kth GWAS, k = 1,2 (in our case) and M is the number of SNPs. In the
GPA model, these P values are assumed to come from a mixture of null
(un-associated) and non-null (associated), with probability π[0] and
π[1] = 1 − π[0], respectively. GPA uses the Uniform distribution on
[0,1] and the Beta distribution with parameters (α,1) to model the P
values from the null and non-null groups, respectively.
Let Zj ∈ {00,10,01,11} indicate the association between the jth SNP and
the two traits: Zj = 00 means the jth SNP is associated with neither of
them, Zj = 10 means it is only associated with the first trait, Zj = 01
means it is only associated with the second trait, and Zj = 11 means it
is associated with both trait. Thus, the four-group model is
represented as:
[MATH: π00=PrZj=00:pj1∼U0,1,pj2∼U0,1,if Zj=10π10=PrZj=10:pj1∼Betaα1,1,pj2∼U0,1,if Zj=00π01=PrZj=01:pj1∼U0,1,pj2∼Betaα2,1,if
Zj=01π11=PrZj=11:pj1∼Betaα1,1,pj2∼Betaα2,1,if
Zj=11 :MATH]
where p[j1] and p[j2] is the P value of the jth SNP in GWAS 1 and 2.
GPA further incorporates functional annotation as follows. Let an
M-dimensional vector A collect functional information from an
annotation source, where Aj ∈ {0,1} indicates whether the jth SNP is a
functional unit according to the annotation source. For example, given
an eQTL data, if the jth SNP is an eQTL, then Aj = 1, otherwise Aj = 0.
The relationship between Zj and Aj is described as:
[MATH: q00=<
/mo>PrAj=1|Zj=00,q10=PrAj=1|Zj=10,q01=PrAj=1|Zj=01,q1
1=PrAj=1|Zj=11, :MATH]
where q[00] is the probability of a null SNP being annotated, q[10] is
the probability of the first trait-associated SNP being annotated,
q[01]is the probability of the second trait-associated SNP being
annotated, and q[11] is the probability of jointly associated SNP being
annotated. GPA then implements an efficient EM-algorithm to obtain the
estimates of the model parameters: {π[00], π[10], π[01], π[11], q[00],
q[10], q[01], q[11], α}.
To assess the significance of enrichment for pleiotropy between two
traits it uses the likelihood ratio test (LRT) with H[0]:
π[11] = (π[10] + π[11])( π[01] + π[11]), versus H[1]: not H0.
Similarly, to assess the significance of enrichment for annotation it
uses LRT with H[0]: q[00] = q[11], versus H1: q[00] ≠ q[11]. GPA also
calculates the standard errors for the model parameters along with
their covariance matrix based on an empirically observed information
matrix.
After estimating the parameters, GPA assigns each SNP four posterior
probabilities (PP) (estimated values of {π[00], π[10], π[01], π[11]})
and controls for false discovery based on the local false discovery
rate (FDR). The local FDR is defined as the probability that the jth
SNP is either not associated with any trait (Fdr[0]) or is associated
with the first trait (Fdr[1]), second trait (Fdr[2]) or both trait
(Fdr[1,2]) given its P value and annotation information.
[MATH: Fdr0pj1,
pj2,A=PrZj00=1|pj1,pj2,A
Fdr1pj1,
pj2,A=PrZj00+Zj01=1|pj1,pj2,AFdr2pj1,
pj2,A=PrZj00+Zj10=1|pj1,pj2,AFdr1,2pj1,
pj2,A=PrZj00+Zj01+Zj10=1|pj1,pj2,A :MATH]
Finally, to infer associations at the variant level and to control the
FDR at 5% we then select those SNPs with any of the four PP > 95% and
FDR < 0.05. From the selected set, SNPs that achieve PP > 95% under the
PP categories of {π[00], π[10], π[01], π[11]} are concluded to be
associated with neither, first, second or both traits. In our study, we
conducted all the tests after controlling for FDR at 0.05 level and
used 10,000 EM iterations.
Pathway analysis
Pathway analysis elucidates the underlying biological processes in
which genes are related by common functionality. To detect such
relationships of the identified pleiotropic genetic loci in our study,
we used QIAGEN’s IPA tool ([177]https://digitalinsights.qiagen.com/).
IPA is a web-based software application that allows analysis of data
obtained from several sequencing platforms. IPA enables for targeted
search of information on genes, proteins, chemicals, diseases, and
drugs. IPA’s data analysis provides clarity in understanding the
significance of data or targets of interest in relation to larger
biological systems. Statistical significance of overrepresented
canonical pathways was determined using Fisher’s exact test after
adjustment for multiple testing using the Benjamini–Hochberg method.
Statistical significance was based on P < 0.05 in pathways with at
least two molecules.
Colocalization analysis
To evaluated whether birthweight and childhood obesity traits share
common genetic causal variant(s) in a region, we performed
colocalization analysis for each of the 19 pleiotropic loci using the
European ancestry GWAS summary statistics. For each genetic locus, the
lead SNP with the highest posterior probability of joint association
with birthweight and childhood obesity traits as well as SNPs within
500 kb window on either side of the lead SNP defined a colocalization
region. Analysis was performed using the coloc R package^[178]47.
Ethical approval
The NIH Office of Human Subjects Research Program granted the study an
exemption from IRB review (OHSRP ID Number: 18-NICHD-00412) per 45 CFR
46 and NIH policy for the use of specimens/data.
Supplementary Information
[179]Supplementary Information.^ (4.4MB, docx)
Author contributions
F.T.-A. and S.C. conceived and designed this study; S.C. gathered and
managed data; S.C. analyzed the data; S.C. wrote the draft paper;
F.T.-A. supervised the work and interpreted the results. M.O. provided
critical intellectual content. All authors approved the final
manuscript.
Funding
This research was supported by the Intramural Research Program of the
Eunice Kennedy Shriver National Institute of Child Health and Human
Development, National Institutes of Health. Additional support was
obtained from the National Institute on Minority Health and Health
Disparities and the National Institute of Diabetes and Digestive and
Kidney Diseases. This work utilized the computational resources of the
NIH HPC Biowulf cluster ([180]http://hpc.nih.gov).
Data availability
The data analyzed in this study are available online. Table [181]S1
lists the URL of the data sources.
Competing interests
The authors declare no competing interests.
Footnotes
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Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-020-80084-9.
References