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Title: Genetic regulation of the placental transcriptome underlies birth weight and risk of childhood obesity}
Article:
Abstract:
GWAS identified variants associated with birth weight( BW), childhood obesity( CO) and childhood BMI( CBMI), and placenta is a critical organ for fetal development and postnatal health. We examined the role of placental transcriptome and eQTLs in mediating the genetic causes for BW, CO and CBMI, and applied integrative analysis( Colocalization and MetaXcan). GWAS loci associated with BW, CO, and CBMI were substantially enriched for placenta eQTLs( 6. 76, 4. 83 and 2. 26 folds, respectively). Importantly, compared to eQTLs of adult tissues, only placental eQTLs contribute significantly to both anthropometry outcomes at birth( BW) and childhood phenotypes( CO/CBMI). Eight, six and one transcripts colocalized with BW, CO and CBMI risk loci, respectively. Our study reveals that placental transcription in utero likely plays a key role in determining postnatal body size, and as such may hold new possibilities for therapeutic interventions to prevent childhood obesity.
Introduction:
Birth weight( BW) is influenced by both fetal and maternal factors, including race, infant sex, plurality, altitude, education, and smoking[1], and is consistently associated with future risk of adult metabolic diseases including type 2 diabetes( T2D) and cardiovascular disease[2]. Childhood obesity( CO) has also emerged as an important health problem in the United States and most other countries in the world[3, 4]. In the USA, 1 in 3 children is afflicted with overweight or obesity[3]. The increasing prevalence of CO is also associated with diseases later in life including T2D, hypertension, nonalcoholic fatty liver disease, obstructive sleep apnea, and dyslipidemia[3]. The placenta, situated at the maternal-fetal interface, is a key organ for fetal growth and development; it performs a variety of functions including controlling fetal access to nutrients, hormone production, and mitigation of adverse effects from the environment[2, 5, 6]. The placenta and associated extraembryonic membranes are of fetal organ( i. e, have the same genetic composition as the fetus), formed from the zygote at the start of each pregnancy[7]. Several studies, including our own, have reported associations linking placenta markers to variation in birth weight[8, 9]. Recently, we leveraged the Rhode Island Child Health Study( RICHS)[6, 10] resource and reported the first placenta expression quantitative trait loci( eQTL) profile based on a large sample size[11]. We discovered more than 3, 000 cis- and trans-eQTLs at ≤10% false discovery rate( FDR), demonstrating that the placenta is a transcriptionally active tissue and that this activity is largely controlled by fetal genetics[11]. Large meta-analysis of BW[12], CO[13] and childhood BMI( CBMI)[14] genome-wide association studies( GWAS) have identified multiple genome-wide significant loci suggesting a strong genetic determinant for these conditions. At genome-wide significance, 60 and 15 fetal loci have been associated with BW( explaining 15% of its variance)[12] and CBMI[14], respectively. As many of these genome-wide significant variants are in non-coding regions, they likely affect these phenotypes through regulation of gene expression. Furthermore, while genetic variants are static within an individual, the regulatory effect that a genetic polymorphism exerts on outcomes will be likely to vary spatially, temporally and in response to differing environmental exposures and influences. As a noted limitation of the GWAS design, while informative for identifying genetic loci implicated in disease, this approach does not provide information on down-stream genes and their pathophysiological functions. More specifically, the GWAS design is unable to identify;( 1) the tissue types and developmental stages where the causal variants and the underlying genes have their effects and( 2) the mechanisms of effect of a causal variant. An eQTL analysis provides a profile of significant associations between transcription levels of a gene and genetic polymorphisms. A related concept, eSNP, denotes a particular SNP whose genotype is significantly associated with the transcription level of a gene. A distinction is made as to the location of the eQTL relative to the physical location of the gene whose expression level it is associated with; if the eQTL is close( within 500kb) to the gene( which encodes the mRNA), it is defined as a cis-eQTL. In contrast, if the eQTL is distant( typically more than 500kb away or on another chromosome altogether) from the gene whose expression level it is associated with, it is defined as a trans-eQTL. In this study, the cis- and trans-eQTLs in placenta and adult tissues were defined using these criteria, as previously reported[11]. An important approach is to apply knowledge of eQTLs in disease-relevant tissues to mine GWAS risk loci datasets to discover underlying mechanisms linking genetic variants to diseases[15, 16]. Recently, using integrative genomics applied to eQTLs has become an even more powerful tool to mine GWAS results and shed light on disease pathways, as it can identify true disease causal variants( as opposed to merely undirected associations) with down-stream genes in a tissue-specific manner. Recently, methods like COLOC[17], SMR[18], MetaXcan[19], and TWAS[20] have shown superior ability to identify causal genes of genetic risk loci compared to earlier integrative analysis simply looking at overlap between eQTLs and GWAS lead SNPs. Herein, to study the in utero genes/pathways influencing BW, CO and CBMI, and to define the specific role of the placenta for neonatal and long-term child health outcomes, we compared placenta eQTLs with 7 STARNET adult tissue eQTLs( blood, atherosclerotic-lesion-free internal mammary artery, atherosclerotic aortic root, subcutaneous fat, visceral abdominal fat, skeletal muscle, and liver). Further, we applied COLOC and MetaXcan analyses on placenta eQTLs integrated with GWAS data.
Discussion:
There is increasing recognition that the period of intrauterine development constitutes one of the most critical periods for defining disease risk later in life[26]. We report the first study to use comprehensive transcriptome data from the placenta( being one of the most relevant tissues in controlling in utero development) to interpret the causal effects of GWAS genetic risk loci for BW, CO and CBMI. We found extensive enrichment of placenta eQTLs for GWAS hits( Fig 1), and importantly, discovered hundreds of transcripts controlled by the genetic variants that influence these anthropometric traits. Our findings demonstrate that placental transcription and related functions defined by identified trait genes play important roles in determining BW, CO and CBMI BW, CO and CBMI are of major clinical and public health importance. BW has been convincingly shown to be inversely associated with risk for T2D, CAD and hypertension in later life[12]. CO or excess BMI are risk factors for T2D and hypertension[3]. These traits are also heritable, especially CO( h2 = 0. 4067). Furthermore, these traits are genetically correlated( Table 3), where higher BW is associated with increased risk of CO. Importantly, we demonstrate that the placenta is a highly relevant tissue for determining not only weight at birth( BW) but also body weight in childhood( CO and CBMI). Identifying the genes mediating the genetic predisposition for BW, CO and CBMI is of great importance for understanding the mechanisms controlling these traits and developing clinical prevention strategies. Integrative genomics can be a powerful tool to mine GWAS results. Many methods have been proposed to integrate GWAS summary data and eQTL data, where GWAS and eQTL studies used different datasets or population. These methods include SMR, PrediXcan, Sherlock, COLOC, eCAVIAR, etc, and they can be categorized into two broad classes[27].( 1) Class 1 includes TWAS, MetaXcan and SMR, which are tests for significant genetic correlation between cis expression and GWAS.( 2) Class 2 includes COLOC and eCAVIAR, which are estimations( rather than tests) of the posterior probability of colocalization, where colocalization is defined as shared causal variant( s) between the expression and GWAS. It has been reported that the results of these two classes of methods do not fully overlap[28]. Possible explanations are different assumption, algorithm, power, etc. Herein, we took a common strategy[28] and report all gene pinpoint by COLOC and/or MetaXcan, as they are potentially on the causal pathway of BW, CO and CBMI. In S3 Table, we report the LD between the lead eQTL and the lead GWAS SNP near the gene locus. There are six genes coded by a single BW GWAS locus with lead SNP rs2421016( chr10:124167512)( Fig 2A), and we found that four out of the six genes were co-localized with BW( Table 1): PLEKHA1( Pleckstrin Homology Domain Containing A1), HTRA1( HtrA Serine Peptidase 1), ARMS2( Age-Related Maculopathy Susceptibility 2), and BTBD16( BTB Domain Containing 16). The eQTLs of these four genes also overlap with BW GWAS peaks( Fig 2B–2E). In particular, the shape of the PLEKHA1 eQTL overlaps well with the BW GWAS peak( Fig 2A and 2B), where the top eSNPs include the GWAS lead SNP( purple dots) and the SNPs in strong LD with the lead SNPs( red and orange dots). In COLOC analysis, PLEKHA1 showed PP. H4 much larger than PP. H3, indicating BW and PLEKHA1 expression levels share a single causal SNP. For HTRA1, ARMS2 and BTBD16 genes, the GWAS lead SNP and its high LD proxy SNPs( purple, red and orange dots) only showed association with transcription at moderate significance, while the top eSNPs are SNPs with moderate LD with the GWAS lead SNP( green and light blue dots), suggesting multiple SNPs influence HTRA1, ARMS2 and BTBD16 expression levels and some of these SNPs also impact BW. Of relevance, a study in a murine model found Htra1 is associated with placenta development[29]. In humans, upregulation of ARMS2 was observed in placenta of growth-restricted infants[29], and perturbations in ARMS2 may result in dysfunction of the extracellular matrix; suggesting that up-regulation of ARMS2 forms part of an important survival mechanism to compensate for placental growth discordance[29]. Furthermore, various GWAS have shown this locus( 10q26. 13) is associated with multiple diseases/traits, including age-related macular degeneration[30], type 2 diabetes[31, 32], migraine[33] and height[34], suggesting that the versatile functions of these genes may affect many biological processes. In this paper, we report BW, CO, and CBMI GWAS signals are more profoundly enriched in placenta eQTLs comparing to adult tissue eQTLs( ie. STARNET). The differences are not driven by a few loci but rather polygenic. After strict LD pruning( Materials and methods), which eliminated the eSNPs that are in high LD, the conclusions remain unchanged that GWAS signals are more profoundly enriched in eQTLs of placenta than adult tissues( S1 Fig). Two-sample K-S test( on pruned SNP lists) showed such difference in enrichment magnitude were statistically significant( S4 Table). Such results suggest placenta could be a relevant tissue type controlling these neonatal traits. As a future direction, it would be important to investigate whether eQTLs of infant or pediatric tissues( e. g. skeleton muscle) substantially enrich for BW, CO, CBMI GWAS loci and compare to the results of placenta. The developmental origins of health and disease( DOHaD) hypothesis emphasizes the role of the in utero environment on shaping the developmental trajectory of the fetus and, thereby, subsequent health outcomes across the lifespan. Our findings showcasing the influence of genetically controlled in utero expression patterns linked to BW, CO and CBMI also support this hypothesis. One of our key observations is that placental eQTLs are enriched to a greater extent for CO/CBMI GWAS hits than eQTLs from other tissues, including adipose and liver( Fig 1B and 1C). This observation strongly suggests that the genomic control and transcription activity of these genes in the placenta has a profound and long lasting impact on postnatal development and human health. In addition, the transcripts potentially mediating the genetic control of BW and CO can be influenced by environmental factors, and our results shed light on environmental health and nutritional elements that are likely involved in the childhood obesity epidemic[4]. Moreover, our analysis also revealed the genes/transcripts likely to mediate the effects of genetic polymorphisms on BW and CO/CBMI, as well as the directionality of the mediation effect; thus providing prioritized targets for experimental follow-up, intervention and prevention in the fight against the current obesity epidemic.
Genetic studies ( e . g GWAS ) revealed substantial heritability on birth weight ( BW ) , childhood obesity ( CO ) and childhood body mass index ( CBMI ) , however , the etiological mechanisms and relevant tissue ( s ) underlying these traits/conditions are not clear . We incorporated the data from largest GWASes to date and placenta expressional quantitative trait loci ( eQTL ) that have been newly published , and showed the variants associated with BW , CO and CBMI were substantially enriched for placenta eQTLs ( 6 . 76 , 4 . 83 and 2 . 26 folds , respectively ) . Importantly , compared to eQTLs in 7 adult tissues such as adipose and liver , only eQTLs in the placenta were found to contribute significantly not only to anthropometry outcomes at birth ( BW ) but also to childhood phenotypes ( CO/CBMI ) . Further , we employed COLOC and MetaXcan analyses and identified placenta transcripts potential mediate the genetic effect of BW/CO/CBMI GWAS loci . In summary , our study strongly supports a key role for the placenta in determining BW , CO and CMBI at the molecular level , and pinpointed genes whose expression levels in placenta potentially influences BW and CO risk .