Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE Study.

Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p<1.7x10-4 ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p=1.9x10-7 ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. This article is protected by copyright. All rights reserved.

A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications.

The problem of the genetics of related phenotypes is often addressed by analyzing adjusted-model traits, but such traits warrant cautious interpretation. Here, we adopt a joint view of adiposity traits in ~322,154 subjects (GIANT consortium). We classify 159 signals associated with body mass index (BMI), waist-to-hip ratio (WHR), or WHR adjusted for BMI (WHRadjBMI) at P < 5 × 10-8, into four classes based on the direction of their effects on BMI and WHR. Our classes help differentiate adiposity genetics with respect to anthropometry, fat depots, and metabolic health. Class-specific Mendelian randomization reveals that variants associated with both WHR-decrease and BMI increase are linked to metabolically rather favorable adiposity through beneficial hip fat. Class-specific enrichment analyses implicate digestive systems as a pathway in adiposity genetics. Our results demonstrate that WHRadjBMI variants capture relevant effects of "unexpected fat distribution given the BMI" and that a joint view of the genetics underlying related phenotypes can inform on important biology.

The genetics of adiposity.

Genome-wide discovery efforts have identified more than 500 genetic loci associated with adiposity traits. The vast majority of these loci were found through large-scale meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), and in European ancestry populations. However, alternative approaches, focusing on non-European ancestry populations, more refined adiposity measures, and low-frequency (minor allele frequency (MAF)<5%) coding variants, identified additional novel loci that had not been identified before. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with fat distribution point to pathways involved in adipocyte biology. Pinpointing the causal gene within each locus remains challenging, but is a critical step towards translation of genome-wide association study (GWAS) loci into new biology. Ultimately, new genes may provide pharmacological targets for the development of weight loss drugs.

Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system.

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic BioMe biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

Blood pressure in young adulthood and residential greenness in the early-life environment of twins.

BACKGROUND: Previous research shows that, besides risk factors in adult life, the early-life environment can influence blood pressure and hypertension in adults. However, the effects of residential traffic exposure and residential greenness in the early-life on blood pressure in young adulthood are currently unknown. METHODS: Ambulatory (24-h) blood pressures of 278 twins (132 pairs) of the East Flanders Prospective Twins Study were obtained at the age of 18 to 25 years. Prenatal and adulthood residential addresses were geocoded and used to assign prenatal and postnatal traffic and greenness indicators. Mixed modelling was performed to investigate blood pressure in association with greenness while adjusting for potential confounding factors. RESULTS: Night-time systolic blood pressure was inversely associated with greenness at the residential address in twins living at the same address their entire life (non-movers, n = 97, 34.9%). An interquartile increase in residential greenness exposure (1000 m radius) was associated with a 3.59 mmHg (95% CI: -6.0 to -1.23; p = 0.005) lower adult night systolic blood pressure. Among twins who were living at a different address than their birth address at time of the measurement (n = 181, 65.1%), night-time blood pressure was inversely associated with residential surrounding greenness at adult age as well as with residential greenness in early-life. However after additional adjustment for residential greenness exposure in adulthood, only residential greenness exposure in early-life was significantly associated with night systolic blood pressure. While no significant effect of adult residential greenness with adult blood pressure was observed, while accounting for the early-life greenness exposure. CONCLUSIONS: Lower residential greenness in the early-life environment was independently associated with a higher adult blood pressure. This indicates that residential greenness has persistent effects on blood pressure.

Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) study.

BACKGROUND: Both genetic and environmental factors are known to affect body mass index (BMI), but detailed understanding of how their effects differ during childhood and adolescence is lacking. OBJECTIVES: We analyzed the genetic and environmental contributions to BMI variation from infancy to early adulthood and the ways they differ by sex and geographic regions representing high (North America and Australia), moderate (Europe), and low levels (East Asia) of obesogenic environments. DESIGN: Data were available for 87,782 complete twin pairs from 0.5 to 19.5 y of age from 45 cohorts. Analyses were based on 383,092 BMI measurements. Variation in BMI was decomposed into genetic and environmental components through genetic structural equation modeling. RESULTS: The variance of BMI increased from 5 y of age along with increasing mean BMI. The proportion of BMI variation explained by additive genetic factors was lowest at 4 y of age in boys (a(2) = 0.42) and girls (a(2) = 0.41) and then generally increased to 0.75 in both sexes at 19 y of age. This was because of a stronger influence of environmental factors shared by co-twins in midchildhood. After 15 y of age, the effect of shared environment was not observed. The sex-specific expression of genetic factors was seen in infancy but was most prominent at 13 y of age and older. The variance of BMI was highest in North America and Australia and lowest in East Asia, but the relative proportion of genetic variation to total variation remained roughly similar across different regions. CONCLUSIONS: Environmental factors shared by co-twins affect BMI in childhood, but little evidence for their contribution was found in late adolescence. Our results suggest that genetic factors play a major role in the variation of BMI in adolescence among populations of different ethnicities exposed to different environmental factors related to obesity.

Obesity genomics: assessing the transferability of susceptibility loci across diverse populations.

The prevalence of obesity has nearly doubled worldwide over the past three decades, but substantial differences exist between nations. Although these differences are partly due to the degree of westernization, genetic factors also contribute. To date, little is known about whether the same genes contribute to obesity-susceptibility in populations of different ancestry. We review the transferability of obesity-susceptibility loci (identified by genome-wide association studies) using both single nucleotide polymorphism (SNP) and locus-wide comparisons. SNPs in FTO and near MC4R, obesity-susceptibility loci first identified in Europeans, replicate widely across other ancestries. SNP-to-SNP comparisons suggest that more than half of the 36 body mass index-associated loci are shared across European and East Asian ancestry populations, whereas locus-wide analyses suggest that the transferability might be even more extensive. Furthermore, by taking advantage of differences in haplotype structure, populations of different ancestries can help to narrow down loci, thereby pinpointing causal genes for functional follow-up. Larger-scale genetic association studies in ancestrally diverse populations will be needed for in-depth and locus-wide analyses aimed at determining, with greater confidence, the transferability of loci and allowing fine-mapping. Understanding similarities and differences in genetic susceptibility across populations of diverse ancestries might eventually contribute to a more targeted prevention and customized treatment of obesity.

Influence of dietary protein intake and glycemic index on the association between TCF7L2 HapA and weight gain.

BACKGROUND: Genetic polymorphisms of transcription factor 7-like 2 (TCF7L2) have been associated with type 2 diabetes and BMI. OBJECTIVE: The objective was to investigate whether TCF7L2 HapA is associated with weight development and whether such an association is modulated by protein intake or by the glycemic index (GI). DESIGN: The investigation was based on prospective data from 5 cohort studies nested within the European Prospective Investigation into Cancer and Nutrition. Weight change was followed up for a mean (±SD) of 6.8 ± 2.5 y. TCF7L2 rs7903146 and rs10885406 were successfully genotyped in 11,069 individuals and used to derive HapA. Multiple logistic and linear regression analysis was applied to test for the main effect of HapA and its interaction with dietary protein or GI. Analyses from the cohorts were combined by random-effects meta-analysis. RESULTS: HapA was associated neither with baseline BMI (0.03 ± 0.07 BMI units per allele; P = 0.6) nor with annual weight change (8.8 ± 11.7 g/y per allele; P = 0.5). However, a previously shown positive association between intake of protein, particularly of animal origin, and subsequent weight change in this population proved to be attenuated by TCF7L2 HapA (P-interaction = 0.01). We showed that weight gain becomes independent of protein intake with an increasing number of HapA alleles. Substitution of protein with either fat or carbohydrates showed the same effects. No interaction with GI was observed. CONCLUSION: TCF7L2 HapA attenuates the positive association between animal protein intake and long-term body weight change in middle-aged Europeans but does not interact with the GI of the diet.

Common variants in or near FGF5, CYP17A1 and MTHFR genes are associated with blood pressure and hypertension in Chinese Hans.

OBJECTIVES: Recent genome-wide association studies have identified a number of variants influencing blood pressure. We aimed to examine whether these associations can be replicated in Chinese. METHODS: We genotyped eight of these variants (in or near FGF5, CYP17A1, MTHFR, ZNF652, PLCD3, ATP2B1, c10orf107) in a population-based cohort of Chinese Hans (N = 3210). Logistics regression and generalized linear analyses were applied to test for association of each variant with hypertension risk and blood pressure (BP), BMI, waistline and high-sensitivity C-reactive protein (hsCRP), respectively. RESULTS: Six variants showed directionally consistent association with blood pressure and risk of hypertension, of which four (FGF5, two in CYP17A1, MTHFR) reached significance. The associations were most pronounced for FGF5-rs16998073 [SBP: ß = 1.97 mmHg/allele, P = 0.0006; DBP: ß = 0.95 mmHg/allele, P = 0.0008, hypertension: odds ratio (OR) 1.36/allele, P = 0.0001]. Effect size of FGF5-rs16998073 on SBP and hypertension were significantly more pronounced in Han Chinese compared to white Europeans. None of these variants was associated with BMI, waistline or hsCRP that are the well established risk factors for hypertension. The genetic risk score, calculated as the sum of BP-increasing alleles of FGF5-rs16998073, CYP17A1-rs11191548, CYP17A1-rs1004467 and MTHFR-rs17367504, was significantly associated with increased SBP (1.16 mmHg/allele, P = 9.01E-5), DBP (0.51 mmHg/allele, P = 4.40E-4) and hypertension risk (OR = 1.22/allele, P = 2.74E-7). CONCLUSION: Variants in or near FGF5, CYP17A1 and MTHFR contributed to variation in BP and hypertension risk. Effect sizes of these three loci tended to be larger in Chinese than in white Europeans, but more studies with larger sample size are required for a definitive conclusion.

A multilevel linear mixed model of the association between candidate genes and weight and body mass index using the Framingham longitudinal family data.

Obesity has become an epidemic in many countries and is one of the major risk conditions for disease including type 2 diabetes, coronary heart disease, stroke, dyslipidemia, and hypertension. Recent genome-wide association studies have identified two genes (FTO and near MC4R) that were unequivocally associated with body mass index (BMI) and obesity. For the Genetic Analysis Workshop 16, data from the Framingham Heart Study were made available, including longitudinal anthropometric and metabolic traits for 7130 Caucasian individuals over three generations, each with follow-up data at up to four time points. We explored the associations between four single-nucleotide polymorphisms (SNPs) on FTO (rs1121980, rs9939609) or near MC4R (rs17782313, rs17700633) with weight and BMI under an additive model. We applied multilevel linear mixed model for continuous outcomes, using the Affymetrix 500k genome-wide genotype data for the four SNPs. The results of the multilevel modeling in the entire sample indicated that the minor alleles of the four SNPs were associated with higher weight and higher BMI. The most significant associations were between rs9939609 and weight (p = 4.7 x 10-6) and BMI (p = 8.9 x 10-8). The results also showed that, for SNPs at FTO, the homozygotes for the minor allele had the most pronounced increase in weight and BMI, while the common allele homozygotes gained less weight and BMI during the follow-up period. Linkage disequilibrium (LD) between the two FTO SNPs was strong (D' = 0.997, r2 = 0.875) but their haplotype was not significantly associated with either weight or BMI. The two SNPs near MC4R were in weak LD (D' = 0.487, r2 = 0.166).

Obesity genes identified in genome-wide association studies are associated with adiposity measures and potentially with nutrient-specific food preference.

BACKGROUND: New genetic loci, most of which are expressed in the brain, have recently been reported to contribute to the development of obesity. The brain, especially the hypothalamus, is strongly involved in regulating weight and food intake. OBJECTIVES: We investigated whether the recently reported obesity loci are associated with measures of abdominal adiposity and whether these variants affect dietary energy or macronutrient intake. DESIGN: We studied 1700 female Dutch participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Their anthropometric measurements and intake of macronutrients were available. Genotyping was performed by using KASPar chemistry. A linear regression model, with an assumption of an additive effect, was used to analyze the association between genotypes of 12 single nucleotide polymorphisms (SNPs) and adiposity measures and dietary intake. RESULTS: Seven SNPs were associated (P < 0.05) with weight, body mass index (BMI), and waist circumference (unadjusted for BMI). They were in or near to 6 loci: FTO, MC4R, KCTD15, MTCH2, NEGR1, and BDNF. Five SNPs were associated with dietary intake (P < 0.05) and were in or near 5 loci: SH2B1 (particularly with increased fat), KCTD15 (particularly with carbohydrate intake), MTCH2, NEGR1, and BDNF. CONCLUSIONS: We confirmed some of the findings for the newly identified obesity loci that are associated with general adiposity in a healthy Dutch female population. Our results suggest that these loci are not specifically associated with abdominal adiposity but more generally with obesity. We also found that some of the SNPs were associated with macronutrient-specific food intake.