Lymphocyte activation gene-3-associated protein networks are associated with HDL-cholesterol and mortality in the Trans-omics for Precision Medicine program.

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A, and trans interactions between LAG3 and B2M. A LAG3-associated protein network has significant associations with HDL-C and mortality.

Presence and transmission of mitochondrial heteroplasmic mutations in human populations of European and African ancestry.

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in 6745 maternal pairs of European (EA, n = 4718 pairs) and African (AA, n = 2027 pairs) Americans in whole blood. Mother-offspring pairs displayed the highest concordance rate, followed by sibling-sibling and more distantly-related maternal pairs. The allele fractions of concordant heteroplasmies exhibited high correlation (R2 = 0.8) between paired individuals. Discordant heteroplasmies were more likely to be in coding regions, be nonsynonymous or nonsynonymous-deleterious (p < 0.001). The number of deleterious heteroplasmies was significantly correlated with advancing age (20-44, 45-64, and ≥65 years, p-trend = 0.01). One standard deviation increase in heteroplasmic burden (i.e., the number of heteroplasmies carried by an individual) was associated with 0.17 to 0.26 (p < 1e - 23) standard deviation decrease in mtDNA copy number, independent of age. White blood cell count and differential count jointly explained 0.5% to 1.3% (p ≤ 0.001) variance in heteroplasmic burden. A genome-wide association and meta-analysis identified a region at 11p11.12 (top signal rs779031139, p = 2.0e - 18, minor allele frequency = 0.38) associated with the heteroplasmic burden. However, the 11p11.12 region is adjacent to a nuclear mitochondrial DNA (NUMT) corresponding to a 542 bp area of the D-loop. This region was no longer significant after excluding heteroplasmies within the 542 bp from the heteroplasmic burden. The discovery that blood mtDNA heteroplasmies were both inherited and somatic origins and that an increase in heteroplasmic burden was strongly associated with a decrease in average number of mtDNA copy number in blood are important findings to be considered in association studies of mtDNA with disease traits.

Searching for parent-of-origin effects on cardiometabolic traits in imprinted genomic regions.

Cardiometabolic traits pose a major global public health burden. Large-scale genome-wide association studies (GWAS) have identified multiple loci accounting for up to 30% of the genetic variance in complex traits such as cardiometabolic traits. However, the contribution of parent-of-origin effects (POEs) to complex traits has been largely ignored in GWAS. Family-based studies enable the assessment of POEs in genetic association analyses. We investigated POEs on a range of complex traits in 3 family-based studies. The discovery phase was carried out in large pedigrees from the Kibbutzim Family Study (n = 901 individuals) and in 872 parent-offspring trios from the Jerusalem Perinatal Study. Focusing on imprinted genomic regions, we examined parent-specific associations with 12 complex traits (i.e., body-size, blood pressure, lipids), mostly cardiometabolic risk traits. Forty five of the 11,967 SNPs initially found to have POE were evaluated for replication (p value < 1 × 10-4) in Framingham Heart Study families (max n = 8000 individuals). Three common variants yielded evidence of POE in the meta-analysis. Two variants, located on chr6 in the HLA region, showed a paternal effect on height (rs1042136: ßpaternal = -0.023, p value = 1.5 × 10-8 and rs1431403: ßpaternal = -0.011, p value = 5.4 × 10-6). The corresponding maternally-derived effects were statistically nonsignificant. The variant rs9332053, located on chr13 in RCBTB2 gene, demonstrated a maternal effect on hip circumference (ßmaternal = -4.24, p value = 9.6 × 10-6; ßpaternal = 1.29, p value = 0.23). These findings provide evidence for the utility of incorporating POEs into association studies of cardiometabolic traits, especially anthropometric traits. The study highlights the benefits of using family-based data for deciphering the genetic architecture of complex traits.

Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study.

BACKGROUND: Increases in circulating LDL cholesterol (LDL-C) and high-sensitivity C-reactive protein (hsCRP) concentrations are significant risk factors for cardiovascular disease (CVD). We assessed direct LDL-C and hsCRP concentrations compared to standard risk factors in the Framingham Offspring Study. METHODS: We used stored frozen plasma samples (-80 °C) obtained after an overnight fast from 3147 male and female participants (mean age, 58 years) free of CVD at cycle 6 of the Framingham Offspring Study. Overall, 677 participants (21.5%) had a CVD end point over a median of 16.0 years of follow-up. Total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), direct LDL-C (Denka Seiken and Kyowa Medex methods), and hsCRP (Dade Behring method) concentrations were measured by automated analysis. LDL-C was also calculated by both the Friedewald and Martin methods. RESULTS: Considering all CVD outcomes on univariate analysis, significant factors included standard risk factors (age, hypertension, HDL-C, hypertension treatment, sex, diabetes, smoking, and TC concentration) and nonstandard risk factors (non-HDL-C, direct LDL-C and calculated LDL-C, TG, and hsCRP concentrations). On multivariate analysis, only the Denka Seiken direct LDL-C and the Dade Behring hsCRP were still significant on Cox regression analysis and improved the net risk reclassification index, but with modest effects. Discordance analysis confirmed the benefit of the Denka Seiken direct LDL-C method for prospective hard CVD endpoints (new-onset myocardial infarction, stroke, and/or CVD death). CONCLUSIONS: Our data indicate that the Denka Seiken direct LDL-C and Dade Behring hsCRP measurements add significant, but modest, information about CVD risk, compared to standard risk factors and/or calculated LDL-C.

Network analysis of drug effect on triglyceride-associated DNA methylation.

BACKGROUND: DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites. METHODS: We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules. RESULTS: Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways (p values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results. CONCLUSIONS: The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglyceride-associated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites.

Targeted sequencing of genome wide significant loci associated with bone mineral density (BMD) reveals significant novel and rare variants: the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) targeted sequencing study.

Background: Bone mineral density (BMD) is a heritable phenotype that predicts fracture risk. We performed fine-mapping by targeted sequencing at WLS, MEF2C, ARHGAP1/F2 and JAG1 loci prioritized by eQTL and bioinformatic approaches among 56 BMD loci from our previous GWAS meta-analysis. Targeted sequencing was conducted in 1,291 Caucasians from the Framingham Heart Study ( n = 925) and Cardiovascular Health Study ( n = 366), including 206 women and men with extreme low femoral neck (FN) BMD. A total of 4,964 sequence variants (SNVs) were observed and 80% were rare with MAF <1%. The associations between previously identified SNPs in these loci and BMD, while nominally significant in sequenced participants, were no longer significant after multiple testing corrections. Conditional analyses did not find protein-coding variants that may be responsible for GWAS signals. On the other hand, in the sequenced subjects, we identified novel associations in WLS , ARHGAP1 , and 5' of MEF2C ( P- values < 8x10 - 5 ; false discovery rate (FDR) q-values < 0.01) that were much more strongly associated with BMD compared to the GWAS SNPs. These associated SNVs are less-common; independent from previous GWAS signals in the same loci; and located in gene regulatory elements. Our findings suggest that protein-coding variants in selected GWAS loci did not contribute to GWAS signals. By performing targeted sequencing in GWAS loci, we identified less-common and rare non-coding SNVs associated with BMD independently from GWAS common SNPs, suggesting both common and less-common variants may associate with disease risks and phenotypes in the same loci.

Robust analysis of secondary phenotypes in case-control genetic association studies.

The case-control study is a common design for assessing the association between genetic exposures and a disease phenotype. Though association with a given (case-control) phenotype is always of primary interest, there is often considerable interest in assessing relationships between genetic exposures and other (secondary) phenotypes. However, the case-control sample represents a biased sample from the general population. As a result, if this sampling framework is not correctly taken into account, analyses estimating the effect of exposures on secondary phenotypes can be biased leading to incorrect inference. In this paper, we address this problem and propose a general approach for estimating and testing the population effect of a genetic variant on a secondary phenotype. Our approach is based on inverse probability weighted estimating equations, where the weights depend on genotype and the secondary phenotype. We show that, though slightly less efficient than a full likelihood-based analysis when the likelihood is correctly specified, it is substantially more robust to model misspecification, and can out-perform likelihood-based analysis, both in terms of validity and power, when the model is misspecified. We illustrate our approach with an application to a case-control study extracted from the Framingham Heart Study. Copyright © 2016 John Wiley & Sons, Ltd.

Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project.

Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.

Multi-ethnic fine-mapping of 14 central adiposity loci.

The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ± 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality.

A Polymorphism in a gene encoding Perilipin 4 is associated with height but not with bone measures in individuals from the Framingham Osteoporosis Study.

There is increasing interest in identifying new pathways and candidate genes that confer susceptibility to osteoporosis. There is evidence that adipogenesis and osteogenesis may be related, including a common bone marrow progenitor cell for both adipocytes and osteoblasts. Perilipin 1 (PLIN1) and Perilipin 4 (PLIN4) are members of the PATS family of genes and are involved in lipolysis of intracellular lipid deposits. A previous study reported gender-specific associations between one polymorphism of PLIN1 and bone mineral density (BMD) in a Japanese population. We hypothesized that polymorphisms in PLIN1 and PLIN4 would be associated with bone measures in adult Caucasian participants of the Framingham Osteoporosis Study (FOS). We genotyped 1,206 male and 1,445 female participants of the FOS for four single-nucleotide polymorphism (SNPs) in PLIN1 and seven SNPs in PLIN4 and tested for associations with measures of BMD, bone ultrasound, hip geometry, and height. We found several gender-specific significant associations with the measured traits. The association of PLIN4 SNP rs8887, G>A with height in females trended toward significance after simulation testing (adjusted P = 0.07) and remained significant after simulation testing in the combined-sex model (adjusted P = 0.033). In a large study sample of men and women, we found a significant association between one SNP in PLIN4 and height but not with bone traits, suggesting that PATS family genes are not important in the regulation of bone. Identification of genes that influence human height may lead to a better understanding of the processes involved in growth and development.

Selection of the most informative individuals from families with multiple siblings for association studies.

Association analyses may follow an initial linkage analysis for mapping and identifying genes underlying complex quantitative traits and may be conducted on unrelated subsets of individuals where only one member of a family is included. We evaluate two methods to select one sibling per sibship when multiple siblings are available: (1) one sibling with the most extreme trait value; and (2) one sibling using a combination score statistic based on extreme trait values and identity-by-descent sharing information. We compare the type I error and power. Furthermore, we compare these selection strategies with a strategy that randomly selects one sibling per sibship and with an approach that includes all siblings, using both simulation study and an application to fasting blood glucose in the Framingham Heart Study. When genetic effect is homogeneous, we find that using the combination score can increase power by 30-40% compared to a random selection strategy, and loses only 8-13% of power compared to the full sibship analysis, across all additive models considered, but offers at least 50% genotyping cost saving. In the presence of genetic heterogeneity, the score offers a 50% increase in power over a random selection strategy, but there is substantial loss compared to the full sibship analysis. In application to fasting blood sample, two SNPs are found in common for the selection strategies and the full sample among the 10 highest ranked single nucleotide polymorphisms. The EV strategy tends to agree with the IBD-EV strategy and the analysis of the full sample.

Genetics of the Framingham Heart Study population.

This chapter provides an introduction to the Framingham Heart Study and the genetic research related to cardiovascular diseases conducted in this unique population. It briefly describes the origins of the study, the risk factors that contribute to heart disease, and the approaches taken to discover the genetic basis of some of these risk factors. The genetic architecture of several biological risk factors has been explained using family studies, segregation analysis, heritability, and phenotypic and genetic correlations. Many quantitative trait loci underlying cardiovascular diseases have been discovered using different molecular markers. Additionally, initial results from genome-wide association studies using 116,000 markers and the prospects of using 550,000 markers for association studies are presented. Finally, the use of this unique sample to study genotype and environment interactions is described.