Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).

Gene-lifestyle interactions in the genomics of human complex traits.

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

VarExp: estimating variance explained by genome-wide GxE summary statistics.

Summary: Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, 'VarExp', that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users. Availability and implementation: The R package is available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git. Supplementary information: Supplementary data are available at Bioinformatics online.

Pharmacogenomic characterization of US FDA-approved cytotoxic drugs.

AIMS: Individualization of cancer chemotherapy based on the patient's genetic makeup holds promise for reducing side effects and improving efficacy. However, the relative contribution of genetics to drug response is unknown. MATERIALS & METHODS: In this study, we investigated the cytotoxic effect of 29 commonly prescribed chemotherapeutic agents from diverse drug classes on 125 lymphoblastoid cell lines derived from 14 extended families. RESULTS: The results of this systematic study highlight the variable role that genetics plays in response to cytotoxic drugs, ranging from a heritability of <0.15 for gemcitabine to >0.60 for epirubicin. CONCLUSION: Putative quantitative trait loci for cytotoxic response were identified, as well as drug class-specific signatures, which could indicate possible shared genetic mechanisms. In addition to the identification of putative quantitative trait locis, the results of this study inform the prioritization of chemotherapeutic drugs with a sizable genetic response component for future investigation.

Methods for analysis in pharmacogenomics: lessons from the Pharmacogenetics Research Network Analysis Group.

Each year, the Pharmacogenetics Research Network (PGRN) holds an analysis workshop for the members of the PGRN to share new methodologies, study design approaches and to discuss real data applications. This event is closed to members of the PGRN, but the methods presented are relevant to others conducting pharmacogenomics research. This special report describes many of the novel approaches discussed at the workshop and provides a resource for investigators in the field performing pharmacogenomics data analysis. While the focus is pharmacogenomics, the methods discussed are far ranging and have relevance to all types of genetic association studies: identifying noncoding variants and tag-SNPs, haplotype analysis, multivariate techniques, quantitative trait analysis, gene-gene and gene-environment interactions, and genome-wide association studies. The goal is to introduce readers to the topics discussed at the workshop and provide a direction for future development of analysis tools and methods for analysis of pharmacogenomic data.

Haplotype association analysis of AGT variants with hypertension-related traits: the HyperGEN study.

OBJECTIVE: Function of the renin-angiotensin system is important to human hypertension, but its genetic etiology remains elusive. We set out to examine a hypothesis that multiple genetic variants in the system act together in blood pressure regulation, via intermediate phenotypes such as blood pressure reactivity. METHODS: A sample of 531 hypertensive cases and 417 controls was selected from the HyperGEN study. Hypertension-related traits including blood pressure responses to challenges to math test, handgrip and postural change (mathBP, gripBP, and postBP), and body mass index (BMI) were analyzed for association with 10 single nucleotide polymorphisms (SNPs) in the angiotensinogen (AGT) gene. Single-marker and haplotype analyses were performed to examine the effects of both individual and multiple variants. Multiple-trait profiling was used to assess interaction of latent intermediate factors with susceptible haplotypes. RESULTS: In Blacks, two SNPs in exon 5 and 3'UTR showed significant association with gripBP, and two promoter SNPs were strongly associated with postBP. In Whites, only borderline association was found for 2 promoter SNPs with mathBP. Haplotype analyses in Blacks confirmed association with gripBP, and detected significant association of a haplotype to BMI (p=0.029). With the interactions modeled, haplotype associations found in Blacks remain significant, while significant associations to BMI (p=0.009) and gripSBP emerged in Whites. CONCLUSION: Genetic variants in regulatory regions of AGT showed strong association with blood pressure reactivity. Interaction of promoter and genic SNPs in AGT revealed collective action of multiple variants on blood pressure reactivity and BMI both in Blacks and in Whites, possibly following different pathways.

Ethnicity and human genetic linkage maps.

Human genetic linkage maps are based on rates of recombination across the genome. These rates in humans vary by the sex of the parent from whom alleles are inherited, by chromosomal position, and by genomic features, such as GC content and repeat density. We have examined--for the first time, to our knowledge--racial/ethnic differences in genetic maps of humans. We constructed genetic maps based on 353 microsatellite markers in four racial/ethnic groups: whites, African Americans, Mexican Americans, and East Asians (Chinese and Japanese). These maps were generated using 9,291 subjects from 2,900 nuclear families who participated in the National Heart, Lung, and Blood Institute-funded Family Blood Pressure Program, the largest sample used for map construction to date. Although the maps for the different groups are generally similar, we did find regional and genomewide differences across ethnic groups, including a longer genomewide map for African Americans than for other populations. Some of this variation was explained by genotyping artifacts--namely, null alleles (i.e., alleles with null phenotypes) at a number of loci--and by ethnic differences in null-allele frequencies. In particular, null alleles appear to be the likely explanation for the excess map length in African Americans. We also found that nonrandom missing data biases map results. However, we found regions on chromosome 8p and telomeric segments with significant ethnic differences and a suggestive interval on chromosome 12q that were not due to genotype artifacts. The difference on chromosome 8p is likely due to a polymorphic inversion in the region. The results of our investigation have implications for inferences of possible genetic influences on human recombination as well as for future linkage studies, especially those involving populations of nonwhite ethnicity.

Race differences in reproducibilities: The HERITAGE family study.

The HERITAGE (HEalth, RIsk factors, exercise Training And GEnetics) Family Study is a multicenter clinical trial conducted by five institutions in the United States and Canada. The overall objective of the study is to investigate the role of the genotype in cardiovascular, metabolic, and hormonal responses to aerobic exercise training and the contribution of regular exercise to changes in cardiovascular disease and diabetes risk factors in white and black families. Since the accuracy of the assessment of the response to training depends on how repeatable or reproducible the measurements are, it is important to assess potential racial differences in reproducibilities, which may have implications for pooling data across races. The sample studied consisted of 96 blacks and 304 whites. The black sample had 46 males with mean age 33.6 ± 14.2 years and 40 females with mean age 33.9 ± 12.7 years. The white sample had 152 males with mean age 35.5 ± 14.9 years, and 152 females with mean age 34.9 ± 14.3 years. Reproducibilities, as measured by intraclass correlations among repeated measures, were comparable between whites and blacks for variables in the anthropometry, i.e, lipid, exercise test, and blood pressure domains. Reproducibilities in both races exceeded 0.85 for most of the variables. When the within-race reproducibilities are very high, statistical significance of any observed racial difference in the reproducibilities may not be very meaningful. There was a significant racial difference in the reproducibility for Apoprotein A1 (0.73 in blacks, 0.89 in whites, P < 0.01). However, this is not a cause for concern, since only one among 37 comparisons was significant. Am. J. Hum. Biol. 9:415-424, 1997. © 1997 Wiley-Liss, Inc.