Biology subjects

Van Hout, C. V.

Publications and source records attributed to Van Hout, C. V..

6 recordsLinked to original sources

bioRxiv2020

Leveraging phenotypic variability to identify genetic interactions in human phenotypes

While thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly due to the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

genetics↗

bioRxiv2020

Exome-wide association studies in general and long-lived populations identify genetic variants related to human age

Aging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40x10-10) and ZYG11A rs74227999 (p = 2.50x10-08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males [≥] 92 years, Females [≥] 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

genetics↗

bioRxiv2020

Characterization of exome variants and their metabolic impact in 6,716 American Indians from Southwest US

Applying whole exome sequencing (WES) to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from an American Indian population in Southwest US (Southwestern American Indian, or SWAI) with well-characterized metabolic traits. We found that individuals of SWAI have distinct allelic architecture compared to individuals with European and East Asian ancestry, with many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in SWAI. We evaluated gene-level associations with metabolic traits using pLOF and nonsynonymous variants in SWAI. Many of the candidate genes from previous GWAS studies for body mass index, type 2 diabetes, and plasma lipid levels were associated with respective traits in SWAI. Notably, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in American Indians, many of which have not been observed in other populations or functionally characterized. Our study illustrates the utility and potential of WES in American Indians to prioritize candidate effector genes within GWAS loci and to find novel variants in known diseases genes with potential clinical impact.

genetics↗

bioRxiv2019

Genetic and functional evidence relates a missense variant in B4GALT1 to lower LDL-C and fibrinogen

Increased LDL-cholesterol (LDL-C) and fibrinogen are independent risk factors for cardiovascular disease (CVD). We identified novel associations between an Amish-enriched missense variant (p.Asn352Ser) in a functional domain of beta-1,4-galactosyltransferase 1 (B4GALT1) and 13.5 mg/dl lower LDL-C (p=1.6E-15), and 26 mg/dl lower plasma fibrinogen (p= 9.8E-05). N-linked glycan profiling found p.Asn352Ser to be associated (p-values from 1.4E-06 to 1.0E-17) with decreased glycosylation of glycoproteins including: fibrinogen, ApoB100, immunoglobulin G (IgG), and transferrin. In vitro assays found that the mutant (352Ser) protein had 50% lower galactosyltransferase activity compared to wild type (352Asn) protein. Knockdown of b4galt1 in zebrafish embryos resulted in significantly lower LDL-C compared to control, which was fully rescued by co-expression of 352Asn human B4GALT1 mRNA but only partially rescued by co-expression of 352Ser human B4GALT1 mRNA. Our findings establish B4GALT1 as a novel gene associated with lower LDL-C and fibrinogen and suggest that targeted modulation of protein glycosylation may represent a therapeutic approach to decrease CVD risk.

genetics↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2017

Profiling and leveraging relatedness in a precision medicine cohort of 92,455 exomes

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in-line with expectations given the underlying population and ascertainment approach. For example, we identified [~]66,000 close (first- and second-degree) relationships within DiscovEHR involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships as DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in [~]1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations through reconstructed pedigrees, including a tandem duplication in LDLR causing familial hypercholesterolemia. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

genomics↗