Biology subjects

Winkler, T. W.

Publications and source records attributed to Winkler, T. W..

8 recordsLinked to original sources

bioRxiv2021

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

genomics↗

bioRxiv2020

Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals

Chronic kidney disease (CKD) has a complex genetic underpinning. Genome-wide association studies (GWAS) of CKD-defining glomerular filtration rate (GFR) have identified hundreds of loci, but prioritization of variants and genes is challenging. To expand and refine GWAS discovery, we meta-analyzed GWAS data for creatinine-based estimated GFR (eGFRcrea) from the Chronic Kidney Disease Genetics Consortium (CKDGen, n=765,348, trans-ethnic) and UK Biobank (UKB, n=436,581, Europeans). The results (i) extend the number of eGFRcrea loci (424 loci; 201 novel; 8.9% eGFRcrea variance explained by 634 independent signals); (ii) improve fine-mapping resolution (138 99% credible sets with [≤]5 variants, 44 single-variant sets); (iii) ascertain likely kidney function relevance for 343 loci (consistent association with alternative biomarkers); and (iv) highlight 34 genes with strong evidence by a systematic Gene PrioritiSation (GPS). We provide a sortable, searchable and customizable GPS tool to navigate through the in silico functional evidence and select relevant targets for functional investigations.

genetics↗

bioRxiv2020

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration (Pint < 5x10-8). Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively (Pint < 5x10-8). Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci ([≤]1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3 (Pint < 5x10-8). This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

genomics↗

bioRxiv2019

Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease

BackgroundAdvanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of the preceding early stages of the diseases. MethodsTo identify genetic factors for early AMD, we conducted a genome-wide association meta-analysis with 14,034 early AMD cases and 91,214 controls from 11 sources of data including data from the International AMD Genomics Consortium (IAMDGC) and the UK Biobank (UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and by using an automated machine learning approach for >170,000 images from UKBB. We searched for significant genetic loci in a genome-wide association screen (P<5x10-8) based on the meta-analysis of the 11 sources and via a candidate approach based on 13 suggestive early AMD variants from Holliday et al 2013 (P<0.05/13, additional 3,432 early AMD cases and 11,235 controls). For the novel AMD regions, we conducted in-silico follow-up analysis to prioritize causal genes and pathway analyses. ResultsWe identified 11 loci for early AMD, 9 novel and 2 known for early AMD. Most of these 11 loci overlapped with known advanced AMD loci (near ARMS2/HTRA1, CFH, APOE, C2, C3, CETP, PVRL2, TNFRSF10A, VEGFA), except two that were completely novel to any AMD. Among the 17 genes within the two novel loci, in-silico functional annotation suggested CD46 and TYR as the most likely responsible genes. We found the presence or absence of an early AMD effect to distinguish known pathways of advanced AMD genetics (complement/lipid pathways or extracellular matrix metabolism, respectively). ConclusionsOur data on early AMD genetics provides a resource comparable to the existing data on advanced AMD genetics, which enables a joint view. Our large GWAS on early AMD identified novel loci, highlighted shared and distinct genetics between early and advanced AMD and provides insights into AMD etiology. The ability of early AMD effects to differentiate the major pathways for advanced AMD underscores the biological relevance of a joint view on early and advanced AMD genetics.

genetics↗

bioRxiv2019

Chances and challenges of machine learning based disease classification in genetic association studies illustrated on age-related macular degeneration

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We established machine learning based disease classification in genetic association analysis as a misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automated classification of age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (images from 4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artefacts in simulation studies. By combining a GWAS on automatically derived AMD classification and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1, CFH) from artefacts (near HERC2) and to identify eye color as relevant source of misclassification. On this example of AMD, we are able to provide a proof-of-concept that a GWAS using machine learning derived disease classification yields relevant results and that misclassification needs to be considered in the analysis. These findings generalize to other phenotypes and also emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.

genetics↗

bioRxiv2019

Multi-ancestry analysis of gene-sleep interactions in 126,926 individuals identifies multiple novel blood lipid loci that contribute to our understanding of sleep-associated adverse blood lipid profile

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To provide new insights in the biology of sleep-associated adverse lipid profile, we conducted multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identified 49 novel lipid loci, and 10 additional novel lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identified new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The novel gene-sleep interactions had a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explained 4.25% of the variance in triglyceride concentration. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

genomics↗

bioRxiv2018

PROTEIN-CODING VARIANTS IMPLICATE NOVEL GENES RELATED TO LIPID HOMEOSTASIS CONTRIBUTING TO BODY FAT DISTRIBUTION

Body fat distribution is a heritable risk factor for a range of adverse health consequences, including hyperlipidemia and type 2 diabetes. To identify protein-coding variants associated with body fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, we analyzed 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries for discovery and 132,177 independent European-ancestry individuals for validation. We identified 15 common (minor allele frequency, MAF[≥]5%) and 9 low frequency or rare (MAF<5%) coding variants that have not been reported previously. Pathway/gene set enrichment analyses of all associated variants highlight lipid particle, adiponectin level, abnormal white adipose tissue physiology, and bone development and morphology as processes affecting fat distribution and body shape. Furthermore, the cross-trait associations and the analyses of variant and gene function highlight a strong connection to lipids, cardiovascular traits, and type 2 diabetes. In functional follow-up analyses, specifically in Drosophila RNAi-knockdown crosses, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). By examining variants often poorly tagged or entirely missed by genome-wide association studies, we implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

genetics↗

bioRxiv2018

Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry

One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

genetics↗