Biology subjects

Haessler, J.

Publications and source records attributed to Haessler, J..

5 recordsLinked to original sources

bioRxiv2025

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores

Polygenic scores (PGS) have promising clinical applications for risk stratification, disease screening, and personalized medicine. However, most PGS are trained on predominantly European ancestry cohorts and have limited portability to external populations. While cross-population PGS methods have demonstrated greater generalizability than single-ancestry PGS, they fail to properly account for individuals with recent admixture between continental ancestry groups. GAUDI is a recently proposed PGS method which overcomes this gap by leveraging local ancestry to estimate ancestry-specific effects, penalizing but allowing ancestry-differential effects. However, the modified fused LASSO approach used by GAUDI is computationally expensive and does not readily accommodate more than two-way admixture. To address these limitations, we introduce HAUDI, an efficient LASSO framework for admixed PGS construction. HAUDI re-parameterizes the GAUDI model as a standard LASSO problem, allowing for extension to multi-way admixture settings and far superior computational speed than GAUDI. In extensive simulations, HAUDI compares favorably to GAUDI while dramatically reducing computation time. In real data applications, HAUDI uniformly out-performs GAUDI across 18 clinical phenotypes, including total triglycerides (TG), C-reactive protein (CRP), and mean corpuscular hemoglobin concentration (MCHC), and shows substantial benefits over an ancestry-agnostic PGS for white blood cell count (WBC) and chronic kidney disease (CKD).

genomics↗

bioRxiv2023

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics

The heritability explained by local ancestry markers in an admixed population [Formula] provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of [Formula] can be susceptible to biases due to population structure in ancestral populations. Here, we present a novel approach, Heritability estimation from Admixture Mapping Summary STAtistics (HAMSTA), which uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA [Formula] estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of [~]5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe [Formula] in the 20 phenotypes range from 0.0025 to 0.033 (mean [Formula]), which translates to [Formula] ranging from 0.062 to 0.85 (mean [Formula]). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 +/-0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

genetics↗

bioRxiv2018

Trans-ethnic genome-wide association study provides insight into effector genes and molecular mechanisms for kidney function and highlights a causal effect on kidney-specific disease aetiologies

Chronic kidney disease (CKD) affects [~]10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assembled genome-wide association studies (GWAS)1-3 of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals from four ancestry groups. We identified 93 loci (20 novel), which were delineated to 127 distinct association signals. These signals were homogenous across ancestries, and were enriched for protein-coding exons, kidney-specific histone modifications, and transcription factor binding sites for HDAC2 and EZH2. Fine-mapping revealed 40 high-confidence variants driving eGFR associations and highlighted potential causal genes with cell-type specific expression in glomerulus, and proximal and distal nephron. Mendelian randomisation (MR) supported causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure (DBP) and hypertension. These results define novel molecular mechanisms and effector genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.

genetics↗

bioRxiv2017

Genetic Diversity Turns a New PAGE in Our Understanding of Complex Traits

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

genetics↗

bioRxiv2017

Exome chip meta-analysis elucidates the genetic architecture of rare coding variants in smoking and drinking behavior

BackgroundSmoking and alcohol use behaviors in humans have been associated with common genetic variants within multiple genomic loci. Investigation of rare variation within these loci holds promise for identifying causal variants impacting biological mechanisms in the etiology of disordered behavior. Microarrays have been designed to genotype rare nonsynonymous and putative loss of function variants. Such variants are expected to have greater deleterious consequences on gene function than other variants, and significantly contribute to disease risk.\n\nMethodsIn the present study, we analyzed [~]250,000 rare variants from 17 independent studies. Each variant was tested for association with five addiction-related phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted single variant tests of all variants, and gene-based burden tests of nonsynonymous or putative loss of function variants with minor allele frequency less than 1%.\n\nResultsMeta-analytic sample sizes ranged from 70,847 to 164,142 individuals, depending on the phenotype. Known loci tagged by common variants replicated, but there was no robust evidence for individually associated rare variants, either in gene based or single variant tests. Using a modified method-of-moment approach, we found that all low frequency coding variants, in aggregate, contributed 1.7% to 3.6% of the phenotypic variation for the five traits (p<.05).\n\nConclusionsThe findings indicate that rare coding variants contribute to phenotypic variation, but that much larger samples and/or denser genotyping of rare variants will be required to successfully identify associations with these phenotypes, whether individual variants or gene- based associations.

genetics↗