Biology subjects

Nivard, M.

Publications and source records attributed to Nivard, M..

3 recordsLinked to original sources

bioRxiv2026

Ultra-Fast Implementation of Multivariate GWAS in Genomic SEM Using Flexible Analytic Estimation

Many medical, physiological, and psychiatric traits and disorders are highly polygenic and exhibit complex patterns of genetic sharing and differentiation. In 2018, we introduced Genomic Structural Equation Modelling (Genomic SEM) as a formal framework and free, open source, R-based software for modelling the multivariate genetic architecture of both continuous and binary Genome-Wide Association Study (GWAS) phenotypes, interrogating their joint and distinct functional genomic pathways, and leveraging empirical models of the genetic relations among phenotypes to guide multivariate GWAS discovery. Here we introduce a closed-form analytic solution for estimating SNP effects within multivariate GWAS in Genomic SEM. This estimator is over 800 times faster than the existing iterative estimator, drastically decreasing reliance on high performance computing (HPC). On a MacBook pro laptop with M4 Max chip, a multivariate GWAS ([~]1M SNPs) of 5 common factors underlying 13 phenotypes takes approximately 2 minutes. Concurrent with the release of this preprint, we are adding an analytic estimation option to the userGWAS function in the GenomicSEM package for alpha testing along with a tutorial on our GitHub wiki.

genetics↗

bioRxiv2025

Genetic basis of partner choice

Previous genetic studies of human assortative mating have primarily focused on searching for its genomic footprint but have revealed limited insights into its biological and social mechanisms. Combining insights from the economics of the marriage market with advanced tools in statistical genetics, we perform the first genome-wide association study (GWAS) on a latent index for partner choice. Using 206,617 individuals from four global cohorts, we uncover phenotypic characteristics and social processes underlying assortative mating. We identify a broadly robust genetic component of the partner choice index between sexes and several countries and identify its genetic correlates. We also provide solutions to reduce assortative mating-driven biases in genetic studies of complex traits by conditioning GWAS summary statistics on the genetic associations with the latent partner choice index.

genetics↗

bioRxiv2019

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trondelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

genetics↗