Biology subjects

Gunnarsson, A. F.

Publications and source records attributed to Gunnarsson, A. F..

3 recordsLinked to original sources

bioRxiv2025

Robust inference and widespread genetic correlates from a large-scale genetic association study of human personality

Personality traits describe stable differences in how individuals think, feel, and behave and how they interact with and experience their social and physical environments. We assemble data from 46 cohorts including 611K-1.14M participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), and data from 51K participants for within-family GWAS. We identify 1,257 lead genetic variants associated with personality, including 823 novel variants. Common genetic variants explain 4.8%-9.3% of the variance in each trait, and 10.5%-16.2% accounting for measurement unreliability. Genetic effects on personality are highly consistent across geography, reporter (self vs. close other), age group, and measurement instrument, and we find minimal spousal assortment for personality in recent history. In stark contrast to many other social and behavioral traits, within-family GWAS and polygenic index analyses indicate little to no shared environmental confounding in genetic associations with personality. Polygenic prediction, genetic correlation, and Mendelian randomization analyses indicate that personality genetics have widespread, potentially causal associations with a wide range of consequential behaviors and life outcomes. The genetic architecture of personality is robust and fundamental to being a human.

genomics↗

bioRxiv2024

A scalable approach for genome-wide inference of ancestral recombination graphs

The ancestral recombination graph (ARG) is a graph-like structure that encodes a detailed genealogical history of a set of individuals along the genome. ARGs that are accurately reconstructed from genomic data have several downstream applications, but inference from data sets comprising millions of samples and variants remains computationally challenging. We introduce Threads, a threading-based method that significantly reduces the computational costs of ARG inference while retaining high accuracy. We apply Threads to infer the ARG of 487,409 genomes from the UK Biobank using [~]10 million high-quality imputed variants, reconstructing a detailed genealogical history of the samples while compressing the input genotype data. Additionally, we develop ARG-based imputation strategies that increase genotype imputation accuracy for ultra-rare variants (MAC [≤]10) from UK Biobank exome sequencing data by 5-10%. We leverage ARGs inferred by Threads to detect associations with 52 quantitative traits in non-European UK Biobank samples, identifying 22.5% more signals than ARG-Needle. These analyses underscore the value of using computationally efficient genealogical modeling to improve and complement genotype imputation in large-scale genomic studies.

genetics↗

bioRxiv2024

Fast variance component analysis using large-scale ancestral recombination graphs

Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE leverages a method for computing genotype-matrix products from genealogical data in sublinear time, along with scalable randomized algorithms. This enables fast estimation of variance components and their statistical significance, supports parallel analysis of multiple quantitative traits, and facilitates other linear mixed-model analyses. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,159 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.

genetics↗