Pierre-Jean, M. | bioRxiv Science

bioRxiv2023

Copy Number Variants and heritability estimates on UKBiobank data

Copy Number Variants (CNVs) are sometimes used to perform association studies. The aim of this paper was to study the use of CNVs in another context: heritability estimation. We wanted to assess the impact of using CNVs in these estimates, either alone, or in conjunction with Single Nucleotide Polymorphisms (SNPs). Using real SNP and CNV data from UK Biobank, we simulated phenotypes depending either on one or the two type(s) of data. We showed that mixed models, usually used for estimating heritability on SNP data, were also capable of estimating CNV heritability and to properly decipher between CNV and SNP heritabilities when phenotypes depend on both types of data. However CNV heritability estimation becomes more challenging when it is only supported by the few relatively common CNVs. Finally we estimated CNV and SNP heritabilities for two real phenotypes from UK Biobank (height and hypertension) but only hypertension showed a small but non-null CNV heritability of about 1.7%.

genetics↗

bioRxiv2020

Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.

Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes, and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a sin gle block, while the last approach considers one model by haplotype. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.

bioinformatics↗