Association of structural variation with cardiometabolic traits in Finns

Abstract

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p ¼ 1.47 3 10 À54 ) and is also associated with increased levels of total cholesterol (p ¼ 1.22 3 10 À28 ) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p ¼ 4.81 3 10 À21 ) and alanine (p ¼ 6.14 3 10 À12 ) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p ¼ 6.24 3 10 À10 ), which was also found to be strongly associated with increased glycoprotein level (p ¼ 3.53 3 10 À35 ). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

The individual and global impact of copy-number variants on complex human traits

“…Future release of large sequencing datasets combined to progress in CNV detection tools could resolve these issues and lead to novel discoveries. 21 , 80 , 132 , 133 Second, despite substantial evidence of CNV- and SNP-GWAS signal colocalization, we did not perform robust enrichment analyses, as the non-random genomic distribution and complex nature of CNVs renders simulating the null scenario beyond the scope of this paper. Signal colocalization is likely to be underestimated, as manual literature searches revealed overlaps missed by our annotation pipeline (e.g., 16p13.11 age at menarche signal 101 ) and we obtained a 7% increase in signal colocalization by using GWAS Catalog annotation 6 months apart (31% April 2021 → 38% October 2021).…”

The individual and global impact of copy number variants on complex human traits

“…To mitigate these issues, we used a pipeline to filter CNVs and transform calls to the probe level (Macé et al, 2016(Macé et al, , 2017. Future release of large whole-genome sequencing datasets combined to progress in CNV detection tools should resolve these issues and lead to the discovery of new small-scale CNV-trait associations (Chen et al, 2021;Halvorsen et al, 2020). Second, as the aim of this study was to provide a set of trustworthy CNV-trait pairs for follow-up analyses, we used stringent criteria both for CNV filtering and association detection, at risk of missing some true associations.…”

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences

“…Our data show highly significant associations between blood-derived MT-CN measurements and several cardiometabolic traits, particularly insulin and fat mass. Anecdotally, it is interesting to note that these MT association signals can also be detected using read-depth analysis of the nuclear genome (Figure S1 -Additional File 1) [54], where reads derived from mtDNA align erroneously to several nuclear loci based on homology between the MT genome and ancient nuclear mitochondrial insertions. This result provides additional evidence for the reported trait associations using an independent MT-CN estimation method, and indicates that these homology-based signals need to be taken into account in future CNV association studies.…”