Exome sequencing identifies high-impact trait-associated alleles enriched in Finns

Abstract

86As yet undiscovered rare variants are hypothesized to substantially influence an 87 individual's risk for common diseases and traits, but sequencing studies aiming to 88 identify such variants have generally been underpowered. In isolated populations that 89 have expanded rapidly after a population bottleneck, deleterious alleles that passed 90 through the bottleneck may be maintained at much higher frequencies than in other 91 populations. In an exome sequencing study of nearly 20,000 cohort participants from 92 northern and eastern Finnish populations that exemplify this phenomenon, most novel 93 trait-associated deleterious variants are seen only in Finland or display frequencies more 94 than 20 times higher than in other European populations. These enriched alleles underlie 95 34 novel associations with 21 disease-related quantitative traits and demonstrate a 96 geographical clustering equivalent to that of Mendelian disease mutations characteristic 97 of the Finnish population. Sequencing studies in populations without this unique history 98 would require hundreds of thousands to millions of participants for comparable power for 99 these variants. 100 101 (defined here as MAF≤1%) which are not well-tagged by the single-nucleotide 109 polymorphisms (SNPs) on genome-wide genotyping arrays are probably responsible for 110 much of the heritability that remains unexplained 2 . Additionally, because purifying 111 selection acts to remove deleterious alleles from the population, most variants that exert a 112 sizable effect on complex traits, and that likely offer the best prospect for revealing 113 biological mechanisms, should be particularly rare. 114 115 Rare variants are unevenly distributed between populations and difficult to represent 116 effectively on commercial genotyping arrays, as evidenced by relatively sparse 117 association findings even from large array-based studies of coding variants 3-6 . 118Discovering rare variant associations will therefore almost certainly require exome or 119 genome sequencing of very large numbers of individuals. However, the sample size 120 required to reliably identify rare-variant associations remains uncertain; most sequencing 121 studies to date have identified few novel associations, and theoretical analyses confirm 122 that they have been underpowered to do so 7 . These analyses also suggest that power to 123 detect rare variant associations varies enormously between populations that have 124 expanded in isolation from recent bottlenecks compared to those that have not. 125 126In isolated populations that expand rapidly following a bottleneck, alleles that pass 127 through the bottleneck often rise to a much higher frequency than in other populations [8][9][10] . 128If the bottleneck was recent, even deleterious alleles under negative selection may remain 129 relatively frequent in these populations, resulting in increased power to detect association 130 with disease-related traits. The Finnish population exemplifies this type of history. It 131 5 grew from bottle...

Association of Structural Variation with Cardiometabolic Traits in Finns

“…All phenotype data were residualized for trait-specific covariates and transformed to a standard normal distribution by inverse normalization. Complete details of sample collection, genotype acquisition, and trait adjustments were described previously 14 .…”

“…Exons with R 2 <0.1 were filtered out and the rest were passed on to validation, restricted to samples absent from the WGS analysis (n=15,205). The genetic relationship matrix used for WES replication was generated in a previous study 14 . We later did a meta-analysis under a fixed effect model using METASOFT (v2.0.1) 29 to combine the results from the two WES batches, considering the two sequencing batches were actually sampled from the same population.…”

“…For genome-wide significant trait-SV associations, we collected previous GWAS signals on the same chromosome with P<10 -7 from the EBI GWAS catalogue (https://www.ebi.ac.uk/gwas/docs/filedownloads, 2019-11-21 version) with the same set of keywords used in a previous study 14 (one publication based on METSIM samples was excluded to only include findings from independent studies). We then performed conditional analysis on the original trait-SV pairs adding the GWAS hits as covariates.…”

“…We next sought to replicate these findings and to follow up on 4,855 loci with sub-threshold associations (p<0.001) via meta-analysis with larger WES (n=20,316) and array genotype datasets (n=19,033) from these same cohorts, using independent samples (nWES=15,205, narray=15,125 ) not included in the original WGS experiment (see Methods) 14 . We developed a strategy to genotype coding CNVs from WES data using read-depth information from XHMM 28 , and measured copy number at the 20,058 exons intersecting with 819 candidate CNVs from WGS.…”

“…Here, we have performed a SV association study using deep (>20x) WGS data from 4,030 individuals from Finland with extensive cardiometabolic trait measurements, and extended these results to a larger set of 15,205 individuals with whole exome sequencing (WES) and single nucleotide polymorphism (SNP) genotype data. Compared to prior work, our study benefits from (1) comprehensive SV ascertainment due to the use of deep WGS data and complementary SV detection methods, (2) deeply phenotyped individuals with existing SNP array and exome sequence data, and (3) the unique history of the Finnish population, which was shaped by multiple population bottlenecks and rapid population expansions, leading to an enrichment of some otherwise rare and low-frequency variants that can be detected by trait association at relatively modest sample sizes [12][13][14] . By testing for associations between structural variants and cardiometabolic traits, we identified 15 genome-wide significant loci, nine of which remained significant after multiple testing correction for the number of phenotypes, including a Finnish-enriched promoter deletion of the ALB gene associated with multiple traits, and a multiallelic CNV affecting the PDPR gene associated with pyruvate levels.…”