Abstract
Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.
Highlights
We used a Genome-wide association studies (GWAS) approach to test for associations with no fewer than 72 inflammatory plasma protein biomarkers, in order to investigate the gain in precision and rare variant-capture with whole genome sequencing (WGS) data compared to genotyped/imputed single-nucleotide polymorphisms (SNPs)
We performed a GWAS on 72 inflammatory biomarkers in a Swedish cohort using WGS data, and identified single nucleotide variants (SNVs) that were associated with the plasma levels for as many as 41 biomarkers
We have previously used both mass spectrometry and the recently developed protein extension assay (PEA) to identify the genetic contribution to variation in protein levels in the Northern Swedish population health study (NSPHS) cohort, where we showed that more than 30% of the biomarkers are influenced by genetic variants[23,28,30,31]
Summary
When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of
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