Abstract
Genome wide association studies (GWAS) rely on microarrays, or more recently mapping of whole-genome sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome for the organism on which the association study is to be performed limits the scope of association studies, and also precludes the identification of differences between cases and controls outside of the reference. We present an alignment free method for association studies that is based on counting k-mers in sequencing reads, testing for associations directly between k-mers and the trait of interest, and local assembly of the statistically significant k-mers to identify sequence differences. Results with simulated data and an analysis of the 1000 genomes data provide a proof of principle for the approach. In a pairwise comparison of the Toscani in Italia (TSI) and the Yoruba in Ibadan, Nigeria (YRI) populations we find that sequences identified by our method largely agree with results obtained using standard GWAS based on variant calling from mapped reads. However unlike standard GWAS, we find that our method identifies associations with structural variations and sites not present in the reference genome revealing sequences absent from the human reference genome. We also analyze data from the Bengali from Bangladesh (BEB) population to explore possible genetic basis of high rate of mortality due to cardiovascular diseases (CVD) among South Asians and find significant differences in frequencies of a number of non-synonymous variants in genes linked to CVDs between BEB and TSI samples, including the site rs1042034, which has been associated with higher risk of CVDs previously, and the nearby rs676210 in the Apolipoprotein B (ApoB) gene.
Author Summary We present a method for associating regions in genomes to traits or diseases. The method is based on finding differences in frequencies of short strings of letters in sequencing reads and do not require reads to be aligned to a reference genome. This makes it applicable to study of organisms with no or incomplete reference genomes. We test our method with simulated data and sequencing data from the 1000 genomes project and find agreement with the conventional approach based on alignment to a reference genome. In addition, our method finds associations with sequences not in reference genomes and reveals sequences missing from the human reference genome. We also explore high rates of mortality due to cardiovascular diseases among South Asians and find prevalence of variations in genes associated with heart diseases in samples from the Bengali from Bangladesh population including one that has been reported to be associated with early onset of cardiovascular diseases.
