Sequence-based GWAS using thousands Sardinian genomes: an application to quantitative traits

In the last ten years, genome-wide association studies (GWAS) have been extensively used to dissect the genetic architecture of complex quantitative traits.Despite these findings, much of the genetic contribution to complex traits remains largely unexplained, even in diseases for which large GWAS meta-analyses have been undertaken.It is plausible that, using whole genome sequencing data and enlarging the spectrum of variants, these could explain additional disease risk or trait variability.Here I present how two different applications of sequence-based GWAS improve the current knowledge of genetic variation associated to important human traits. In particular, I show how whole-genome sequencing integrated with genotyping arrays by statistical inference led to the identification of novel common, low frequency and rare variants associated with levels of five inflammatory biomarkers and with two parameters related to thyroid function.This work highlights not only advantages but also current pitfalls of the sequence-based GWAS approach, such as the statistical methods utilized and the difficulty in replication of the association results and in estimation of variance explained.Nevertheless, sequence-based GWAS will enlarge our current knowledge of genes associated to complex traits, highlight novel biological pathways and elucidate underlying mechanisms, suggesting critical points and issues to be considered in further developments and improvements of existing statistical methods.