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Genome-wide association studies: in search of common and low frequency variants in complex traits

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Genome-wide association studies (GWAS) have transformed the field of complex trait genetics over the past 7 years. A number of scientific achievements made GWAS feasible, for example the availability of large sample sizes, better understanding of human genome sequence variation, high-throughput genotyping technologies, and the development of methodological and analytical approaches to analyze and interpret genetic data. Traditionally, GWAS have focused on common-frequency single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) ≥ 0.05) and have typically been powered to detect modest/small effect sizes. Genome-wide meta-analysis, facilitated by imputation of untyped genetic variants, has been used as a robust framework within which to synthesize data across studies and genotyping platforms, thus increasing power and leading to further novel discoveries. Although these findings have improved our understanding of the genetic basis of many complex traits, for most traits they explain only a fraction of genetic heritability. This observation supports the long established idea that low frequency and rare variants may play an important role in common diseases. This hypothesis has caused a recent shift of the complex trait genetics field towards low frequency (MAF between 0.01 and 0.05) and rare variation (MAF less than 0.01). In this talk, I will introduce key principles and analytical issues when conducting GWAS . I will also discuss current and impending extensions in the field of complex disease genetics employing the next generation of chips, whole genome sequencing, and a wide array of populations.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

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