Introduction Genome wide association studies (GWASs) have been used to analyse thegenetic architecture of common diseases and quantitative traits (Visscher etal., 2012). These studies assess common variants that have a minor allelefrequency (MAF) >5% in the human genome. They have been completed for mostcommon diseases and numerous associated traits. They have uncovered more thantwo thousand disease-related genetic common variants (NHGRI, 2015).
But these relatedcommon variants have very small effect sizes and a modest effect in predicting diseaserisk or quantitative traits. For example, substantial meta-analysis of GWAS oftype 2 diabetes (T2D) in more than 10,128 people have identified more than 18SNPs associated with the disease, but these sites explain only 6% of the heritabilityof the T2D, and does not explain the causal biology (Zeggini et al., 2008).
Aswell, in Crohn disease, GWAS meta-analysis in more than 210,000 people haveidentified 70 loci associated with the disease, but these explain only 23% ofthe increased disease risk between relatives (Frankeet al., 2010). Generally, the majority of identified common variants through GWASshave shed no light on the casual biology of the disease or trait. This problemreferred to as missing heritability. Low-frequency and rare variants mightsolve a portion of missing heritability. Thus, it is reasonable that analyses oflow-frequency with a MAF of (0.
5% ?MAF <5%) and rare with a MAF of < 0.5variants could give an explanation to disease risk or quantitative trait (Lee et al., 2014). The advancement in sequencing technologies allows in depth examinationson the genetic contribution of rare variants to complex traits.
This essay will look into challenges of studying rare variants and what sequencingapproaches and statistical methods that can be used for rare variant associationdetection analysis and testing. And mention some current studies thatdiscovered rare variants.