Statistical genetics methods for complex traits using large-scale genomic data
Principal Investigator: Dr Guo-Bo Chen
Approved Research ID: 41376
Approval date: September 10th 2018
Many human traits (such as height) and complex disease (such as Crohn's disease) are complex traits, which are known to be controlled by many genes of small effects. To reveal the how genes control the complex traits require large sample size, such as promised by UK Biobank. AIMs: the study is to develop statistical methods that are able to quantity how phenotypic variation is associated to genotypes. Using UK Biobank cohort as demonstration, we can provide an atlas of the heritability for a range of human complex traits and reveal the genetic architecture for complex traits. Scientific rationale: large sample size is important for scientific discovery, especially for human complex traits and diseases. However, the current statistical methods have been designed to handle small sample size, and computationally infeasible for emerging cohort such as UK Biobank. We propose a method that has its computational cost proportional to the square of the sample size. So, it will be able to handle UK Biobank data more efficiently and bring about comprehensive results than ever before. Project duration: so far we have already developed the statistical algorithm of the statistical method. It takes 1 year to realize and test the method in compute codes. Another 1 year is required to analyze the UK Biobank data thoroughly. The third year will be spent in write a report for our discovery. Public health impact: the final product of the study will be a comprehensive atlas of the quantified relationship between complex traits, a map that will shed light on the genetic architecture of complex traits and provide guidelines on mining etiology of various human complex traits and diseases.