Dissecting the genetic basis of complex diseases via the stratified high-definition likelihood method

Heritability and genetic correlation are central parameters for understanding the genetic architecture of complex diseases. It is essential to assess the enrichment of these parameters in genomic functional regions.

Making use of genome-wide association study summary statistics resources, linkage disequilibrium (LD) score regression was developed for the estimation of heritability and genetic correlation, without accessing individual-level data. Stratified LD score regression (S-LDSC) can assess heritability enrichment on various genome annotations.

However, LD score regression does not consider the full linkage information in the genome, and certain types of genomic annotations, including pairwise topological interactions between DNA segments, can hardly be considered in S-LDSC.

In 2020, the PI published the high-definition likelihood (HDL) method in Nature Genetics, improving the estimation precision of LD score regression, however, the stratified likelihood model is yet to be developed.

Following the demand of the research community, this project aims to develop the stratified high-definition likelihood (S-HDL) method, including both the statistical genetics model and an efficient computational algorithm. The method will be evaluated by both simulations and real data applications.

The new method will identify significantly more functional enrichment results for human complex diseases, so that will better reveal the underlying genetic architecture and disease etiology.