The large-scale genome-wide association studies (GWASs) have detected tens of thousands of risk variants underlying complex phenotypes. However, there are still outstanding challenges that hamper the clinical translation and biological interpretation of GWAS discoveries. In this thesis, we propose two statistical methods to address challenges in these two directions.

In clinical applications, the development of polygenic-risk-scores (PRSs) has proved useful to stratify the general population into different risk groups for the European population. However, PRS is less accurate in non-European populations due to genetic differences across different populations. The differences in genetic architectures between populations arise from three aspects. First, variants with biologically importan...[ Read more ]

The large-scale genome-wide association studies (GWASs) have detected tens of thousands of risk variants underlying complex phenotypes. However, there are still outstanding challenges that hamper the clinical translation and biological interpretation of GWAS discoveries. In this thesis, we propose two statistical methods to address challenges in these two directions.

In clinical applications, the development of polygenic-risk-scores (PRSs) has proved useful to stratify the general population into different risk groups for the European population. However, PRS is less accurate in non-European populations due to genetic differences across different populations. The differences in genetic architectures between populations arise from three aspects. First, variants with biologically important roles in the non-European populations may be neglected in GWAS if they are absent or have very low allele frequencies in Europeans. Second, the same variant may have different effect sizes on the same phenotype across different populations, limiting the extrapolation value of GWAS findings and the GWAS-derived PRS power in non-discovery populations. Third, the linkage disequilibrium (LD) patterns vary across populations, exacerbating the bias in extrapolating the PRS for risk prediction.

To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3% − 198.0% accuracy gain for height and 19.5% − 313.3% accuracy gain for body mass index (BMI) in terms of predictive R² compared to existing PRS approaches, respectively. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRS in the African population, suggesting the generality of our framework across global populations.

For a better biological interpretation of GWAS discoveries, integrative analysis of multi-omics data have been conducted to implicate biological insights. By leveraging existing GWAS and transcriptomic information, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically-regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference transcriptomic data, which depends on the cellular conditions of the transcriptomic data.

Considering the increasing availability of transcriptomic data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference transcriptomic panel and individual-level or summary-level GWAS data. Using transcriptomic data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans expression quantitative trait loci (trans-eQTL) and analyzing genetically-regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.