Leveraging pleiotropic association using sparse group variable selection in genomics data

Abstract

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis

“…Pervasive genetic correlations between diseases allowed MTL to improve the PRS estimation broadly across diseases. While many cross-strait studies have shown the genetic correlation between specific pairs of diseases [18][19][20][21][22][23][24], our study suggested that various degrees of shared genetic basis may be very prevalent among many complex diseases. Our results highlighted the potential value of holistic association studies between the whole human phenome and the whole human genome for both risk variant discovery and PRS estimation.…”

“…Many complex diseases share a substantial amount of common genetic determinants. Genome-wide cross-trait analyses have been performed between obesity and cardiovascular diseases [18], between thyroid and breast cancers [19], between uterine leiomyoma and breast cancer [20], between asthma and cardiovascular diseases [21], between Alzheimer's disease and gastrointestinal tract disorders [22], between Alzheimer disease and major depressive disorder [23], between lung cancer and chronic bronchitis [24], and so on. These studies were often motivated by frequent co-occurrences of pairs of diseases in a population.…”