Tantalizing dilemma in risk prediction from disease scoring statistics

Abstract

Over the past decade, human host genome-wide association studies (GWASs) have contributed greatly to our understanding of the impact of host genetics on phenotypes. Recently, the microbiome has been recognized as a complex trait in host genetic variation, leading to microbiome GWAS (mGWASs). For these, many different statistical methods and software tools have been developed for association mapping. Applications of these methods and tools have revealed several important findings; however, the establishment of causal factors and the direction of causality in the interactive role between human genetic polymorphisms, the microbiome and the host phenotypes are still a huge challenge. Here, we review disease scoring approaches in host and mGWAS and their underlying statistical methods and tools. We highlight the challenges in pinpointing the genetic-associated causal factors in host and mGWAS and discuss the role of multi-omic approach in disease scoring statistics that may provide a better understanding of human phenotypic variation by enabling further system biological experiment to establish causality.

Keywords: genome-wide association study; microbiome; microbiome GWAS; multi-omics.