Online Program Home
My Program

Abstract Details

Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305052
Title: Evaluation of Modern Approaches for the Complex Trait Prediction Using Genetic Data
Author(s): Miao Zhang* and Julie Granka
Companies: and
Keywords: polygenic risk score; phenotypic prediction; genetic architecture; high-dimensional data; GWAS

Genome-wide association studies (GWAS) have been widely used to discover the genetic basis of complex phenotypes. As such, GWAS provide starting points and potential opportunities for researchers to develop methods to predict complex traits. As a natural extension to GWAS, the polygenic risk score (PRS) is one of most popular methods for complex traits prediction in the genetics community. At the same time, other PRS extensions and alternatives have also been developed in the context of greater precision in prediction. To explore the advantages and disadvantages among various prediction methods under different genetic architectures, we leverage genotype data and phenotype data collected from surveys from a subset of over one million AncestryDNA customers who have consented to research. Our study shows that no specific method dominates across all traits, as each method has its merits in terms of model complexity and prediction accuracy under certain genetic architectures. While more in-depth research is still required, this work provides a sound foundation in the comparison of various statistical techniques for prediction.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program