Main content

Imputing Genotypes Using Regularized Generalized Linear Regression Models

Show full item record

Title: Imputing Genotypes Using Regularized Generalized Linear Regression Models
Author: Griesman, Joshua
Department: Department of Mathematics and Statistics
Program: Bioinformatics
Advisor: Feng, Zeny
Abstract: As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits (Hirschhorn and Daly, 2005). However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted GLMNET is proposed to impute missing genotypes. The method aimed to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly multicollinearity among predictors. The performance of GLMNET-based method is compared to the performance of the phase-based method fastPHASE. Two simulation settings were evaluated: a sparse-missing model, and a small-panel expan- sion model. The sparse-missing model simulated a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of test individuals that were only genotyped at a small subset of the SNPs of the large panel. Each imputation method was tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Although the proposed method was able to perform with high accuracy (>90% in all simulations), fastPHASE per- formed with higher accuracy (>94%). However, the new method, which was coded in R, was able to impute genotypes with better time efficiency than fastPHASE and this could be further improved by optimizing in a compiled language.
Date: 2012-05
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.

Files in this item

Files Size Format View Description

This item appears in the following Collection(s)

Show full item record