Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes

TL;DR

This paper reviews modern retrospective likelihood methods for case-control genetic association studies, highlighting their advantages over classical approaches, and introduces novel score-tests, pseudo-likelihoods, and a two-stage analysis method for untyped SNPs.

Contribution

It introduces new statistical methods and a two-stage approach for analyzing untyped SNPs in case-control studies, improving power and flexibility.

Findings

Retrospective likelihood methods outperform classical logistic regression in power.

Novel score-tests and pseudo-likelihoods enhance association analysis.

Two-stage method effectively analyzes untyped SNPs using genotype imputation.

Abstract

Although prospective logistic regression is the standard method of analysis for case-control data, it has been recently noted that in genetic epidemiologic studies one can use the ``retrospective'' likelihood to gain major power by incorporating various population genetics model assumptions such as Hardy-Weinberg-Equilibrium (HWE), gene-gene and gene-environment independence. In this article we review these modern methods and contrast them with the more classical approaches through two types of applications (i) association tests for typed and untyped single nucleotide polymorphisms (SNPs) and (ii) estimation of haplotype effects and haplotype-environment interactions in the presence of haplotype-phase ambiguity. We provide novel insights to existing methods by construction of various score-tests and pseudo-likelihoods. In addition, we describe a novel two-stage method for analysis of untyped SNPs that can use any flexible external algorithm for genotype imputation followed by a powerful association test based on the retrospective likelihood. We illustrate applications of the methods using simulated and real data.