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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: Mar 28, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Exploratory Failure Time Analysis in Large Scale Genomics.

¹Department of Biostatistics, St. Jude Children's Research Hospital 262 Danny Thomas Place, Memphis, TN 38105-2794, USA.

Computational Statistics & Data Analysis

|December 19, 2015

Summary

This summary is machine-generated.

A new correlation profile test (CPT) offers a robust method for genomic association studies, improving analysis of failure-time data with censoring and competing risks. This statistically sound approach enhances genotype-phenotype association detection.

Keywords:

Censored failure time data Correlation Profile Test Exploratory analysis Failure event point process GWAS Hybrid permutation test Large scale genomic analysis Stochastically monotone dependence genotype-phenotype association

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Area of Science:

Genomics
Biostatistics
Survival Analysis

Background:

Genomic analyses like genome-wide association studies (GWAS) require robust statistical methods for genotype-phenotype association detection.
Existing methods for analyzing failure-time phenotypes with censoring and competing risks can have limitations in numerical stability and assumption violations.

Purpose of the Study:

To develop and evaluate a novel statistical test procedure, the correlation profile test (CPT), for detecting genomic associations.
To specifically address challenges in analyzing failure-time phenotypes subject to right censoring and competing risks within large-scale genomic studies.

Main Methods:

Development of the correlation profile test (CPT) based on sample moments.
Performance evaluation through simulation studies and analysis of a real genomic dataset.
Comparison of CPT against existing semiparametric and nonparametric methods.

Main Results:

CPT demonstrates numerical robustness by relying solely on sample moments.
CPT shows increased robustness against violations of the proportional hazards condition.
CPT offers greater flexibility in handling diverse failure and censoring scenarios compared to existing methods.

Conclusions:

The correlation profile test (CPT) provides a numerically and statistically robust approach for genomic association studies with complex failure-time data.
CPT is a versatile tool applicable beyond genomics for testing stochastic independence between event point processes and random variables.