Yeager, M., et al. [(2007) Nature Genetics 39:645–649] and Sladek, R., et al. [(2007) Nature 445:881–885] have used single-nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS) to identify novel risk loci for prostate cancer and Type 2 diabetes, respectively. Each study suggests that disease-risk genes can be identified that significantly contribute to the disease state. Given your understanding of such complex diseases, what would you determine as reasonable factors to consider when interpreting the results of GWAS?

Verified step by step guidance

1. Understand that Genome-Wide Association Studies (GWAS) identify associations between single-nucleotide polymorphisms (SNPs) and diseases by scanning the genome for variants more frequent in affected individuals compared to controls.
2. Consider the effect size of identified SNPs, recognizing that many SNPs associated with complex diseases often have small individual effects and contribute modestly to overall disease risk.
3. Evaluate the population structure and sample size used in the GWAS, as population stratification and insufficient sample sizes can lead to false positives or reduce the power to detect true associations.
4. Assess the biological relevance of the identified loci by examining whether the SNPs are located in or near genes with known functions related to the disease or in regulatory regions that might affect gene expression.
5. Remember that GWAS results show correlation, not causation, so further functional studies and replication in independent cohorts are necessary to confirm the role of identified SNPs in disease risk.

Key Concepts

Single-Nucleotide Polymorphisms (SNPs)

SNPs are variations at a single DNA base pair among individuals and serve as genetic markers in GWAS. They help identify regions of the genome associated with disease risk by comparing SNP frequencies between affected and unaffected groups. Understanding SNPs is crucial for interpreting how genetic variation contributes to complex diseases.

Genome-Wide Association Studies (GWAS)

GWAS analyze many SNPs across the genome to find genetic variants linked to diseases. They require large sample sizes and statistical rigor to detect associations, but identified loci often have small effect sizes and may not directly cause disease. Recognizing GWAS limitations helps in evaluating the significance and biological relevance of findings.

Complex Disease Genetics and Interpretation Factors

Complex diseases result from multiple genetic and environmental factors interacting. When interpreting GWAS results, consider population stratification, linkage disequilibrium, effect size, replication of findings, and gene-environment interactions. These factors influence the reliability and applicability of identified risk loci to disease mechanisms.