A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers

Area of Science:

• Genetics
• Genomics
• Bioinformatics

Background:

• Copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) are key genetic factors influencing human traits.
• Existing methods for analyzing these variants in association studies have limitations, including susceptibility to false positives and ignoring call uncertainties.
• Allele-specific copy numbers (ASCNs) capture both CNV and SNP information, but their analysis requires integrated approaches.

Purpose of the Study:

• To develop a unified statistical framework for the integrated analysis of CNVs and SNPs (ASCNs) in complex disease association studies.
• To address limitations of current methods, such as differential measurement errors and ignored uncertainties in variant calls.
• To provide a robust method applicable to genome-wide association studies (GWAS).

Main Methods:

• Developed a general framework combining ASCN calling and association analysis into a single step.
• Constructed likelihood functions accounting for case-control sampling and measurement errors.
• Employed maximum likelihood estimation and expectation-maximization (EM) algorithms for inference.

Main Results:

• The proposed integrated method effectively reduces false-positive findings compared to traditional two-step approaches.
• Demonstrated the method's advantages through realistic simulation studies.
• Successfully applied the framework to a genome-wide association study of schizophrenia.

Conclusions:

• The integrated analysis framework offers a more accurate and reliable approach for studying complex diseases influenced by both CNVs and SNPs.
• The method accounts for potential biases from differential measurement errors and variant call uncertainties.
• The framework is extendable to next-generation sequencing data, broadening its applicability in genetic research.