Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Area of Science:

• Genetics
• Statistical Genetics
• Genomic Research

Background:

• Genetic association analyses of rare variants in next-generation sequencing (NGS) studies face challenges due to high dimensionality and low minor allele frequencies.
• Current methods often pool variants for gene-level analysis, but pinpointing individual causal variants remains critical for understanding disease mechanisms.
• Standard false-positive control methods like Bonferroni and false discovery rate (FDR) are often impractical for rare variant studies.

Purpose of the Study:

• To propose a novel statistical procedure, Adaptive False-Negative Control (AFNC), for informative analysis of individual rare variants in large-scale sequencing studies.
• To develop a method that can confidently include a large proportion of causal variants while identifying noncausal variants.
• To provide a computationally efficient and adaptable framework for rare variant association analysis.

Main Methods:

• The Adaptive False-Negative Control (AFNC) procedure was developed, incorporating a novel statistical inquiry to confidently dispatch noncausal variants.
• AFNC offers a general framework adaptable to various statistical models and significance tests.
• The procedure's performance was evaluated through extensive simulation studies across diverse scenarios and applied to the CoLaus dataset.

Main Results:

• Extensive simulations demonstrated that AFNC is advantageous for identifying individual rare variants, unlike the overly conservative Bonferroni and FDR methods.
• AFNC successfully identified individual variants responsible for gene-level significances in the CoLaus dataset.
• The single-variant results obtained using AFNC were effectively applied to infer related genes with annotation information.

Conclusions:

• The Adaptive False-Negative Control (AFNC) procedure provides a powerful and practical approach for identifying individual rare variants in next-generation sequencing studies.
• AFNC overcomes the limitations of traditional methods, offering improved confidence in detecting causal variants.
• This method facilitates a more precise understanding of the genetic architecture of diseases by pinpointing specific variants and their associated genes.