Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2

Area of Science:

• Genomics and Evolutionary Biology
• Population Genetics
• Bioinformatics

Background:

• Gene duplication drives the evolution of novel gene functions and contributes significantly to genetic diversity through copy-number variation (CNV).
• Existing CNV detection methods often struggle with duplicated sequences and are computationally intensive, hindering analysis of large population datasets.
• Distinguishing between highly similar paralogous genes is a key challenge in accurately assessing CNV.

Purpose of the Study:

• To develop a rapid, mapping-free computational approach for constructing paralog-specific copy-number maps from short-read sequencing data.
• To enable efficient analysis of copy-number variation in paralogous genes across large populations.
• To identify novel instances of copy-number variation in human populations.

Main Methods:

• Developed QuicK-mer2, a self-contained, mapping-free bioinformatics tool.
• Utilizes tabulation of unique k-mer sequences from short-read data to identify paralog-specific copy numbers.
• Applied QuicK-mer2 to 2457 individuals from the 1000 Genomes Project dataset, analyzing a 20X human genome in approximately 20 minutes.

Main Results:

• Successfully constructed paralog-specific copy-number maps for 2457 individuals.
• Identified widespread copy-number variation in paralogous genes, including nine genes with no samples having a copy number of two.
• Discovered 92 genes with a majority of samples exhibiting a copy number other than two, and characterized rare CNV at the APOBEC3 locus.

Conclusions:

• QuicK-mer2 provides a fast and computationally efficient method for analyzing paralog-specific copy-number variation.
• The study reveals substantial uncharacterized copy-number variation in human paralogous genes.
• This approach facilitates the study of genetic diversity and evolutionary dynamics of gene families in population-scale genomics.