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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Comparing variant calling algorithms for target-exon sequencing in a large sample.

Yancy Lo¹, Hyun M Kang², Matthew R Nelson³

¹Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA. yancylo@umich.edu.

BMC Bioinformatics

|April 18, 2015

Summary

This summary is machine-generated.

Population-based and LD-aware calling methods offer high-quality variant calls with fewer missing genotypes in exonic sequencing studies. Individual-based analyses can identify more singleton variants when used alongside these methods.

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Area of Science:

Genomics
Human Genetics
Bioinformatics

Background:

Exonic sequencing studies aim to find rare variants linked to complex traits.
High coverage sequencing often uses simple variant calling, but heterogeneous coverage necessitates evaluating sophisticated methods for low-coverage sites.
This study compares different variant calling strategies on exonic data from a large cohort.

Purpose of the Study:

To evaluate the benefits of individual-based, population-based, and linkage disequilibrium (LD)-aware variant calling methods.
To compare genotype accuracy and completeness across different calling strategies.
To identify optimal methods for variant detection in exonic sequencing data.

Main Methods:

Comparative analysis of variant calling methods (individual-based, population-based, LD-aware) on exonic data from 7,842 individuals (202 genes, 24x coverage).
Genotype accuracy assessed by concordance with on-target GWAS genotypes and sequencing replicates.
Validation of selected singleton variants using capillary sequencing.

Main Results:

Over 27,500 variants detected, with >57% being singletons.
Individual-based analyses yielded the most high-quality singletons but had the highest missing genotype rates (4.72%).
LD-aware methods with extended haplotypes provided the most accurate and complete genotypes, outperforming population-based and individual-based methods in concordance and missing data.

Conclusions:

Population-based analyses are recommended for high-quality variant calls with minimal missing genotypes.
LD-aware methods, particularly with extended haplotypes, offer the most accurate and complete genotype data.
Individual-based analyses should complement population-based and LD-aware methods to maximize singleton variant discovery.