학술논문

A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data
Document Type
article
Source
Journal of Heredity. 114(5)
Subject
Biological Sciences
Bioinformatics and Computational Biology
Genetics
Cancer
Human Genome
Genetic Testing
Networking and Information Technology R&D (NITRD)
Biotechnology
1.1 Normal biological development and functioning
Underpinning research
Genotype
Genome
Genomics
Sequence Analysis
DNA
DNA
Polymorphism
Single Nucleotide
High-Throughput Nucleotide Sequencing
DNA identification
genome
identity-by-descent
Evolutionary Biology
Evolutionary biology
Language
Abstract
Several methods exist for detecting genetic relatedness or identity by comparing DNA information. These methods generally require genotype calls, either single-nucleotide polymorphisms or short tandem repeats, at the sites used for comparison. For some DNA samples, like those obtained from bone fragments or single rootless hairs, there is often not enough DNA present to generate genotype calls that are accurate and complete enough for these comparisons. Here, we describe IBDGem, a fast and robust computational procedure for detecting genomic regions of identity-by-descent by comparing low-coverage shotgun sequence data against genotype calls from a known query individual. At less than 1× genome coverage, IBDGem reliably detects segments of relatedness and can make high-confidence identity detections with as little as 0.01× genome coverage.