Relationship estimation from whole-genome sequence data.
The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.
Institute for Systems Biology, Seattle, Washington, United States of America.
Hong
H
Li
Institute for Systems Biology, Seattle, Washington, United States of America.
Gustavo
G
Glusman
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Hao
H
Hu
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Shankaracharya
Institute for Systems Biology, Seattle, Washington, United States of America.
Juan
J
Caballero
Institute for Systems Biology, Seattle, Washington, United States of America.
Robert
R
Hubley
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
David
D
Witherspoon
Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Stephen L
SL
Guthery
Institute for Systems Biology, Seattle, Washington, United States of America.
Denise E
DE
Mauldin
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Lynn B
LB
Jorde
Institute for Systems Biology, Seattle, Washington, United States of America.
Leroy
L
Hood
Institute for Systems Biology, Seattle, Washington, United States of America.
Jared C
JC
Roach
Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Chad D
CD
Huff
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D008040:Genetic Linkage; D005828:Genetics, Population; D015894:Genome, Human; D023281:Genomics; D018095:Germ-Line Mutation / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software