Evidence map›Paper›PMID 24497848›Full record

ArticlePLoS genetics2014

Relationship estimation from whole-genome sequence data.

Hong Li, Gustavo Glusman, Hao Hu, Shankaracharya, Juan Caballero, Robert Hubley, David Witherspoon, Stephen L Guthery, Denise E Mauldin, Lynn B Jorde and 3 more

Abstract read
In one paragraph

Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed.

  1. Article
  2. Article
  3. Complex kinship testing using the MGIEasy Pa-SNPs genotyping kit.International journal of legal medicine · 2025
    Article
  4. Article
  5. Estimating realized relatedness in free-ranging macaques by inferring identity-by-descent segments.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Parent-offspring inference in inbred populations.Molecular ecology resources · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Host genetic effects in pneumonia.American journal of human genetics · 2021
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hong LiInstitute for Systems Biology, Seattle, Washington, United States of America.
Gustavo GlusmanInstitute for Systems Biology, Seattle, Washington, United States of America.
Hao HuDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
ShankaracharyaDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Juan CaballeroInstitute for Systems Biology, Seattle, Washington, United States of America.
Robert HubleyInstitute for Systems Biology, Seattle, Washington, United States of America.
David WitherspoonDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Stephen L GutheryDepartment of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Denise E MauldinInstitute for Systems Biology, Seattle, Washington, United States of America.
Lynn B JordeDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Leroy HoodInstitute for Systems Biology, Seattle, Washington, United States of America.
Jared C RoachInstitute for Systems Biology, Seattle, Washington, United States of America.
Chad D HuffDepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

Funding

LCenter for Clinical and Translational Science and TrainingUL1TR001425 · NCATS · UNIVERSITY OF CINCINNATI · PI HEUBI, JAMES E., KISSELA, BRETT M · 2015 to 2024
$37.5M
POPULATION GENETICS OF MOBILE ELEMENTSR01GM059290 · NIGMS · UNIVERSITY OF UTAH · PI BATZER, MARK A, FESCHOTTE, CEDRIC · 1999 to 2018
$8.5M
Rare disease susceptibility alleles in children with Crohn diseaseR01DK091374 · NIDDK · UNIVERSITY OF UTAH · PI GUTHERY, STEPHEN L · 2011 to 2014
$1.3M
NCATS NIH HHS UL1 TR001425NIDDK NIH HHS R01 DK091374NIGMS NIH HHS GM59290NIGMS NIH HHS R01 GM059290
6 · The paper itself

Abstract

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.

Indexed as

Genetics, PopulationSoftwareAlgorithmsChromosome MappingGenetic LinkageGenome, HumanGenomicsGerm-Line MutationHigh-Throughput Nucleotide SequencingHumansPedigreePolymorphism, Single Nucleotide

Identifiers

PMID24497848
PMCPMC3907355

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.