Evidence map›Paper›PMID 32197076›Full record

ArticleAmerican journal of human genetics2020

Rapid, Phase-free Detection of Long Identity-by-Descent Segments Enables Effective Relationship Classification.

Daniel N Seidman, Sushila A Shenoy, Minsoo Kim, Ramya Babu, Ian G Woods, Thomas D Dyer, Donna M Lehman, Joanne E Curran, Ravindranath Duggirala, John Blangero and 1 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
–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

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  5. Census and genetic analysis of the United States marmoset population.bioRxiv : the preprint server for biology · 2026
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  19. 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
  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

11 authors.

Daniel N SeidmanDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Sushila A ShenoyDepartment of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Minsoo KimDepartment of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
Ramya BabuDepartment of Computer Science, Cornell University, Ithaca, NY 14853, USA.
Ian G WoodsDepartment of Biology, Ithaca College, Ithaca, NY 14850, USA.
Thomas D DyerSouth Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA.
Donna M LehmanDepartment of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
Joanne E CurranSouth Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA.
Ravindranath DuggiralaSouth Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA.
John BlangeroSouth Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA.
Amy L WilliamsDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA. Electronic address: alw289@cornell.edu.

Funding

SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Construction of a Biomedical Research Facility at the U*C06RR020547 · NCRR · UNIV/TEXAS BROWNSVILLE & SOUTHMOST COLL · PI KROUSE, JOHN H · 2010 to 2010
$4.0M
Tri-Institutional Training Program in Computational Biology & MedicineT32GM083937 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHRISTINI, DAVID J. · 2009 to 2018
$3.1M
Scalable methods for the characterization and analysis of families in large genomic datasetsR35GM133805 · NIGMS · CORNELL UNIVERSITY · PI WILLIAMS, AMY LYNNE · 2019 to 2023
$1.6M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCRR NIH HHS C06 RR020547NIBIB NIH HHS R01 EB015611NIGMS NIH HHS R35 GM133805NIGMS NIH HHS T32 GM083937Wellcome Trust 076113Wellcome Trust 085475Wellcome Trust 090355
6 · The paper itself

Abstract

Identity-by-descent (IBD) segments are a useful tool for applications ranging from demographic inference to relationship classification, but most detection methods rely on phasing information and therefore require substantial computation time. As genetic datasets grow, methods for inferring IBD segments that scale well will be critical. We developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals, and benchmarked it with Refined IBD, GERMLINE, and TRUFFLE on 3,000 simulated individuals. Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE segment detection in 2.9 or 1.1 h, respectively. By comparison, IBIS finishes in 6.8 min or 7.8 min with IBD2 functionality enabled: speedups of 805-946× including phasing time. TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3×. IBIS is also accurate, inferring ≥7 cM IBD segments at quality comparable to Refined IBD and GERMLINE. With these segments, IBIS classifies first through third degree relatives in real Mexican American samples at rates meeting or exceeding other methods tested and identifies fourth through sixth degree pairs at rates within 0.0%-2.0% of the top method. While allele frequency-based approaches that do not detect segments can infer relationship degrees faster than IBIS, the fastest are biased in admixed samples, with KING inferring 30.8% fewer fifth degree Mexican American relatives correctly compared with IBIS. Finally, we ran IBIS on chromosome 2 of the UK Biobank dataset and estimate its runtime on the autosomes to be 3.3 days parallelized across 128 cores.

Indexed as

AllelesChromosomes, Human, Pair 2Gene FrequencyGenome, HumanHumansModels, GeneticPolymorphism, Single NucleotideSequence AnalysisIBDidentical by descent segmentsidentity by descentrelatedness inferenceunphased genotypes

Identifiers

PMID32197076
PMCPMC7118564

What OpenQuestion holds

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Registered trials

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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.