Evidence map›Paper›PMID 31018026›Full record

ArticleHuman mutation2019

Heterozygosity mapping for human dominant trait variants.

Atsuko Imai-Okazaki, Yi Li, Sukanya Horpaopan, Yasser Riazalhosseini, Masoud Garshasbi, Yael P Mosse, Di Zhang, Isabelle Schrauwen, Aarushi Sharma, Cathy S J Fann and 3 more

Abstract read
In one paragraph

Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Atsuko Imai-OkazakiDiagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Yi LiSchool of Statistics, Shanxi University of Finance and Economics, Taiyuan, China.
Sukanya HorpaopanDepartment of Anatomy, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand.
Yasser RiazalhosseiniMcGill University and Genome Québec Innovation Centre, Montréal, Québec, Canada.
Masoud GarshasbiDepartment of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Yael P MosseDivision of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, Philadelphia.
Di ZhangCenter for Statistical Genetics, Baylor College of Medicine, Houston, Texas.
Isabelle SchrauwenCenter for Statistical Genetics, Baylor College of Medicine, Houston, Texas.
Aarushi SharmaInstitute of Biotechnology, Amity University, Gwalior, Madhya Pradesh, India.
Cathy S J FannInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Suzanne M LealCenter for Statistical Genetics, Baylor College of Medicine, Houston, Texas.
Mark LathropMcGill University and Genome Québec Innovation Centre, Montréal, Québec, Canada.
Jurg OttLaboratory of Statistical Genetics, Rockefeller University, New York, New York.ORCID 0000-0002-6188-1388

Funding

UW Center for Mendelian GenomicsU54HG006493 · NHGRI · UNIVERSITY OF WASHINGTON · PI BAMSHAD, MICHAEL JOSEPH, NICKERSON, DEBORAH A · 2012 to 2015
$20.0M
UW Center for Mendelian GenomicsUM1HG006493 · NHGRI · UNIVERSITY OF WASHINGTON · PI BAMSHAD, MICHAEL JOSEPH, LEAL, SUZANNE M · 2016 to 2020
$15.3M
Identification of Nonsyndromic Hearing Impairment GenesR01DC003594 · NIDCD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE M LEAL · 2004 to 2026
$7.7M
Family-based methods to analyze sequence data to elucidate AD etiologyRF1AG058131 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEAL, SUZANNE M · 2018 to 2018
$3.0M
Localization of Nonsyndromic Hearing Impairment GenesR01DC011651 · NIDCD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEAL, SUZANNE M · 2011 to 2020
$2.9M
Computational tools for sequence-based large-scale epidemiology studiesR01HG008972 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEAL, SUZANNE M, PENG, BO · 2016 to 2019
$1.6M
ISOLATION OF NON SYNDROMIC HEARING LOSS GENESR29DC003594 · NIDCD · ROCKEFELLER UNIVERSITY · PI LEAL, SUZANNE MARGARET · 1998 to 2002
$345k
Medical Research Council G9815508Medical Research Council MC_PC_19009NHGRI NIH HHS R01 HG008972NHGRI NIH HHS U54 HG006493NHGRI NIH HHS UM1 HG006493NIA NIH HHS RF1 AG058131NIDCD NIH HHS R01 DC003594NIDCD NIH HHS R01 DC011651NIDCD NIH HHS R29 DC003594NIH HHS DC003594NIH HHS DC011651Wellcome Trust
6 · The paper itself

Abstract

Homozygosity mapping is a well-known technique to identify runs of homozygous variants that are likely to harbor genes responsible for autosomal recessive disease, but a comparable method for autosomal dominant traits has been lacking. We developed an approach to map dominant disease genes based on heterozygosity frequencies of sequence variants in the immediate vicinity of a dominant trait. We demonstrate through theoretical analysis that DNA variants surrounding an inherited dominant disease variant tend to have increased heterozygosity compared with variants elsewhere in the genome. We confirm existence of this phenomenon in sequence data with known dominant pathogenic variants obtained on family members and in unrelated population controls. A computer-based approach to estimating empirical significance levels associated with our test statistics shows genome-wide p-values smaller than 0.05 for many but not all of the individuals carrying a pathogenic variant.

Indexed as

HeterozygoteChromosome MappingComputational BiologyGenetic Predisposition to DiseaseGenetic VariationHumansModels, GeneticALSPACcomputer simulationgene mappinggenetic association analysissequence variants

Identifiers

PMID31018026
PMCPMC6617796

What OpenQuestion holds

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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.