Evidence map›Paper›PMID 30104420›Full record

ArticleGenetics2018

Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method.

Ming Li, Zihuai He, Xiaoran Tong, John S Witte, Qing Lu

Open access · bronzeAbstract read
In one paragraph

Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 27% of its field
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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
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  5. 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

5 authors at 4 institutions in 1 country.

Ming LiDepartment of Epidemiology and Biostatistics, Indiana University at Bloomington, Indiana 47405.
Zihuai HeDepartment of Biostatistics, Columbia University, New York, New York 10032.
Xiaoran TongDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.
John S WitteDepartment of Epidemiology and Biostatistics, University of California at San Francisco, California 94143.
Qing LuDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 qlu@epi.msu.edu.ORCID 0000-0002-7943-966X
Michigan State University · USColumbia University · USIndiana University Bloomington · USUniversity of California, San Francisco · US

Funding

Indiana Clinical and Translational Sciences InstituteUL1TR001108 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$23.3M
Genome-wide Pleiotropy Scan across Multiple CancersR01CA201358 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, SAKODA, LORI · 2016 to 2019
$2.5M
Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing DataR01DA043501 · NIDA · UNIVERSITY OF FLORIDA · PI LU, QING · 2017 to 2021
$2.1M
Methods and Software for High-dimensional Risk Prediction ResearchR01LM012848 · NLM · UNIVERSITY OF FLORIDA · PI LU, QING · 2018 to 2021
$1.2M
Random Field Modelling of genetic and epigenetic association underlying congenital heart defects in the presence of disease heterogeneityK01HL140333 · NHLBI · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2021
$724k
Gene-by-Gene and Gene-by-Environment Interactions Underlying Congenital Heart DefectsR03HD092854 · NICHD · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2019
$171k
NCATS NIH HHS UL1 TR001108NCI NIH HHS R01 CA201358NHLBI NIH HHS K01 HL140333NICHD NIH HHS R03 HD092854NIDA NIH HHS R01 DA043501NLM NIH HHS R01 LM012848
6 · The paper itself

Abstract

The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (

Indexed as

Genetic HeterogeneityGenetic Predisposition to DiseaseModels, GeneticMutation RatePedigreeAlcoholismAlgorithmsHumansPhenotypealcohol dependencefamily-based association studygenetic heterogeneitypopulation stratificationrare variants

Identifiers

PMID30104420
PMCPMC6216585
OpenAlexW2885726782

What OpenQuestion holds

Textmetadata
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.