Evidence map›Paper›PMID 29334894›Full record

ArticleBMC genetics2018

Rare variant association analysis in case-parents studies by allowing for missing parental genotypes.

Yumei Li, Yang Xiang, Chao Xu, Hui Shen, Hongwen Deng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yumei LiSchool of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan, 418008, People's Republic of China. lymmail@126.com.ORCID 0000-0003-1755-4504
Yang XiangSchool of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan, 418008, People's Republic of China.
Chao XuCenter for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA.
Hui ShenCenter for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA.
Hongwen DengCenter for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA. hdeng2@tulane.edu.

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Tony Y. Hu · 2016 to 2026
$25.3M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Identification of Proteins Important for Male OsteoporosisR01AR057049 · NIAMS · UNIVERSITY OF MISSOURI KANSAS CITY · PI DENG, HONG-WEN · 2009 to 2013
$2.4M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
Research Training in Gene-Environment Interaction in ChinaD43TW009107 · FIC · TULANE UNIVERSITY OF LOUISIANA · PI GU, DONGFENG, HE, JIANG · 2012 to 2016
$1.1M
FIC NIH HHS D43 TW009107NIAMS NIH HHS R01 AR057049NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIGMS NIH HHS P20 GM109036NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354
6 · The paper itself

Abstract

backgroundThe development of next-generation sequencing technologies has facilitated the identification of rare variants. Family-based design is commonly used to effectively control for population admixture and substructure, which is more prominent for rare variants. Case-parents studies, as typical strategies in family-based design, are widely used in rare variant-disease association analysis. Current methods in case-parents studies are based on complete case-parents data; however, parental genotypes may be missing in case-parents trios, and removing these data may lead to a loss in statistical power. The present study focuses on testing for rare variant-disease association in case-parents study by allowing for missing parental genotypes.

resultsIn this report, we extended the collapsing method for rare variant association analysis in case-parents studies to allow for missing parental genotypes, and investigated the performance of two methods by using the difference of genotypes between affected offspring and their corresponding "complements" in case-parent trios and TDT framework. Using simulations, we showed that, compared with the methods just only using complete case-parents data, the proposed strategy allowing for missing parental genotypes, or even adding unrelated affected individuals, can greatly improve the statistical power and meanwhile is not affected by population stratification.

conclusionsWe conclude that adding case-parents data with missing parental genotypes to complete case-parents data set can greatly improve the power of our strategy for rare variant-disease association.

Indexed as

Genetic Association StudiesGenotypePedigreeComputer SimulationGene FrequencyGenetic Predisposition to DiseaseHumansModels, GeneticCase-parent triosCollapsing methodRare-variant association analysis

Identifiers

PMID29334894
PMCPMC5769338

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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.