Evidence map›Paper›PMID 27980637›Full record

ArticleBMC proceedings2016

Transmission and decorrelation methods for detecting rare variants using sequencing data from related individuals.

Burcu F Darst, Corinne D Engelman

Abstract read
In one paragraph

Article in BMC proceedings, 2016. 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

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2 · The registry

The trial behind it

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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

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5 · Who and what money

Authors and funding

2 authors.

Burcu F DarstUniversity of Wisconsin, Madison, WI USA ; Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI USA.
Corinne D EngelmanUniversity of Wisconsin, Madison, WI USA ; Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI USA.

Funding

Research Training for Computation and Informatics in Biology and MedicineT15LM007359 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI Mark W. Craven, Colin Noel Dewey · 2002 to 2026
$22.6M
Statistical Data Enclave CoreP30AG017266 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Michal Engelman · 1999 to 2026
$17.0M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Center for Demography and EcologyR24HD047873 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI RAYMO, JAMES M · 2004 to 2013
$7.2M
Scientific and Technical CoreP2CHD047873 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Katherine J. Curtis · 2014 to 2026
$5.1M
NIA NIH HHS P30 AG017266NICHD NIH HHS P2C HD047873NICHD NIH HHS R24 HD047873NIGMS NIH HHS R01 GM031575NLM NIH HHS T15 LM007359
6 · The paper itself

Abstract

backgroundAdvances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods to detect associations with rare variants are developed for unrelated individuals; however, several methods exist that utilize family studies and could have better power to detect such associations.

methodsUsing whole genome sequencing data and simulated phenotypes provided by the organizers of the Genetic Analysis Workshop 19 (GAW19), we compared family-based methods that test for associations between rare and common variants with a quantitative trait. This was done using 2 fairly novel methods: family-based association test for rare variants (FBAT-RV), which is a transmission-based method that utilizes the transmission of genetic information from parent to offspring; and Minimum

resultsMONSTER had much higher overall power than FBAT-RV and FBAT-Min P. Interestingly, FBAT-LC had similar overall power as MONSTER. MONSTER had the highest power for a gene accounting for a larger percent of the phenotypic variance, whereas MONSTER and FBAT-LC both had the highest power for a gene accounting for moderate variance. FBAT-LC had the highest power for a gene accounting for the least variance.

conclusionsBased on the simulated data from GAW19, MONSTER and FBAT-LC were the most powerful of the methods assessed. However, there are limitations to each of these methods that should be carefully considered when conducting an analysis of rare variants in related individuals. This emphasizes the need for methods that can incorporate the advantages of each of these methods into 1 family-based association test for rare variants.

Identifiers

PMID27980637
PMCPMC5133523

What Socratic holds

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LicenceCC BY
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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 Socratic graph.