Evidence map›Paper›PMID 25044337›Full record

ArticleGenetic epidemiology2014

Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families.

Yunxuan Jiang, Karen N Conneely, Michael P Epstein

Open access · greenAbstract read
In one paragraph

Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Yunxuan JiangDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
Karen N Conneely
Michael P Epstein
Emory University · US

Funding

Enhanced Gene Identification in Complex Traits Using Kernel MachinesR01HG007508 · NHGRI · EMORY UNIVERSITY · PI EPSTEIN, MICHAEL PHILIP · 2013 to 2015
$1.0M
NHGRI NIH HHS R01 HG007508
6 · The paper itself

Abstract

Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., ] into the kernel-based SNP association test KMFAM of Schifano et al. [Schifano et al., ]. The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of P-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. Although within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.

Indexed as

Genetic VariationQuantitative Trait LociChildGenome, HumanGenotypeHumansModels, GeneticNuclear FamilyParentsPedigreePhenotypePolymorphism, Single NucleotideSoftwarefamily studiesquantitative traitsrare variantssequencing

Identifiers

PMID25044337
PMCPMC4138536
OpenAlexW1877277349

What Socratic holds

Textmetadata
LicenceTDM
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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 Socratic graph.