Evidence map›Paper›PMID 24398795›Full record

ArticleEuropean journal of human genetics : EJHG2014

Analysis of rare variant population structure in Europeans explains differential stratification of gene-based tests.

Matthew Zawistowski, Mark Reppell, Daniel Wegmann, Pamela L St Jean, Margaret G Ehm, Matthew R Nelson, John Novembre, Sebastian Zöllner

Abstract read
In one paragraph

Article in European journal of human genetics : EJHG, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage.Proceedings of the National Academy of Sciences of the United States of America · 2016
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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

8 authors.

Matthew ZawistowskiDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Mark ReppellDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Daniel WegmannDepartment of Biology, University of Fribourg, Fribourg, Switzerland.
Pamela L St JeanQuantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
Margaret G EhmQuantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
Matthew R NelsonQuantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA.
John NovembreDepartment of Human Genetics, University of Chicago, Chicago, IL, USA.
Sebastian Zöllner1] Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA [2] Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
DESIGN AND ANALYSIS OF HUMAN GENE MAPPING STUDIESR01HG000376 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 1993 to 2016
$5.5M
Haplotype-based analysis methods for population genomicsR01HG007089 · NHGRI · UNIVERSITY OF CHICAGO · PI NOVEMBRE, JOHN · 2013 to 2017
$1.5M
Design and Analysis of Human Gene Mapping StudiesR56HG000376 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 2017 to 2017
$400k
DESIGN ISSUES IN GENETIC LINKAGE AND SEGREGATION STUDIESR29HG000376 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL · 1990 to 1992
–
The Lowe Oculocerebrorenal SyndromeZ01HG000040 · NHGRI · HUMAN GENOME RESEARCH · PI NUSSBAUM, ROBERT L · 1995 to 2005
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Intramural NIH HHS Z01 HG000040NHGRI NIH HHS HG000040NHGRI NIH HHS HG000376NHGRI NIH HHS HG005855NHGRI NIH HHS HG007089NHGRI NIH HHS R01 HG000376NHGRI NIH HHS R01 HG005855NHGRI NIH HHS R01 HG007089NHGRI NIH HHS R56 HG000376NHGRI NIH HHS T32 HG000040
6 · The paper itself

Abstract

There is substantial interest in the role of rare genetic variants in the etiology of complex human diseases. Several gene-based tests have been developed to simultaneously analyze multiple rare variants for association with phenotypic traits. The tests can largely be partitioned into two classes - 'burden' tests and 'joint' tests - based on how they accumulate evidence of association across sites. We used the empirical joint site frequency spectra of rare, nonsynonymous variation from a large multi-population sequencing study to explore the effect of realistic rare variant population structure on gene-based tests. We observed an important difference between the two test classes: their susceptibility to population stratification. Focusing on European samples, we found that joint tests, which allow variants to have opposite directions of effect, consistently showed higher levels of P-value inflation than burden tests. We determined that the differential stratification was caused by two specific patterns in the interpopulation distribution of rare variants, each correlating with inflation in one of the test classes. The pattern that inflates joint tests is more prevalent in real data, explaining the higher levels of inflation in these tests. Furthermore, we show that the different sources of inflation between tests lead to heterogeneous responses to genomic control correction and the number of variants analyzed. Our results indicate that care must be taken when interpreting joint and burden analyses of the same set of rare variants, in particular, to avoid mistaking inflated P-values in joint tests for stronger signals of true associations.

Indexed as

Gene FrequencyModels, GeneticData Interpretation, StatisticalGenetic TestingHumansPolymorphism, GeneticWhite People

Identifiers

PMID24398795
PMCPMC4135410

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.