Evidence map›Paper›PMID 17668381›Full record

ArticleAmerican journal of human genetics2007

Case-control association testing with related individuals: a more powerful quasi-likelihood score test.

Timothy Thornton, Mary Sara McPeek

Abstract read
In one paragraph

Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 4 pooled it
–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

116 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. A genome-wide association study on African-ancestry populations for asthma.The Journal of allergy and clinical immunology · 2010
    Pooled it
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  19. Outcomes of 92 patient-driven family studies for reclassification of variants of uncertain significance.Genetics in medicine : official journal of the American College of Medical Genetics · 2019
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56 more citing papers are in PubMed but not listed here.

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

2 authors.

Timothy ThorntonDepartment of Statistics, University of Chicago, Chicago, IL 60637, USA.
Mary Sara McPeek

Funding

Subject CollectionU10AA008401 · NIAAA · SUNY DOWNSTATE MEDICAL CENTER · PI JESSICA E SALVATORE · 1989 to 2026
$162.7M
Methods for Human Genetic MappingR01HG001645 · NHGRI · UNIVERSITY OF CHICAGO · PI MCPEEK, MARY SARA · 2003 to 2023
$6.8M
Genome-wide Association Mapping of Metabolic, Lung and Cardiovascular PhenotypesU01HL084715 · NHLBI · UNIVERSITY OF CHICAGO · PI COX, NANCY J · 2006 to 2008
$836k
METHODS FOR HUMAN GENETIC LINKAGE MAPPINGR29HG001645 · NHGRI · UNIVERSITY OF CHICAGO · PI MCPEEK, MARY SARA · 1997 to 2001
$126k
NHGRI NIH HHS HG001645NHGRI NIH HHS R01 HG001645NHGRI NIH HHS R29 HG001645NHLBI NIH HHS HL084715NHLBI NIH HHS U01 HL084715NIAAA NIH HHS U10 AA008401NIAAA NIH HHS U10AA008401
6 · The paper itself

Abstract

We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the MQLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the MQLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the MQLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 (P=5.9x10(-7)), tsc1750530 (P=4.0x10(-7)), tsc0046696 (P=4.7x10(-7)), and tsc0057290 (P=5.2x10(-7)) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.

Indexed as

Gene FrequencyLikelihood FunctionsLinkage DisequilibriumModels, GeneticCase-Control StudiesHumansPedigreePenetranceStatistics as Topic

Identifiers

PMID17668381
PMCPMC1950805

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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.