Evidence map›Paper›PMID 22968922›Full record

ArticleGenetic epidemiology2012

SNP set association analysis for familial data.

Elizabeth D Schifano, Michael P Epstein, Lawrence F Bielak, Min A Jhun, Sharon L R Kardia, Patricia A Peyser, Xihong Lin

Abstract read
In one paragraph

Article in Genetic epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 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

76 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  17. Rare Coding Variants Associated with Breast Cancer.Advances in experimental medicine and biology · 2021
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16 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

7 authors.

Elizabeth D SchifanoDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
Michael P Epstein
Lawrence F Bielak
Min A Jhun
Sharon L R Kardia
Patricia A Peyser
Xihong Lin

Funding

GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Genomic Predictors of Arteriosclerosis in HypertensivesR01HL087660 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARDIA, SHARON L · 2007 to 2009
$5.1M
Interdisciplinary Training in Genes and the EnvironmentT32ES016645 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LEE, CHIH-HAO · 2008 to 2018
$4.0M
Statistical Methods for Correlated and High-Dimensional Biomedical DataR37CA076404 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG · 2007 to 2014
$2.5M
Novel Statistical Methods for Human Gene MappingR01HG003618 · NHGRI · EMORY UNIVERSITY · PI EPSTEIN, MICHAEL PHILIP · 2006 to 2010
$1.5M
NCI NIH HHS P01 CA134294NCI NIH HHS R35 CA197449NCI NIH HHS R37 CA076404NHGRI NIH HHS R01 HG003618NHLBI NIH HHS R01 HL087660NHLBI NIH HHS R01 HL87660NIEHS NIH HHS T32 ES007142NIEHS NIH HHS T32 ES016645
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

Indexed as

FamilyGenetic Association StudiesModels, GeneticAcid CeramidaseAlgorithmsArtificial IntelligenceChromosomes, Human, Pair 10GenesGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideAcid CeramidaseASAH1 protein, humanfamily association studieskernel machinelinear mixed modelmultilocus testscore statisticsvariance component testwithin-family correlation

Identifiers

PMID22968922
PMCPMC3683469

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.