Evidence map›Paper›PMID 26493781›Full record

ArticleGenetic epidemiology2015

Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance.

Arunabha Majumdar, John S Witte, Saurabh Ghosh

Abstract read
In one paragraph

Article in Genetic epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

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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.

Arunabha MajumdarDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
John S WitteDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
Saurabh GhoshHuman Genetics Unit, Indian Statistical Institute, Kolkata, West Bengal, India.

Funding

Training in Molecular& Genetic Epidemiology of CancerR25CA112355 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2005 to 2018
$6.5M
Genetic Epidemiology of Prostate Cancer Risk and ProgressionR01CA088164 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VAN DEN EEDEN, STEPHEN K., WITTE, JOHN S. · 2000 to 2015
$6.1M
Lifespan Psychosocial Profiles and Biological Pathways to Bone StrengthR01AG033067 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CRANDALL, CAROLYN JANET, KARLAMANGLA, ARUN S · 2009 to 2012
$1.4M
NCI NIH HHS R01 CA088164NCI NIH HHS R01CA088164NCI NIH HHS R25 CA112355NCI NIH HHS R25CA112355NIA NIH HHS R01 AG033067NIA NIH HHS RC2 AG033067
6 · The paper itself

Abstract

Binary phenotypes commonly arise due to multiple underlying quantitative precursors and genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g., MultiPhen (O'Reilly et al. []), have been developed to identify genetic factors underlying a multivariate phenotype. For univariate phenotypes, the usefulness and applicability of allele-level tests have been investigated. The test of allele frequency difference among cases and controls is commonly used for mapping case-control association. However, allelic methods for multivariate association mapping have not been studied much. In this article, we explore two allelic tests of multivariate association: one using a Binomial regression model based on inverted regression of genotype on phenotype (Binomial regression-based Association of Multivariate Phenotypes [BAMP]), and the other employing the Mahalanobis distance between two sample means of the multivariate phenotype vector for two alleles at a single-nucleotide polymorphism (Distance-based Association of Multivariate Phenotypes [DAMP]). These methods can incorporate both discrete and continuous phenotypes. Some theoretical properties for BAMP are studied. Using simulations, the power of the methods for detecting multivariate association is compared with the genotype-level test MultiPhen's. The allelic tests yield marginally higher power than MultiPhen for multivariate phenotypes. For one/two binary traits under recessive mode of inheritance, allelic tests are found to be substantially more powerful. All three tests are applied to two different real data and the results offer some support for the simulation study. We propose a hybrid approach for testing multivariate association that implements MultiPhen when Hardy-Weinberg Equilibrium (HWE) is violated and BAMP otherwise, because the allelic approaches assume HWE.

Indexed as

Models, GeneticRegression AnalysisAllelesBinomial DistributionComputer SimulationGene FrequencyGenetic MarkersGenetic VariationGenome-Wide Association StudyGenotypeHumansModels, StatisticalPhenotypePolymorphism, Single NucleotideGenetic Markersallelic testsgenome-wide association studymultiple phenotypesmultivariate association

Identifiers

PMID26493781
PMCPMC4958458

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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.