Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A comparison of power to detect a QTL in sib-pair data using multivariate phenotypes, mean phenotypes, and factor scores.

Behavior genetics - 1 Sept 1998

Boomsma D I, Dolan C V

Abstract excerpt

The power to detect a quantitative trait locus (QTL) in sib-pair data is investigated. We assume that we have at our disposal 3 or 4 related phenotypic measures in a sample of sib-pairs. Individual differences in these phenotypes are due to a common QTL and specific (i.e., unique to each phenotype) nonshared environmental and specific polygenic additive effects. In addition, models are considered that include...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Chi-Square Distribution
Share to Chromosome Mapping
Share to Models, Genetic

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Multivariate Analysis

Share to Phenotype

Share to Quantitative Trait, Heritable

Share to Social Environment

Source

Identifiers

PMID: 9926615
DOI: 10.1023/a:1021665501312

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A comparison of power to detect a QTL in sib-pair data using multivariate phenotypes, mean phenotypes, and factor scores.PMID 9926615Read ↗

Genetic association in multivariate phenotypic data: power in five models.Semantic proximity Read ↗

Mapping quantitative trait loci using multiple phenotypes in general pedigrees.Semantic proximity Read ↗

Dissecting the correlation structure of a bivariate phenotype: common genes or shared environment?Semantic proximity Read ↗

A simulation study concerning the effect of varying the residual phenotypic correlation on the power of bivariate quantitative trait loci linkage analysis.Semantic proximity Read ↗

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.Semantic proximity Read ↗

A statistical framework for expression quantitative trait loci mapping.Semantic proximity Read ↗

A latent class model for testing for linkage and classifying families when the sample may contain segregating and non-segregating families.Semantic proximity Read ↗

Deciphering the genetic architecture of a multivariate phenotype.Semantic proximity Read ↗

Multivariate genetic analyses in heterogeneous populations.Semantic proximity Read ↗

A univariate perspective of multivariate genome-wide association analysis.Semantic proximity Read ↗

Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power.Semantic proximity Read ↗

Testing causal hypotheses in multivariate linkage analysis of quantitative traits: general formulation and application to sibpair data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.