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

Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes.

Nature communications - 14 Aug 2018

Ganjgahi Habib, Winkler Anderson M, Glahn David C, Blangero John, Donohue Brian, Kochunov Peter, Nichols Thomas E

Abstract excerpt

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes...

Read the complete abstract on PubMed

Topics

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

Share to Anisotropy
Share to Brain

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Databases, Genetic

Share to Genome-Wide Association Study

Share to Linear Models

Share to Models, Genetic

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 30108209
DOI: 10.1038/s41467-018-05444-6
PMCID: PMC6092439

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes.PMID 30108209Read ↗

Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.Semantic proximity Read ↗

Independent Genomic Sources of Brain Structure and FunctionSemantic proximity Read ↗

Phenotype Discovery from Population Brain ImagingSemantic proximity Read ↗

Phenotype discovery from population brain imaging.Semantic proximity Read ↗

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.Semantic proximity Read ↗

Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation.Semantic proximity Read ↗

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.Semantic proximity Read ↗

Imaging genomics.Semantic proximity Read ↗

Genetic association of high-dimensional traitsSemantic proximity Read ↗

Sparse parallel independent component analysis and its application to identify linked genomic and gray matter alterations underlying working memory impairment in attention-deficit/hyperactivity disorderSemantic proximity Read ↗

FPLS-DC: functional partial least squares through distance covariance for imaging genetics.Semantic proximity Read ↗

Multi-trait genome-wide analyses of the brain imaging phenotypes in UK BiobankSemantic proximity Read ↗

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