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

Coverage and power in genomewide association studies.

American journal of human genetics - 1 May 2006

Jorgenson Eric, Witte John S

Abstract excerpt

The ability of genomewide association studies to decipher genetic traits is driven in part by how well the measured single-nucleotide polymorphisms "cover" the unmeasured causal variants. Estimates of coverage based on standard linkage-disequilibrium measures, such as the average maximum squared correlation coefficient (r2), can lead to inaccurate and inflated estimates of the power of genomewide association...

Read the complete abstract on PubMed

Topics

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

Share to Chromosome Mapping
Share to Genetic Variation
Share to Genome, Human

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Linkage Disequilibrium

Share to Models, Statistical

Share to Polymorphism, Single Nucleotide

Share to Quantitative Trait, Heritable

Source

Open in PubMed Open full text

Identifiers

PMID: 16642443
DOI: 10.1086/503751
PMCID: PMC1474045

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Coverage and power in genomewide association studies.PMID 16642443Read ↗

You've gotta be lucky: Coverage and the elusive gene-gene interaction.Semantic proximity Read ↗

Evaluation of coverage variation of SNP chips for genome-wide association studies.Semantic proximity Read ↗

Evaluating coverage of genome-wide association studies.Semantic proximity Read ↗

The impact of improved microarray coverage and larger sample sizes on future genome-wide association studies.Semantic proximity Read ↗

Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern.Semantic proximity Read ↗

Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project.Semantic proximity Read ↗

Imputation-Based Genomic Coverage Assessments of Current Genotyping Arrays: <i>Illumina HumanCore, OmniExpress, Multi-Ethnic global array and sub-arrays, Global Screening Array, Omni2.5M, Omni5M, and Affymetrix UK Biobank</i>Semantic proximity Read ↗

Mathematical bounds on r2 and the effect size in case-control genome-wide association studies.Semantic proximity Read ↗

Estimating coverage and power for genetic association studies using near-complete variation data.Semantic proximity Read ↗

Calibrating the performance of SNP arrays for whole-genome association studies.Semantic proximity Read ↗

Power analysis for genome-wide association studies.Semantic proximity Read ↗

Power and sample size calculations for high-throughput sequencing-based experiments.Semantic proximity Read ↗

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