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

Smoothed functional principal component analysis for testing association of the entire allelic spectrum of genetic variation.

European journal of human genetics : EJHG - 1 Feb 2013

Luo Li, Zhu Yun, Xiong Momiao

Abstract excerpt

Fast and cheaper next-generation sequencing technologies will generate unprecedentedly massive and highly dimensional genetic variation data that allow nearly complete evaluation of genetic variation including both common and rare variants. There are two types of association tests: variant-by-variant test and group test. The variant-by-variant test is designed to test the association of common variants, while the...

Read the complete abstract on PubMed

Topics

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

Share to Alleles
Share to Angiopoietins
Share to Data Interpretation, Statistical

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Variation

Share to Genome-Wide Association Study

Share to Heart Diseases

Share to Principal Component Analysis

Share to Schizophrenia

Source

Open in PubMed Open full text

Identifiers

PMID: 22781089
DOI: 10.1038/ejhg.2012.141
PMCID: PMC3548254

Genes

ANGPTL4

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

Smoothed functional principal component analysis for testing association of the entire allelic spectrum of genetic variation.PMID 22781089Read ↗

Association studies for next-generation sequencing.Semantic proximity Read ↗

Family-based association studies for next-generation sequencing.Semantic proximity Read ↗

A fast and noise-resilient approach to detect rare-variant associations with deep sequencing data for complex disorders.Semantic proximity Read ↗

An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data.Semantic proximity Read ↗

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the WorldSemantic proximity Read ↗

Pathway analysis with next-generation sequencing data.Semantic proximity Read ↗

CLUSTERING OF RARE VARIANTS FOR CAUSAL VARIANTS IDENTIFICATION AND EFFECT DIRECTION CLASSIFICATIONSemantic proximity Read ↗

Scalable probabilistic PCA for large-scale genetic variation data.Semantic proximity Read ↗

Identifying associations in dense connectomes using structured kernel principal component regressionSemantic proximity Read ↗

An Optimal Bahadur-Efficient Method in Detection of Sparse Signals with Applications to Pathway Analysis in Sequencing Association Studies.Semantic proximity Read ↗

Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets.Semantic proximity Read ↗

A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.Semantic proximity Read ↗

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