ArticleAmerican journal of human genetics2004
Finding haplotype tagging SNPs by use of principal components analysis.
Article in American journal of human genetics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.Machine learning · 2020Article
- A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction.Heredity · 2018Article
- A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease.PloS one · 2017Article
- Efficiently identifying significant associations in genome-wide association studies.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling.Heredity · 2012Article
- Variable set enrichment analysis in genome-wide association studies.European journal of human genetics : EJHG · 2011Article
- Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification.Statistical analysis and data mining · 2011Article
- Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms.Genetics · 2011Article
- Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.NeuroImage · 2010Article
- Allosteric drug discrimination is coupled to mechanochemical changes in the kinesin-5 motor core.The Journal of biological chemistry · 2010Article
- Efficient genome-wide TagSNP selection across populations via the linkage disequilibrium criterion.Journal of computational biology : a journal of computational molecular cell biology · 2010Article
- CGTS: a site-clustering graph based tagSNP selection algorithm in genotype data.BMC bioinformatics · 2009Article
- Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA.Human brain mapping · 2009Article
- Do Estrogen Receptor beta Polymorphisms Play A Role in the Pharmacogenetics of Estrogen Signaling?Current pharmacogenomics and personalized medicine · 2008Article
- Efficient association study design via power-optimized tag SNP selection.Annals of human genetics · 2008Article
- Tracing sub-structure in the European American population with PCA-informative markers.PLoS genetics · 2008Article
- Supervised learning-based tagSNP selection for genome-wide disease classifications.BMC genomics · 2008Article
- A modified T-test feature selection method and its application on the HapMap genotype data.Genomics, proteomics & bioinformatics · 2007Article
- Selected questions on biomechanical exposures for surveillance of upper-limb work-related musculoskeletal disorders.International archives of occupational and environmental health · 2007Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. One such challenge is to select an optimal subset of SNPs, commonly referred as "haplotype tagging SNPs" (htSNPs), to capture most of the haplotype diversity of each haplotype block or gene-specific region. This information-reduction process facilitates cost-effective genotyping and, subsequently, genotype-phenotype association studies. It also has implications for assessing the risk of identifying research subjects on the basis of SNP information deposited in public domain databases. We have investigated methods for selecting htSNPs by use of principal components analysis (PCA). These methods first identify eigenSNPs and then map them to actual SNPs. We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ability of the selected htSNPs to reconstruct genotypes of non-htSNPs. We also compared these methods with two other htSNP finders, one of which is PCA based. We applied these methods to three experimental data sets and found that the PCA-based methods tend to select the smallest set of htSNPs to achieve a 90% reconstruction precision.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.