ArticleGenetic epidemiology2005
A sparse marker extension tree algorithm for selecting the best set of haplotype tagging single nucleotide polymorphisms.
Article in Genetic epidemiology, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites.BMC bioinformatics · 2018Article
- A prospective study of leukocyte telomere length and risk of type 2 diabetes in postmenopausal women.Diabetes · 2012Article
- Common variations in the genes encoding C-reactive protein, tumor necrosis factor-alpha, and interleukin-6, and the risk of clinical diabetes in the Women's Health Initiative Observational Study.Clinical chemistry · 2011Article
- "PolyMin": software for identification of the minimum number of polymorphisms required for haplotype and genotype differentiation.BMC bioinformatics · 2009Article
- Relation of genetic variation in the gene coding for C-reactive protein with its plasma protein concentrations: findings from the Women's Health Initiative Observational Cohort.Clinical chemistry · 2009Article
- Calibrating the performance of SNP arrays for whole-genome association studies.PLoS genetics · 2008Article
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Authors and funding
3 authors.
Funding
Abstract
Single nucleotide polymorphisms (SNPs) play a central role in the identification of susceptibility genes for common diseases. Recent empirical studies on human genome have revealed block-like structures, and each block contains a set of haplotype tagging SNPs (htSNPs) that capture a large fraction of the haplotype diversity. Herein, we present an innovative sparse marker extension tree (SMET) algorithm to select optimal htSNP set(s). SMET reduces the search space considerably (compared to full enumeration strategy), and therefore improves computing efficiency. We tested this algorithm on several datasets at three different genomic scales: (1) gene-wide (NOS3, CRP, IL6 PPARA, and TNF), (2) region-wide (a Whitehead Institute inflammatory bowel disease dataset and a UK Graves' disease dataset), and (3) chromosome-wide (chromosome 22) levels. SMET offers geneticists with greater flexibilities in SNP tagging than lossless methods with adjustable haplotype diversity coverage (phi). In simulation studies, we found that (1) an initial sample size of 50 individuals (100 chromosomes) or more is needed for htSNP selection; (2) the SNP tagging strategy is considerably more efficient when the underlying block structure is taken into account; and (3) htSNP sets at 80-90% phi are more cost-effective than the lossless sets in term of relative power, relative risk ratio estimation, and genotyping efforts. Our study suggests that the novel SMET algorithm is a valuable tool for association tests.
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