scholarly journals Haplotype Block Partitioning and Tag SNP Selection Using Genotype Data and Their Applications to Association Studies

2004 ◽  
Vol 14 (5) ◽  
pp. 908-916 ◽  
Author(s):  
K. Zhang
Keyword(s):  
Haplotype Block ◽  
Association Studies ◽  
Genotype Data ◽  
Block Partitioning ◽  
Tag Snp ◽  
Tag Snp Selection

HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms

2004 ◽  
Vol 21 (1) ◽  
pp. 131-134 ◽  
Author(s):  
K. Zhang ◽  
Z. Qin ◽  
T. Chen ◽  
J. S. Liu ◽  
M. S. Waterman ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Haplotype Block ◽  
Block Partitioning ◽  
Tag Snp ◽  
Tag Snp Selection ◽  
Programming Algorithms

scholarly journals Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies

2018 ◽  
Vol 8 (10) ◽  
pp. 3255-3267 ◽  
Author(s):  
Genevieve L. Wojcik ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Ryan Welch ◽  
Alicia R Martin ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Tag Snp ◽  
Tag Snp Selection

scholarly journals Increasing the Power of Association Studies by Imputation-based Sparse Tag SNP Selection

2009 ◽  
Vol 9 (3) ◽  
pp. 269-282 ◽  
Author(s):  
Ofir Davidovich ◽  
Eran Halperin ◽  
Gad Kimmel ◽  
Ron Shamir
Keyword(s):  
Association Studies ◽  
Tag Snp ◽  
Tag Snp Selection

scholarly journals Tag SNP selection in genotype data for maximizing SNP prediction accuracy

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i195-i203 ◽  
Author(s):  
E. Halperin ◽  
G. Kimmel ◽  
R. Shamir
Keyword(s):  
Prediction Accuracy ◽  
Genotype Data ◽  
Tag Snp ◽  
Tag Snp Selection

scholarly journals Efficient Haplotype Block Partitioning and Tag SNP Selection Algorithms under Various Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wen-Pei Chen ◽  
Che-Lun Hung ◽  
Yaw-Ling Lin
Keyword(s):  
Linkage Disequilibrium ◽  
Haplotype Block ◽  
Association Studies ◽  
Optimal Solution ◽  
High Linkage Disequilibrium ◽  
Chromosome 21 ◽  
Small Subset ◽  
Genome Wide Association Studies ◽  
Block Partitioning ◽  
Tag Snps

Patterns of linkage disequilibrium plays a central role in genome-wide association studies aimed at identifying genetic variation responsible for common human diseases. These patterns in human chromosomes show a block-like structure, and regions of high linkage disequilibrium are called haplotype blocks. A small subset of SNPs, called tag SNPs, is sufficient to capture the haplotype patterns in each haplotype block. Previously developed algorithms completely partition a haplotype sample into blocks while attempting to minimize the number of tag SNPs. However, when resource limitations prevent genotyping all the tag SNPs, it is desirable to restrict their number. We propose two dynamic programming algorithms, incorporating many diversity evaluation functions, for haplotype block partitioning using a limited number of tag SNPs. We use the proposed algorithms to partition the chromosome 21 haplotype data. When the sample is fully partitioned into blocks by our algorithms, the 2,266 blocks and 3,260 tag SNPs are fewer than those identified by previous studies. We also demonstrate that our algorithms find the optimal solution by exploiting the nonmonotonic property of a common haplotype-evaluation function.

EFFECTIVE ALGORITHMS FOR TAG SNP SELECTION

2005 ◽  
Vol 03 (05) ◽  
pp. 1089-1106 ◽  
Author(s):  
TIE-FEI LIU ◽  
WING-KIN SUNG ◽  
YI LI ◽  
JIAN-JUN LIU ◽  
ANKUSH MITTAL ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Association Studies ◽  
Strong Association ◽  
Genetic Association Studies ◽  
Approximate Solutions ◽  
Computational Time ◽  
Nucleotide Polymorphisms ◽  
Tag Snps ◽  
Tag Snp ◽  
Tag Snp Selection

Single nucleotide polymorphisms (SNPs), due to their abundance and low mutation rate, are very useful genetic markers for genetic association studies. However, the current genotyping technology cannot afford to genotype all common SNPs in all the genes. By making use of linkage disequilibrium, we can reduce the experiment cost by genotyping a subset of SNPs, called Tag SNPs, which have a strong association with the ungenotyped SNPs, while are as independent from each other as possible. The problem of selecting Tag SNPs is NP-complete; when there are large number of SNPs, in order to avoid extremely long computational time, most of the existing Tag SNP selection methods first partition the SNPs into blocks based on certain block definitions, then Tag SNPs are selected in each block by brute-force search. The size of the Tag SNP set obtained in this way may usually be reduced further due to the inter-dependency among blocks. This paper proposes two algorithms, TSSA and TSSD, to tackle the block-independent Tag SNP selection problem. TSSA is based on A* search algorithm, and TSSD is a heuristic algorithm. Experiments show that TSSA can find the optimal solutions for medium-sized problems in reasonable time, while TSSD can handle very large problems and report approximate solutions very close to the optimal ones.

scholarly journals Haplotype block partitioning as a tool for dimensionality reduction in SNP association studies

BMC Genomics ◽  
2008 ◽  
Vol 9 (1) ◽  
pp. 405 ◽  
Author(s):  
Cristian Pattaro ◽  
Ingo Ruczinski ◽  
Daniele M Fallin ◽  
Giovanni Parmigiani
Keyword(s):  
Dimensionality Reduction ◽  
Haplotype Block ◽  
Association Studies ◽  
Block Partitioning ◽  
Snp Association

scholarly journals Imputation aware tag SNP selection to improve power for multi-ethnic association studies

2017 ◽  
Author(s):  
Genevieve L. Wojcik ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Ryan Welch ◽  
Alicia R Martin ◽  
...  
Keyword(s):  
Rare Variant ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genomic Research ◽  
Array Design ◽  
Rare Variant Association ◽  
Tag Snps ◽  
Tag Snp ◽  
Tag Snp Selection ◽  
The Impact

AbstractThe emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. Consequently, a new generation of genotyping arrays are being developed designed with tag single nucleotide polymorphisms (SNPs) to improve rare variant imputation. Selection of these tag SNPs poses several challenges as rare variants tend to be continentally-or even population-specific and reflect fine-scale linkage disequilibrium (LD) structure impacted by recent demographic events. To explore the landscape of tag-able variation and guide design considerations for large-cohort and biobank arrays, we developed a novel pipeline to select tag SNPs using the 26 population reference panel from Phase of the 1000 Genomes Project. We evaluate our approach using leave-one-out internal validation via standard imputation methods that allows the direct comparison of tag SNP performance by estimating the correlation of the imputed and real genotypes for each iteration of potential array sites. We show how this approach allows for an assessment of array design and performance that can take advantage of the development of deeper and more diverse sequenced reference panels. We quantify the impact of demography on tag SNP performance across populations and provide population-specific guidelines for tag SNP selection. We also examine array design strategies that target single populations versus multi-ethnic cohorts, and demonstrate a boost in performance for the latter can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Finally, we demonstrate the utility of improved array design to provide meaningful improvements in power, particularly in trans-ethnic studies. The unified framework presented will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

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