Combining functional and linkage disequilibrium information in the selection of tag SNPs

Sham, Pak C.; Ao, Sio-Iong; Kwan, Jamie; Kao, Patrick Y. P.; Cheung, Florence Shin Gee; Fong, Pui-Yee; Ng, Michael K.

doi:10.1093/bioinformatics/btl532

Cited by 15 publications

(10 citation statements)

References 7 publications

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“…Among them, cluster algorithms partition objects into different groups that have similar characteristics. These methods have already become a valuable tool to detect associations between combinations of SNP markers and diseases and for the selection of tag SNPs [2,3]. Not only here, the size of the generated data sets has grown up to 1000000 markers per chip.…”

Section: Introductionmentioning

confidence: 99%

A highly efficient multi-core algorithm for clustering extremely large datasets

Kraus

Kestler

2010

BMC Bioinformatics

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BackgroundIn recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.ResultsWe introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization.ConclusionsMost desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.

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Section: Introductionmentioning

confidence: 99%

A highly efficient multi-core algorithm for clustering extremely large datasets

Kraus

Kestler

2010

BMC Bioinformatics

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“…For follow-up analyses, we used the WCLUSTAG [Sham et al, 2007] program for selection of additional tag SNPs within the most promising gene from the initial screen (which was neuroglycan C, see below). WCLUSTAG allows users to adjust the clustering threshold according to the location or presumed functional significance of the SNPs.…”

Section: Follow-up Analyses Of Identified Genesmentioning

confidence: 99%

“…1). The region was interrogated by tag SNPs selected using a clustering algorithm [Ao et al, 2005] which has been further improved by using both LD and biological information in the selection process [Sham et al, 2007]. Using these tools, we have conducted a highdensity association study of this 47-53 Mb region of 3p to search for SZ susceptibility genes.…”

Section: Introductionmentioning

confidence: 99%

Identification of neuroglycan C and interacting partners as potential susceptibility genes for schizophrenia in a Southern Chinese population

Fong

Chen

et al. 2009

American J of Med Genetics Pt B

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Chromosome 3p was reported by previous studies as one of the regions showing strong evidence of linkage with schizophrenia. We performed a fine-mapping association study of a 6-Mb high-LD and gene-rich region on 3p in a Southern Chinese sample of 489 schizophrenia patients and 519 controls to search for susceptibility genes. In the initial screen, 4 SNPs out of the 144 tag SNPs genotyped were nominally significant (P < 0.05). One of the most significant SNPs (rs3732530, P = 0.0048) was a non-synonymous SNP in the neuroglycan C (NGC, also known as CSPG5) gene, which belongs to the neuregulin family. The gene prioritization program Endeavor ranked NGC 8th out of the 129 genes in the 6-Mb region and the highest among the genes within the same LD block. Further genotyping of NGC revealed 3 more SNPs to be nominally associated with schizophrenia. Three other genes (NRG1, ErbB3, ErbB4) involved in the neuregulin pathways were subsequently genotyped. Interaction analysis by multifactor dimensionality reduction (MDR) revealed a significant two-SNP interaction between NGC and NRG1 (P = 0.015) and three-SNP interactions between NRG1 and ErbB4 (P = 0.009). The gene NGC is exclusively expressed in the brain. It is implicated in neurodevelopment in rats and was previously shown to promote neurite outgrowth. Methamphetamine, a drug that may induce psychotic symptoms, was reported to alter the expression of NGC. Taken together, these results suggest that NGC may be a novel candidate gene, and neuregulin signaling pathways may play an important role in schizophrenia.

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“…In addition, some selected regions (the ''Encode'' regions) were subjected to deep re-sequencing to identify all common variants, and these were exhaustively genotyped (The International HapMap Consortium 2005). The HapMap data can be downloaded to examine the LD pattern of any region of the genome, and to inform the selection of a subset of SNPs (called tagSNPs; Carlson et al 2004;Sham et al 2007) that would indirectly ''capture'' all the other SNPs in the region by virtue of high LD. In addition, data in the Encode region can be used to assess the extent to which commercial whole-genome genotyping products are able to ''capture'' all common variants in the genome (Li et al 2008).…”

Section: Whole-genome Snp Datamentioning

confidence: 99%

Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci

2009

Self Cite

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The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.

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Combining functional and linkage disequilibrium information in the selection of tag SNPs

Cited by 15 publications

References 7 publications

A highly efficient multi-core algorithm for clustering extremely large datasets

A highly efficient multi-core algorithm for clustering extremely large datasets

Identification of neuroglycan C and interacting partners as potential susceptibility genes for schizophrenia in a Southern Chinese population

Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci

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