How well do HapMap SNPs capture the untyped SNPs?

BACKGROUND: The recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs. RESULTS: Our analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs. CONCLUSION: We have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis

<p>Abstract</p> <p>Background</p> <p>We report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis.</p> <p>Methods</p> <p>We combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform.</p> <p>Results</p> <p>Validation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r²from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the ≥ 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays.</p> <p>Conclusion</p> <p>A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen.</p

Using Population Mixtures to Optimize the Utility of Genomic Databases: Linkage Disequilibrium and Association Study Design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis – such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65949/1/j.1469-1809.2008.00457.x.pd

Identifying Highly Conserved and Highly Differentiated Gene Ontology Categories in Human Populations

Detecting and interpreting certain system-level characteristics associated with human population genetic differences is a challenge for human geneticists. In this study, we conducted a population genetic study using the HapMap genotype data to identify certain special Gene Ontology (GO) categories associated with high/low genetic difference among 11 Hapmap populations. Initially, the genetic differences in each gene region among these populations were measured using allele frequency, linkage disequilibrium (LD) pattern, and transferability of tagSNPs. The associations between each GO term and these genetic differences were then identified. The results showed that cellular process, catalytic activity, binding, and some of their sub-terms were associated with high levels of genetic difference, and genes involved in these functional categories displayed, on average, high genetic diversity among different populations. By contrast, multicellular organismal processes, molecular transducer activity, and some of their sub-terms were associated with low levels of genetic difference. In particular, the neurological system process under the multicellular organismal process category had low levels of genetic difference; the neurological function also showed high evolutionary conservation between species in some previous studies. These results may provide a new insight into the understanding of human evolutionary history at the system-level

iHAP – integrated haplotype analysis pipeline for characterizing the haplotype structure of genes

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at , provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies

Assembly of Inflammation-Related Genes for Pathway-Focused Genetic Analysis

Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP) arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs) and African (21,542 SNPs) populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and associated subpathways can facilitate comprehensive inflammation pathway- focused association analyses

A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r(2) of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r(2) of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62863/1/nature06258.pd

The genetics of obesity-related traits and lipoproteins in Filipino women

The underlying genetic component of risk factors for cardiovascular disease (CVD) is not well understood. Recently, advances in high-throughput genotyping, single nucleotide polymorphism (SNP) discovery, and the development of databases such as the International Haplotype Map (HapMap) have provided scientists with tools to complete a genetic analysis of complex diseases such as CVD. Research presented in this dissertation aims to further understand the genetics of obesity-related traits and lipoprotein levels and identify variants that are associated with these traits in a cohort of adult women from metro Cebu, Philippines, who participated in the Cebu Longitudinal Health and Nutrition Survey (CLHNS). Initially I assess the transferability of tag SNPs chosen from HapMap panels to the CLHNS. I show that the Asian HapMap samples are an effective resource for studies in the CLHNS. I then investigate the association between 19 candidate variants in 10 genes previously reported to be associated with obesity-related traits with similar traits in the CLHNS. We observe evidence for association with the A-allele of rs9939609 of FTO and ADRB3 Trp64-allele with obesity traits. I perform a genome-wide association study for HDL-C, triglycerides, LDL-C, and total cholesterol. Among ~2 million SNPs analyzed, we observe evidence of association for 11 loci previously described. We observe suggestive evidence of trait association (P &lt;10-5) for Tankyrase (TNKS) with LDL-C and Collecting-12 (COLEC12) with total cholesterol. In a separate study, I investigate an HDL-C associated locus, GALNT2, to identify functional variants responsible for the association signal. I identify variants in moderate linkage disequilibrium (r2 &gt;.5) with HDL-C associated SNPs, clone regions that have suggestive regulatory function into a luciferase reporter vector, and measure transcriptional activity in HepG2 cells. The results suggest that a 21 bp deletion, rs4849913, and/or rs2144300 may act to increase the transcriptional activity of GALNT2 or an unknown novel intronic transcript to increase HDL-C. These studies present the first genetic study of CVD traits in the CLHNS and a molecular study of a gene that is associated with HDL-C. Together this research provides a solid foundation for one day identifying the molecular mechanism underlying complex diseases

Common genetic variants of the ion channel transient receptor potential membrane melastatin 6 and 7 (TRPM6 and TRPM7), magnesium intake, and risk of type 2 diabetes in women

<p>Abstract</p> <p>Background</p> <p>Ion channel transient receptor potential membrane melastatin 6 and 7 (TRPM6 and TRPM7) play a central role in magnesium homeostasis, which is critical for maintaining glucose and insulin metabolism. However, it is unclear whether common genetic variation in <it>TRPM6 </it>and <it>TRPM7 </it>contributes to risk of type 2 diabetes.</p> <p>Methods</p> <p>We conducted a nested case-control study in the Women's Health Study. During a median of 10 years of follow-up, 359 incident diabetes cases were diagnosed and matched by age and ethnicity with 359 controls. We analyzed 20 haplotype-tagging single nucleotide polymorphisms (SNPs) in <it>TRPM6 </it>and 5 common SNPs in <it>TRPM7 </it>for their association with diabetes risk.</p> <p>Results</p> <p>Overall, there was no robust and significant association between any single SNP and diabetes risk. Neither was there any evidence of association between common <it>TRPM6 </it>and <it>TRPM7 </it>haplotypes and diabetes risk. Our haplotype analyses suggested a significant risk of type 2 diabetes among carriers of both the rare alleles from two non-synomous SNPs in <it>TRPM6 </it>(Val1393Ile in exon 26 [rs3750425] and Lys1584Glu in exon 27 [rs2274924]) when their magnesium intake was lower than 250 mg per day. Compared with non-carriers, women who were carriers of the haplotype 1393Ile-1584Glu had an increased risk of type 2 diabetes (OR, 4.92, 95% CI, 1.05–23.0) only when they had low magnesium intake (<250 mg/day).</p> <p>Conclusion</p> <p>Our results provide suggestive evidence that two common non-synonymous <it>TRPM6 </it>coding region variants, Ile1393Val and Lys1584Glu polymorphisms, might confer susceptibility to type 2 diabetes in women with low magnesium intake. Further replication in large-scale studies is warranted.</p