Dataset Information

Single nucleotide polymorphism-based genome-wide linkage analysis in Japanese atopic dermatitis families.

ABSTRACT: BACKGROUND: Atopic dermatitis develops as a result of complex interactions between several genetic and environmental factors. To date, 4 genome-wide linkage studies of atopic dermatitis have been performed in Caucasian populations, however, similar studies have not been done in Asian populations. The aim of this study was to identify chromosome regions linked to atopic dermatitis in a Japanese population. METHODS: We used a high-density, single nucleotide polymorphism genotyping assay, the Illumina BeadArray Linkage Mapping Panel (version 4) comprising 5,861 single nucleotide polymorphisms, to perform a genome-wide linkage analysis of 77 Japanese families with 111 affected sib-pairs with atopic dermatitis. RESULTS: We found suggestive evidence for linkage with 15q21 (LOD = 2.01, NPL = 2.87, P = .0012) and weak linkage to 1q24 (LOD = 1.26, NPL = 2.44, P = .008). CONCLUSION: We report the first genome-wide linkage study of atopic dermatitis in an Asian population, and novel loci on chromosomes 15q21 and 1q24 linked to atopic dermatitis. Identification of novel causative genes for atopic dermatitis will advance our understanding of the pathogenesis of atopic dermatitis.

SUBMITTER: Enomoto H

PROVIDER: S-EPMC2082241 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Single nucleotide polymorphism-based genome-wide linkage analysis in Japanese atopic dermatitis families.

Enomoto Hisako H Noguchi Emiko E Iijima Shigeruko S Takahashi Takenori T Hayakawa Kazuhito K Ito Mikako M Kano Toshiyuki T Aoki Takeshi T Suzuki Yoichi Y Koga Minori M Tamari Mayumi M Shiohara Tetsuo T Otsuka Fujio F Arinami Tadao T

BMC dermatology 20070928

<h4>Background</h4>Atopic dermatitis develops as a result of complex interactions between several genetic and environmental factors. To date, 4 genome-wide linkage studies of atopic dermatitis have been performed in Caucasian populations, however, similar studies have not been done in Asian populations. The aim of this study was to identify chromosome regions linked to atopic dermatitis in a Japanese population.<h4>Methods</h4>We used a high-density, single nucleotide polymorphism genotyping ass ...[more]

PMID: 17900373

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Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Project description:Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing.

Project description:BACKGROUND:The ideal malaria parasite populations for initial mapping of genomic regions contributing to phenotypes such as drug resistance and virulence, through genome-wide association studies, are those with high genetic diversity, allowing for numerous informative markers, and rare meiotic recombination, allowing for strong linkage disequilibrium (LD) between markers and phenotype-determining loci. However, levels of genetic diversity and LD in field populations of the major human malaria parasite P. vivax remain little characterized. RESULTS:We examined single-nucleotide polymorphisms (SNPs) and LD patterns across a 100-kb chromosome segment of P. vivax in 238 field isolates from areas of low to moderate malaria endemicity in South America and Asia, where LD tends to be more extensive than in holoendemic populations, and in two monkey-adapted strains (Salvador-I, from El Salvador, and Belem, from Brazil). We found varying levels of SNP diversity and LD across populations, with the highest diversity and strongest LD in the area of lowest malaria transmission. We found several clusters of contiguous markers with rare meiotic recombination and characterized a relatively conserved haplotype structure among populations, suggesting the existence of recombination hotspots in the genome region analyzed. Both silent and nonsynonymous SNPs revealed substantial between-population differentiation, which accounted for ~40% of the overall genetic diversity observed. Although parasites clustered according to their continental origin, we found evidence for substructure within the Brazilian population of P. vivax. We also explored between-population differentiation patterns revealed by loci putatively affected by natural selection and found marked geographic variation in frequencies of nucleotide substitutions at the pvmdr-1 locus, putatively associated with drug resistance. CONCLUSION:These findings support the feasibility of genome-wide association studies in carefully selected populations of P. vivax, using relatively low densities of markers, but underscore the risk of false positives caused by population structure at both local and regional levels.

Dataset Information

Single nucleotide polymorphism-based genome-wide linkage analysis in Japanese atopic dermatitis families.

Publications

Single nucleotide polymorphism-based genome-wide linkage analysis in Japanese atopic dermatitis families.

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