10 research outputs found

    Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.

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    <p>Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.</p

    Analysis of case-control study type I error rates from 3 simulated SNPs within <i>BDNF</i>.

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    <p>The three SNPs show allele frequency differences between CEU and YRI of 0.066 (rs11030108), 0.102 (rs10767658), and 0.233 (rs1013402). The y-axis is estimated type I error rate versus the simulated CEU proportion (x-axis). Panels on the left show data with a difference in disease prevalence ratio of 1.25 while a ratio of 1.5 is shown on the right.</p

    Detection of population structure in four HapMap populations.

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    <p>The first two principal components from EIGENSTRAT are plotted for all 3 SNP panels (A, SNP panel 93; B, SNP panel 52; C, SNP panel 19). As more AIMs are used in the analysis, the resolution improves. The 52 SNP panel appears to have some overlap between CEU and CHB+JPT though it should be noted that these datapoints are more clearly differentiated by considering the third and fourth principal components (not shown).</p

    Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

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    <div><p>Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.</p></div

    Examples of the effects of data processing on linkage results within individual data subsets.

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    <p>The labels for each line indicate state of phenotype (Pheno) and genotype (Geno) data, which can be Human Genetics Initiative (HGI) or Combined Analysis of Psychiatric Studies (CAPS).</p

    Linkage disequilibrium between 1,544 SNPs and broad schizophrenia spectrum phenotype.

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    <p>PPLD|L values for 1,544 SNPs, including five MirSNPs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194233#pone.0194233.t001" target="_blank">Table 1</a>), from chr17: 74,684,647 to 83,257,441 (GRCh38), were calculated using KELVIN v2.4.0 and plotted vs physical distance. The MirSNP rs1060120 in <i>H3F3B</i> produced a PPLD|L of 0.21, notably higher than the remaining SNPs.</p