25 research outputs found

    Simulated statistical power to detect an association with a putative CNV as a function of false negative rate (ν<sub>n</sub>).

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    <p>The CNV explains 1% of the phenotypic variation when present in 20% of the population. The CNV has a frequency of 1% (red), 5% (orange), 10% (green), or 20% (blue). False positive rate (ν<sub>p</sub>) is zero.</p

    Square root of the variance of Δ for the multiallelic CNV locus with and false negative (ν<sub>n</sub>) and false positive error rates (ν<sub>p</sub>).

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    <p>Square root of the variance of Δ for the multiallelic CNV locus with and false negative (ν<sub>n</sub>) and false positive error rates (ν<sub>p</sub>).</p

    Recovery rate of deletions (red) and duplications (blue) from PennCNV using simulated intensity measurements as a function of CNV size.

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    <p>Recovery rate of deletions (red) and duplications (blue) from PennCNV using simulated intensity measurements as a function of CNV size.</p

    Square root of the variance of Δ for the deletion and duplication CNV loci with and false negative (ν<sub>n</sub>) and false positive error rates (ν<sub>p</sub>).

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    <p>Square root of the variance of Δ for the deletion and duplication CNV loci with and false negative (ν<sub>n</sub>) and false positive error rates (ν<sub>p</sub>).</p

    Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.

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    <p>Prediction accuracy in MESA using lead SNPs vs. SNPs identified in C/J analysis at different p-value thresholds.</p

    Prediction accuracy in MESA at 3 loci with additional detected SNPs at the 5×10<sup>−8</sup> threshold.

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    <p>Prediction accuracy in MESA at 3 loci with additional detected SNPs at the 5×10<sup>−8</sup> threshold.</p

    SNPs identified by conditional/joint analysis with p-value <5×10<sup>−5</sup>, and corresponding evidence of regulatory function from RegulomeDB.

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    <p>Abbreviations: Chr: chromosome, bp: base pair position, refA: reference allele, freq: frequency of the risk allele, b: regression coefficient from meta-analysis summary statistics, se: standard error from meta-analysis summary statistics, p: p-value from meta-analysis summary statistics, n: sample size in meta-analysis, bJ: regression coefficient estimated from conditional/joint analysis, bJ_se: standard error estimated from conditional/joint analysis, pJ: p-value from estimated from conditional/joint analysis, LD (r): linkage disequilibrium between corresponding SNP and the following SNP at the same locus.</p><p>(1f: eQTL+transcription factor (TF) binding/DNase peak; 2a: TF binding+matched TF motif+matched DNase Footprint+DNase peak; 3a: TF binding+any motif+DNase peak; 5: TF binding or DNase peak).</p><p>SNPs identified by conditional/joint analysis with p-value <5×10<sup>−5</sup>, and corresponding evidence of regulatory function from RegulomeDB.</p

    Variance explained at various p-value thresholds in the MESA validation dataset by the collection of individual SNPs on the liability scale, variance explained by, and model fit of, the weighted GRS, using Nagelkerke's R<sup>2</sup>, and AIC, respectively.

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    <p>Variance explained at various p-value thresholds in the MESA validation dataset by the collection of individual SNPs on the liability scale, variance explained by, and model fit of, the weighted GRS, using Nagelkerke's R<sup>2</sup>, and AIC, respectively.</p

    An Examination of the Relationship between Lipid Levels and Associated Genetic Markers across Racial/Ethnic Populations in the Multi-Ethnic Study of Atherosclerosis

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    <div><p>Large genome-wide association studies have reported hundreds of genetic markers associated with lipid levels. However, the discovery and estimated effect of variants at these loci, derived from samples of exclusively European descent, may not generalize to the majority of the world populations. We examined the collective strength of association among these loci in a diverse set of U.S. populations from the Multi-Ethnic Study of Atherosclerosis. We constructed a genetic risk score for each lipid outcome based on previously identified lipid-associated genetic markers, and examined the relationship between the genetic risk scores and corresponding outcomes. We discover this relationship was often moderated by race/ethnicity. Our findings provide insight into the generalizability and predictive utility of large sample size meta-analyses results when leveraging data from a single population. We hope these findings will encourage researchers to investigate genetic susceptibility in more diverse populations and explore the source of such discrepancies. Until then, we caution clinicians, genetic counselors, and genetic testing consumers when interpreting genetic data on complex traits.</p></div

    Added phenotypic variation explained by genetic risk scores over a covariate-only model.

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    <p>Error bars represent 95% confidence intervals. Red: African Americans; Blue: Asian Americans; Yellow: Caucasians; Green: Hispanics.</p
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