20 research outputs found

    S1 Data -

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    Table A. Continuous Q-risk outcome regression results for LDL-C polygenic deviators, for all methods. Table B. Binary outcome regression results for LDL-C polygenic deviators, for all methods. Analyses where the logistic regression model did not converge are labelled with “NA”. Table C. SNP weights used to calculate the polygenic score for height (GIANT meta-analysis excluding UKB and 23&Me). (XLSX)</p

    Fig 3 -

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    Scatter plots showing the distribution of individuals who deviate (red) and do not deviate (black) from their genetic predictor for height, based on a) Mahalanobis distances with P 0.001 and b) P 0.05/n, c) regression residuals at the 2SD and d) 3SD threshold, e) GRS centiles with a Q3 + 1.5 IQR and f) Q3 + 3 IQR threshold, and finally g) GRS rank with P 0.001 and (h) P (1/10000).</p

    Phenotypic criteria for filtering genes catalogued in OMIM and described as causal for syndromes associated with stature.

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    Table A. List of 238 genes with prior evidence for a causal association with syndromes associated with stature, filtered on those with evidence of a dominant inheritance relationship. Table B. The number of individuals who are defined as deviating from their polygenic score for height using different methodologies, split by those relatively tall and relatively short for their polygenic score (total n = 158,951). Table C. Percentage overlap of individuals classified as shorter than expected for their polygenic score for height across derivation methods Table D. Percentage overlap of individuals classified as taller than expected for their polygenic score for height across derivation methods. Note, no individuals were classified as being relatively tall when using a Mahalanobis-based P-value threshold = 0.05/n. Table E. Empirical P-values for enrichment in individuals who are short relative to their genetically predicted height across all deviator definitions. Table F. Empirical P-values for enrichment in individuals who are tall relative to their genetically predicted height across all deviator definitions. No individuals were classified as being relatively tall when using a Mahalanbobis-based P-value threshold = 0.05/n. Table G. Number of individuals, and percentage of population, identified as deviating from their polygenic score for measured LDL using different methodologies. Table H. UKB Fields used to derive Q-risk measures. (PDF)</p

    Fig 4 -

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    Scatter plots showing the distribution of individuals who deviate (red) and do not deviate (black) deviate their genetic predictor for LDL cholesterol, based on a) Mahalanobis distances with P 0.001 and b) P 0.05/n, c) regression residuals at the 2SD and d) 3SD threshold, e) GRS centiles with a Q3 + 1.5 IQR and f) Q3 + 3 IQR threshold, and finally g) GRS rank with P 0.001 and (h) P (1/10000).</p

    Fig 2 -

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    a) Observed (red) and simulated (black) polygenic scores and standardized height adjusted for age, sex and assessment centre. b) Individuals aligned (black) and misaligned (red) to genetically predicted height defined using Mahalanobis distance P 0.001 and being more than 2 standard deviations away from the mean of the residual distribution generated by regressing the polygenic score against height. Individuals who were neither classified as aligned or misaligned were removed from 2)b).</p

    Replication results for top signals from APCAT (Stage1 <i>N</i> = 18,604) in additional studies (Stage 2 <i>N</i> = 15,576) and in GABRIEL.

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    a<p>Positions/alleles are relative to the forward strand of NCBI build36. <sup>b,c</sup>Results from GABRIEL are from a re-analysis using fixed-effects meta-analysis, excluding the B58C and ECRHS2 cohorts which are included in Stage2 or with occupational asthma (see Methods), and are for the APCAT SNP or the best available proxy. All p values are two-tailed.</p
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