Odds Ratios and 95% confidence intervals representing the odds of a stroke diagnosis per 1 unit increase in log transformed urinary tungsten (expressed as µg per mg of urinary creatinine) for NHANES participants less than 75 years of age or less than 50 years of age.

a<p>The adjusted models include age, sex, ethnicity, SES, smoking, occupation, BMI, hypertenstion, hypercholesterolemia, molybdenum and cobalt concentration as covariates.</p>b<p>In this model all urinary tungsten measures were included, including the 503 individuals with a concentration below the lowest detectable limit.</p><p>Statistical significance is denoted by ^?, * and ** representing <i>P</i><0.1, <i>P</i><0.05 and <i>P</i><0.01 respectively.</p

Demographics of the pooled dataset and the weighted mean urinary tungsten concentration for the various demographic variables.

<p><i>P</i> values represent statistical significance of the different tungsten concentrations for each demographic variable. The <i>P</i> value presented in () includes an adjustment for urinary creatinine. Statistical comparisons excluded individuals in the unknown categoriesTables.</p

Odds Ratios and 95% confidence intervals representing the odds of a CVD diagnosis per 1 unit increase in log transformed urinary tungsten (expressed as µg per mg of urinary creatinine) for NHANES participants less than 75 years of age or less than 50 years of age.

a<p>The adjusted models include age, sex, ethnicity, SES, smoking, alcohol consumption, occupation, BMI, hypertenstion, hypercholesterolemia, molybdenum and cobalt concentration as covariates.</p>b<p>In this model all urinary tungsten measures were included, including the 503 individuals with a concentration below the lowest detectable limit.</p><p>Statistical significance is denoted by * representing <i>P</i><0.05.</p

Bar charts representing the mean urinary tungsten concentrations in individuals with and without a self-reported stroke or CVD diagnosis.

<p>Error bars represent the 95% confidence intervals and the <i>P</i> value represents the comparison of tungsten concentrations in people with and without a stroke diagnosis.</p

Fig 4 -

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

Odds ratio of an individual being a carrier of a loss-of-function variant in one of three genes known to affect LDL-C levels: (<i>LDLR</i>, <i>APOB</i> and <i>PCSK9</i>) if they were classified as misaligned to their LDL-C polygenic score.

Odds ratio of an individual being a carrier of a loss-of-function variant in one of three genes known to affect LDL-C levels: (LDLR, APOB and PCSK9) if they were classified as misaligned to their LDL-C polygenic score.</p

Odds ratio per standard deviation increase in Q-Risk exposure phenotypes with respect to being classified as a deviating for a polygenic score for LDL cholesterol.

Odds ratio per standard deviation increase in Q-Risk exposure phenotypes with respect to being classified as a deviating for a polygenic score for LDL cholesterol.</p

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

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

S1 Data -

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

A density plot of standardized polygenic scores for height plotted against standardized height for 158,951 unrelated individuals from the UK Biobank.

A density plot of standardized polygenic scores for height plotted against standardized height for 158,951 unrelated individuals from the UK Biobank.</p