Association of genetic variants rs7759938 (upper panel) and rs314279 (lower panel) with adult metabolic profiles.

<p>All phenotypes were standardized prior to analyses. Two regression models were used: Model A including age as a covariate, and Model B including both the alternative SNP and age as covariates. Subjects using lipid lowering drugs or with lipid levels +/−5 standard deviations (SD) from the mean were excluded from the lipid/lipoprotein analysis. Only subjects with normal glucose tolerance not receiving treatment for diabetes were included in the regression analyses of glucose and serum insulin. The effect allele for both rs7759938 and rs314279 is C. ApoA1 =  Apolipoprotein A1, ApoB  =  Apolipoprotein B, FP  =  Fasting plasma. FS  =  Fasting serum, 2H glucose  =  Plasma glucose concentrations at 2 h after a 75 g oral glucose load. Participants of the oral glucose test were instructed to fast for 10 hours. ^# The variable was logarithm-transformed prior to the analysis.</p

Association of genetic variants rs7759938 (upper panel) and rs314279 (lower panel) with adult anthropometric traits.

<p>All phenotypes were standardized prior to analyses. Two regression models were used: Model A including age as a covariate, Model B including both the alternative SNP and age as covariates. The effect allele for both rs7759938 and rs314279 is C. BMI  =  body mass index, WHR  =  waist to hip ratio.</p

The number of childhood adverse life events affects relative telomere length at adult age.

<p>Relative telomere length is adjusted for age and sex. Each bar presents group mean (± standard error of the mean).</p

Telomere length as a function of age.

<p>Anxiety disorder core and subthreshold cases (N = 321) are shown with red dots and controls (N = 653) with blue dots, each dot representing one individual. Regression lines for both groups are shown with the same color coding.</p

Telomere length is affected by childhood adverse life events but not by anxiety disorder diagnosis or recent psychological stress.

1<p>Difference in standardized telomere length for one unit or category change in each independent variable.</p>2<p>Standard error of the mean.</p>3<p>Sum score of the 12-item General Health Questionnaire.</p>4<p>Categorized to 0 adversities, 1 adversity, 2 or 3 adversities, and 4 or more adversities.</p><p>Results from three independent regression models are shown in which sex and age adjusted telomere length was explained by either anxiety disorder status, GHQ-12 score, or number of childhood adversities.</p

Multidimensional scaling (MDS) plots.

<p>Each point represents an individual genome. Red triangles are individuals from the Kuopio region of Finland while violet triangles are individuals from Helsinki. Each blue circle represents a proband from each of the 13 CDGP families. The yellow circle is the proband from Family 1. Panel A shows the relationship between principal components (PCs) 1 and 2, which explain most of the genetic variation. Panel B shows PC1 versus PC3, which appear to mimic a geographical northeast to southwest axis.</p

Pedigrees of the 13 families included in the study.

<p>Circles represent females and squares are males. Filled circles are classified as affected with CDGP, while shaded circles are unknown or do not fulfill the criteria for CDGP. The proband from each family is marked with an arrow. Individuals with an asterisk (*) have been sequenced at the pericentromere of chr 2. All probands and both of their parents were genotyped (denoted with the symbol #) except family 11, in which only the proband and affected parent were genotyped.</p

DataSheet1_Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury.pdf

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury.Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models.Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p −8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p −6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000–1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320–3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372–0.835, p = 0.005) were significant in the full model.Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.</p

Analysis strategy.

<p>The flow of statistical analysis and the amount of entities (genes/transcripts) after each step are illustrated. a) The 15101 entities that passed the filtering by flags and reannotation included 1292 up-regulated (red) and 1039 down-regulated (green) transcripts with at least 1.2-fold change from baseline (BL) to sleep restriction (SR) in sleep-restricted subjects ( = cases). * The pathway analysis was run for these genes. b) The 2331 entities were analyzed with 2-way ANOVA using the case/control status and the three timepoints as analysis axes. Changes with ANOVA interaction <i>P</i> value <0.05 were observed in 227 up-regulated and 83 down-regulated transcripts. c) Altogether 310 entities were further analyzed with 1-way repeated measures ANOVA considering the three timepoints. The 43 entities showing changes also in the control group were excluded from the analysis. The 133 genes with 1-way ANOVA <i>P</i> value <0.05 for the cases but not for the controls were then analyzed using a <i>t</i> test between the timepoints BL and SR. 62 genes were up-regulated and 55 down-regulated (P<0.05).</p

Expression changes after partial sleep restriction.

<p>The 310 entities (genes/transcripts) with interaction <i>P</i> value (<i>P</i><0.05) in 2-way ANOVA, sorted by average fold change from baseline (BL) to sleep restriction (SR) (with the up-regulated (red) on top, followed by the down-regulated (green). Each lane represents one individual (sleep deprived subjects, N = 9; controls, N = 4), and colour codes represent the fold change from BL to SR (BL  = 1).</p