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

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes

<div><p>Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.</p></div

Power and type I errors of meta-analysis of single variant tests in the presence of missing data for continuous outcomes.

<p>Datasets were simulated according to the genetic and phenotype model described in METHODS. Meta-analysis was performed to combine 20 cohorts with 1500 individuals each. For each replicate, summary association statistics were generated, and a certain fraction of the generated summary statistics were masked as missing. Scenarios with different combinations of known variant effects, candidate variant effects and fractions of missingness were considered. Six analysis strategies were considered: 1) PCBS; 2) SYN+; 3) ImpG+meta; 4) COJO; 5) DISCARD and 6) REPLACE0. Type I error and power were evaluated using 10⁵ replicates under the significance threshold of <i>α</i> = 0.005.</p

Independently associated variants identified using sequential forward selection with PCBS method.

<p>Sequential conditional analyses for the 9 loci were conducted, where we iteratively performed conditional analysis, conditioning on the top variants from earlier rounds. Top association signals at each iteration are shown. The sequential conditional analysis stops when the top association signal is no longer significant under the genome-wide significance threshold <i>α</i> = 5 × 10⁻⁸.</p

Power and type I errors of meta-analysis of gene-level tests in the presence of missing data.

<p>Datasets were simulated according to the genetic and phenotype model described in METHODS. Within the gene region, 20% of the variant sites are deemed causal. Meta-analysis was performed to combine 10 cohorts with 2000 individuals each. For each replicate, summary association statistics were generated, and a certain fraction (10%, 30% or 50%) of the generated summary statistics were masked as missing. Scenarios with different combinations of known variant effect, candidate variant effects and fractions of missingness were considered. To evaluate the power loss due to missing data, we also analyzed the full dataset as a gold standard. Type I errors and power were evaluated for three rare variant tests (simple burden, SKAT and VT) using 1 million replicates under the significance threshold of <i>α</i> = 0.005.</p