Biology subjects

Zaykin, D.

Publications and source records attributed to Zaykin, D..

4 recordsLinked to original sources

bioRxiv2019

Methods for combining multiple correlated biomarkers with application to the study of low-grade inflammation and muscle mass in senior horses

The simplest analysis of biomarker data is based on a series of single biomarker hypothesis tests, followed by correction for multiple testing. However, it is intuitively plausible that a joint analysis of multiple biomarkers will have higher statistical power and promise improved discrimination over tests based on single markers. In this article, we study analytical properties of the approach for joint analysis of correlated summary statistics based on the test for quadratic forms (TQ). Based on the derivation of the TQ-distribution, we proposed a scale-location approximation of the TQ statistic, which we call approximate TQ. We show that the approximate TQ has very similar power to the traditional TQ test under varying correlation structures among biomarkers. Our application of both the TQ and the approximate TQ test to data on biomarkers for inflamm-aging - an age-related low-grade chronic inflammation - reveals an association between the percentage of IFN{gamma} positive lymphocytes and overall muscle condition in senior horses.

genetics↗

bioRxiv2019

Quantifying posterior effect size distribution of susceptibility loci by common summary statistics

Testing millions of SNPs in genetic association studies has become standard routine for disease gene discovery, followed by prioritization of the strongest signals based on the set of the smallest P-values. In light of recent re-evaluation of statistical practice, it has been suggested that P-values are unfit as summaries of statistical evidence. Despite this criticism, P-values are commonly used and are unlikely to be abandoned by practitioners. Moreover, P-values contain information that can be utilized to address the concerns about their flaws and misuse. We present a new method for utilizing evidence summarized by P-values for estimating odds ratio (OR) based on its approximate posterior distribution. In our method, only P-value, sample size, and standard deviation for log(OR) are needed as summaries of data, accompanied by a suitable prior distribution for log(OR) that can assume any shape. The parameter of interest, log(OR), is the only parameter with a specified prior distribution, hence our model is a mix of classical and Bayesian approaches. We show that our \"Mix Bayes\" (MB) method retains the main advantages of the Bayesian approach: it yields direct probability statements about hypotheses for OR and is resistant to biases caused by selection of top-scoring SNPs. MB enjoys greater flexibility than similarly inspired methods in the assumed distribution for the summary statistic and in the form of the prior for the parameter of interest. We illustrate our method by presenting interval estimates of effect size for reported genetic associations with lung cancer. Although we focus on OR, our method is not limited to this particular measure of effect size and can be used broadly for assessing reliability of findings in studies testing multiple predictors.

genetics↗

bioRxiv2019

Detecting weak signals by combining small P-values in genetic association studies

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the Rank Truncated Product (RTP), have been developed for combining top-ranking associations and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking SNPs, while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation, practical implementation, and hinders further developments. Here, we propose the Augmented Rank Truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of {micro}-opioid receptor variants with sensitivity to pain.

genetics↗

bioRxiv2019

DOT: Gene-set analysis by combining decorrelated association statistics

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer breast cancer risk.

genetics↗