General Approaches for Combining Multiple Rare Variant Associate Tests Provide Improved Power Across a Wider Range of Genetic Architecture

In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by providing a general method that works for combining an arbitrarily large number of any gene‐based rare variant test—a flexibility typically not available in other combined testing methods. We provide a theoretical framework for evaluating our combined test to provide direct insights into the relationship between test‐test correlation, test power and the combined test power relative to individual testing approaches and other combined testing approaches. We demonstrate that our flexible combined testing method can provide improved power and robustness against a wide range of genetic architectures. We further demonstrate the performance of our combined test on simulated genotypes, as well as on a dataset of real genotypes with simulated phenotypes. We support the increased use of flexible combined tests in practice to maximize robustness of rare‐variant testing strategies against a wide‐range of genetic architectures

Serrate RNA effector molecule (SRRT) is associated with prostate cancer progression and is a predictor of poor prognosis in lethal prostate cancer

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRT's potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.Prostate Cancer Foundation Young Investigator Award ; Prostate Cancer Canada ; Canadian Cancer Society (CCS

UMP, SCUN seal bilateral collaborations

Universiti Malaysia Pahang (UMP) fortified its international networking when it sealed a Memorandum of Understanding (MoU) with China’s South-Central University for Nationalities (SCUN) in Beijing, China, on June 1

General Approach for Combining Diverse Rare Variant Association Tests Provides Improved Robustness Across a Wider Range of Genetic Architectures

The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we propose an expansion of these approaches, yielding a general method that works for combining any number of individual tests. We demonstrate that running multiple different tests on the same dataset and using a Bonferroni correction for multiple testing is never better than combining tests using our general method. We also find that using a test statistic that is highly robust to the inclusion of non-causal variants (Joint-infinity) together with a previously published combined test (SKAT-O) provides improved robustness to a wide range of genetic architectures and should be considered for use in practice. Software for this approach is supplied. We support the increased use of combined tests in practice-- as well as further exploration of novel combined testing approaches using the general framework provided here--to maximize robustness of rare-variant testing strategies against a wide range of genetic architectures

Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability

University of Minnesota Ph.D. dissertation August . 2018. Major: Biostatistics. Advisor: Saonli Basu. 1 computer file (PDF); x, 127 pages.The development of a complex human disease is an intricate interplay of genetic and environmental factors. Broadly speaking, “heritability” is defined as the proportion of total trait variance due to genetic factors within a given population. Over the past 50 years, studies involving monozygotic and dizygotic twins have estimated the heritability of over 17,800 human traits [1]. Genetic association studies that measure thousands to millions of genetic “markers” have attempted to determine the exact markers that explain a given trait’s heritability. However, often the identified set of “statistically-significant” markers fails to explain more than 10% of the estimated heritability of a trait [2], which has been defined as the “missing heritability” problem [3][4]. “Missing heritability’ implies that many genetic markers that contribute to disease risk are still waiting to be discovered. Identification of the exact genetic markers associated with a disease is important for the development of pharmaceutical drugs that may target these markers (see [5] for recent examples). Additionally, “missing heritability” may imply that we are inaccurately estimating heritability in the first place [3, 4, 6], thus motivating the development of more robust models for estimating heritability. This dissertation focuses on two objectives that attempt to address the missing heritability problem: (1) develop a more robust framework for estimating heritability; and (2) develop powerful association tests in attempt to find more genetic markers associated with a given trait. Specifically: in Chapter 2, robust variance component models are developed for estimating heritability in twin studies using second-order generalized estimating equations (GEE2). We demonstrate that GEE2 can improve coverage rates of the true heritability parameter for non-normally distributed outcomes, and can easily incorporate both mean and variance-level covariate effects (e.g. let heritability vary by sex or age). In Chapter 3, penalized regression is used to jointly model all genetic markers. It is demonstrated that jointly modeling all markers can improve power to detect individual associated markers compared to conventional methods that model each marker “one-at-a-time.” Chapter 4 expands on this work by developing a more flexible nonparametric Bayesian variable selection model that can account for non-linear or non-additive effects, and can also test biologically meaningful groups of markers for an association with the outcome. We demonstrate how the nonparametric Bayesian method can detect markers with complex association structures that more conventional models might miss

Resampling-based tests for Lasso in genome-wide association studies

Abstract Background Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings. Methods We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study. Results We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions. Conclusion Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies

Interest in Medication and Aspiration Abortion Training among Colorado Nurse Practitioners, Nurse Midwives, and Physician Assistants

ObjectivesWe examined advanced practice clinicians' (APCs: nurse practitioners [NPs], certified nurse midwives [CNMs], physician assistants) interest in training to provide medication and aspiration abortion in Colorado, where abortion provision by APCs is legal.MethodsWe surveyed a stratified random sample of APCs, oversampling women's health (CNMs/women's health nurse practitioners [WHNPs]) and rural APCs. We examined prevalence and predictors of interest in abortion training using weighted χ2 tests.ResultsOf 512 participants (21% response), the weighted sample is 50% NPs, 41% physician assistants, and 9% CNMs/WHNPs; 55% provide primary care. Only 12% are aware they can legally provide abortion. A minority of participants disagree that medication abortion (15%) or aspiration abortion (25%) should be in APC scope of practice. Almost one-third (29%) are interested in medication abortion training and 16% are possibly interested; interest is highest among CNMs/WHNPs (52%) (p < .01). Interest in aspiration abortion training is 15% with another 11% who are possibly interested; interest is highest among CNMs/WHNPs (34%) (p < .01). There are no significant differences in abortion training interest by rural practice location or by receipt of abortion education in graduate school. Participants not interested in medication and aspiration abortion training cited abortion being outside their specialty practice scope (44% and 38%, respectively) and religious or personal objections (42% and 34%). Among clinicians interested in medication abortion training, 33% believe their clinical facility is likely to allow them to provide this service, compared with 16% for aspiration abortion.ConclusionsInterest in abortion training among Colorado APCs is substantial. However, facility barriers to abortion provision must be addressed to increase abortion access with APCs

Single-Cell Analysis in Lung Adenocarcinoma Implicates RNA Editing in Cancer Innate Immunity and Patient Prognosis.

RNA editing modifies single nucleotides of RNAs, regulating primary protein structure and protein abundance. In recent years, the diversity of proteins and complexity of gene regulation associated with RNA editing dysregulation has been increasingly appreciated in oncology. Large-scale shifts in editing have been observed in bulk tumors across various cancer types. However, RNA editing in single cells and individual cell types within tumors has not been explored. By profiling editing in single cells from lung adenocarcinoma biopsies, we found that the increased editing trend of bulk lung tumors was unique to cancer cells. Elevated editing levels were observed in cancer cells resistant to targeted therapy, and editing sites associated with drug response were enriched. Consistent with the regulation of antiviral pathways by RNA editing, higher editing levels in cancer cells were associated with reduced antitumor innate immune response, especially levels of natural killer cell infiltration. In addition, the level of RNA editing in cancer cells was positively associated with somatic point mutation burden. This observation motivated the definition of a new metric, RNA editing load, reflecting the amount of RNA mutations created by RNA editing. Importantly, in lung cancer, RNA editing load was a stronger predictor of patient survival than DNA mutations. This study provides the first single cell dissection of editing in cancer and highlights the significance of RNA editing load in cancer prognosis.SignificanceRNA editing analysis in single lung adenocarcinoma cells uncovers RNA mutations that correlate with tumor mutation burden and cancer innate immunity and reveals the amount of RNA mutations that strongly predicts patient survival. See related commentary by Luo and Liang, p. 351