Ashley Petersen

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Prehospital traumatic cardiac arrest: Management and outcomes from the resuscitation outcomes consortium epistry-trauma and PROPHET registries.

BACKGROUND Traumatic arrests have historically had poor survival rates. Identifying salvageable patients and ideal management is challenging. We aimed to (1) describe the management and outcomes of prehospital traumatic arrests; (2) determine regional variation in survival; and (3) identify Advanced Life Support (ALS) procedures associated with survival. METHODS This was a secondary analysis of cases from the Resuscitation Outcomes Consortium Epistry-Trauma and Prospective Observational Prehospital and Hospital Registry for Trauma (PROPHET) registries. Patients were included if they had a blunt or penetrating injury and received cardiopulmonary resuscitation. Logistic regression analyses were used to determine the association between ALS procedures and survival. RESULTS We included 2,300 patients who were predominately young (Epistry mean [SD], 39 [20] years; PROPHET mean [SD], 40 [19] years), males (79%), injured by blunt trauma (Epistry, 68%; PROPHET, 67%), and treated by ALS paramedics (Epistry, 93%; PROPHET, 98%). A total of 145 patients (6.3%) survived to hospital discharge. More patients with blunt (Epistry, 8.3%; PROPHET, 6.5%) vs. penetrating injuries (Epistry, 4.6%; PROPHET, 2.7%) survived. Most survivors (81%) had vitals on emergency medical services arrival. Rates of survival varied significantly between the 12 study sites (p = 0.048) in the Epistry but not PROPHET (p = 0.14) registries. Patients in the PROPHET registry who received a supraglottic airway insertion or intubation experienced decreased odds of survival (adjusted OR, 0.27; 95% confidence interval, 0.08–0.93; and 0.37; 95% confidence interval, 0.17–0.78, respectively) compared to those receiving bag-mask ventilation. No other procedures were associated with survival. CONCLUSIONS Survival from traumatic arrest may be higher than expected, particularly in blunt trauma and patients with vitals on emergency medical services arrival. Although limited by confounding and statistical power, no ALS procedures were associated with increased odds of survival. LEVEL OF EVIDENCE Prognostic study, level IV.

Fused Lasso Additive Model

We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two datasets. Supplemental materials are available online, and the R package flam is available on CRAN.

Evaluating methods for combining rare variant data in pathway-based tests of genetic association

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or Fisher’s combined probability method). In the second approach, rare variant methods (e.g., CMC and WS) are applied directly to sets of single-nucleotide polymorphisms (SNPs) representing all SNPs within genes in a pathway. In this paper we use simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17 to analyze sets of rare variants using these two competing approaches. The initial results suggest substantial differences in the methods, with Fisher’s combined probability method and the direct application of the WS method yielding the best power. Evidence suggests that the WS method works well in most situations, although Fisher’s method was more likely to be optimal when the number of causal SNPs in the set was low but the risk of the causal SNPs was high.

Classification of RNA-seq Data

Next-generation sequencing technologies have made it possible to obtain, at a relatively low cost, a detailed snapshot of the RNA transcripts present in a tissue sample. The resulting reads are usually binned by gene, exon, or other region of interest; thus the data typically amount to read counts for tens of thousands of features, on no more than dozens or hundreds of observations. It is often of interest to use these data to develop a classifier in order to assign an observation to one of several pre-defined classes. However, the high dimensionality of the data poses statistical challenges: because there are far more features than observations, many existing classification techniques cannot be directly applied. In recent years, a number of proposals have been made to extend existing classification approaches to the high-dimensional setting. In this chapter, we discuss the use of, and modifications to, logistic regression, linear discriminant analysis, principal components analysis, partial least squares, and the support vector machine in the high-dimensional setting. We illustrate these methods on two RNA-sequencing data sets.

Incorporating prior knowledge to increase the power of genome-wide association studies.

Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variants genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.