MOTIVATION: In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive. RESULTS: We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 variants, MendelIHT completed a three-trait joint analysis (n=185 656) in 20 h and an 18-trait joint analysis (n=104 264) in 53 h with an 80 GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY AND IMPLEMENTATION: Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl.

MOTIVATION: Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. RESULTS: We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. AVAILABILITY AND IMPLEMENTATION: Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools. RESULTS: We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research. CONCLUSION: The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.

MOTIVATION: The distribution of allele frequencies across polymorphic sites, also known as the site frequency spectrum (SFS), is of primary interest in population genetics. It is a complete summary of sequence variation at unlinked sites and more generally, its shape reflects underlying population genetic processes. One practical challenge is that inferring the SFS from low coverage sequencing data in a straightforward manner by using genotype calls can lead to significant bias. To reduce bias, previous studies have used a statistical method that directly estimates the SFS from sequencing data by first computing site allele frequency (SAF) likelihood for each site (i.e. the likelihood a site has each possible allele frequency conditional on observed sequence reads) using a dynamic programming (DP) algorithm. Although this method produces an accurate SFS, computing the SAF likelihood is quadratic in the number of samples sequenced. RESULTS: To overcome this computational challenge, we propose an algorithm, 'score-limited DP' algorithm, which is linear in the number of genomes to compute the SAF likelihood. This algorithm works because in a lower triangular matrix that arises in the DP algorithm, all non-negligible values of the SAF likelihood are concentrated on a few cells around the best-guess allele counts. We show that our score-limited DP algorithm has comparable accuracy but is faster than the original DP algorithm. This speed improvement makes SFS estimation practical when using low coverage NGS data from a large number of individuals. AVAILABILITY AND IMPLEMENTATION: The program will be available via a link from the Novembre lab website (http://jnpopgen.org/).

Abstract Summary: Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. Availability: Free from www.genetics.ucla.edu/software/mendel Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: This article extends our recent research on penalized estimation methods in genome-wide association studies to the realm of rare variants. Results: The new strategy is tested on both simulated and real data. Our findings on breast cancer data replicate previous results and shed light on variant effects within genes. Availability: Rare variant discovery by group penalized regression is now implemented in the free program Mendel at http://www.genetics.ucla.edu/software/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.