A comparative analysis of current phasing and imputation software.

Whole-genome data has become significantly more accessible over the last two decades. This can largely be attributed to both reduced sequencing costs and imputation models which make it possible to obtain nearly whole-genome data from less expensive genotyping methods, such as microarray chips. Although there are many different approaches to imputation, the Hidden Markov Model (HMM) remains the most widely used. In this study, we compared the latest versions of the most popular HMM-based tools for phasing and imputation: Beagle5.4, Eagle2.4.1, Shapeit4, Impute5 and Minimac4. We benchmarked them on four input datasets with three levels of chip density. We assessed each imputation software on the basis of accuracy, speed and memory usage, and showed how the choice of imputation accuracy metric can result in different interpretations. The highest average concordance rate was achieved by Beagle5.4, followed by Impute5 and Minimac4, using a reference-based approach during phasing and the highest density chip. IQS and R2 metrics revealed that Impute5 and Minimac4 obtained better results for low frequency markers, while Beagle5.4 remained more accurate for common markers (MAF>5%). Computational load as measured by run time was lower for Beagle5.4 than Minimac4 and Impute5, while Minimac4 utilized the least memory of the imputation tools we compared. ShapeIT4, used the least memory of the phasing tools examined with genotype chip data, while Eagle2.4.1 used the least memory phasing WGS data. Finally, we determined the combination of phasing software, imputation software, and reference panel, best suited for different situations and analysis needs and created an automated pipeline that provides a way for users to create customized chips designed to optimize their imputation results.

SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration.

BACKGROUND: Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files. RESULTS: SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss. CONCLUSIONS: SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.

EZTraits: A programmable tool to evaluate multi-site deterministic traits.

The vast majority of human traits, including many disease phenotypes, are affected by alleles at numerous genomic loci. With a continually increasing set of variants with published clinical disease or biomarker associations, an easy-to-use tool for non-programmers to rapidly screen VCF files for risk alleles is needed. We have developed EZTraits as a tool to quickly evaluate genotype data against a set of rules defined by the user. These rules can be defined directly in the scripting language Lua, for genotype calls using variant ID (RS number) or chromosomal position. Alternatively, EZTraits can parse simple and intuitive text including concepts like 'any' or 'all'. Thus, EZTraits is designed to support rapid genetic analysis and hypothesis-testing by researchers, regardless of programming experience or technical background. The software is implemented in C++ and compiles and runs on Linux and MacOS. The source code is available under the MIT license from https://github.com/selfdecode/rd-eztraits.

A Novel Mitochondrial Complex of Aldosterone Synthase, Steroidogenic Acute Regulatory Protein, and Tom22 Synthesizes Aldosterone in the Rat Heart.

Aldosterone, which regulates renal salt retention, is synthesized in adrenocortical mitochondria in response to angiotensin II. Excess aldosterone causes myocardial injury and heart failure, but potential intracardiac aldosterone synthesis has been controversial. We hypothesized that the stressed heart might produce aldosterone. We used blue native gel electrophoresis, immunoblotting, protein crosslinking, coimmunoprecipitations, and mass spectrometry to assess rat cardiac aldosterone synthesis. Chronic infusion of angiotensin II increased circulating corticosterone levels 350-fold and induced cardiac fibrosis. Angiotensin II doubled and telmisartan inhibited aldosterone synthesis by heart mitochondria and cardiac production of aldosterone synthase (P450c11AS). Heart aldosterone synthesis required P450c11AS, Tom22 (a mitochondrial translocase receptor), and the intramitochondrial form of the steroidogenic acute regulatory protein (StAR); protein crosslinking and coimmunoprecipitation studies showed that these three proteins form a 110-kDa complex. In steroidogenic cells, extramitochondrial (37-kDa) StAR promotes cholesterol movement from the outer to inner mitochondrial membrane where cholesterol side-chain cleavage enzyme (P450scc) converts cholesterol to pregnenolone, thus initiating steroidogenesis, but no function has previously been ascribed to intramitochondrial (30-kDa) StAR; our data indicate that intramitochondrial 30-kDa StAR is required for aldosterone synthesis in the heart, forming a trimolecular complex with Tom22 and P450c11AS. This is the first activity ascribed to intramitochondrial StAR, but how this promotes P450c11AS activity is unclear. The stressed heart did not express P450scc, suggesting that circulating corticosterone (rather than intracellular cholesterol) is the substrate for cardiac aldosterone synthesis. Thus, the stressed heart produced aldosterone using a previously undescribed intramitochondrial mechanism that involves P450c11AS, Tom22, and 30-kDa StAR. SIGNIFICANCE STATEMENT: Prior studies of potential cardiac aldosterone synthesis have been inconsistent. This study shows that the stressed rat heart produces aldosterone by a novel mechanism involving aldosterone synthase, Tom22, and intramitochondrial steroidogenic acute regulatory protein (StAR) apparently using circulating corticosterone as substrate. This study establishes that the stressed rat heart produces aldosterone and for the first time identifies a biological role for intramitochondrial 30-kDa StAR.