Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits.

Summary Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes. Availability and implementation The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.

Objective The goal of this study was to investigate whether there is a genotype by treatment interaction in patients experiencing stroke and treated with one of three antihypertensive drugs, i.e. chlorthalidone, amlodipine and lisinopril. Methods A population of 436 African Americans and 539 whites that have experienced stroke in the GenHAT study were genotyped for 768 single nucleotide polymorphisms in 280 candidate genes. To detect a genotype by treatment interaction we used the Pearson's chi-square test to assess if the genotype frequencies differed at the single SNP level for the three drug treatment groups. From these single SNP analyses we derived a summary statistic for the degree of association at the gene and gene complex levels. This was done by grouping SNPs using information on gene locations and defining gene complexes based on protein-protein interactions. To assess the statistical significance of the observed test statistic we derived an empirical p-value by simulating data under the null hypothesis. Results We found that, in patients that have experienced stroke, there is a significant genetic difference between hypertension drug treatment groups. In African Americans SNP rs12143842 showed significant association (p < 0.001) with drug treatment. At gene-level HNRNPA1P4 and NOS1AP in African Americans and PRICKLE1 and NINJ2 in non-Hispanic whites were significantly associated (p < 0.01) to drug treatment, while none of the gene-complexes tested showed significance. Conclusions Based on the genetic differences between drug treatment groups, we conclude that there may be an interaction between certain genotypes and antihypertensive treatment in stroke patients. This needs to be replicated in other studies.

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Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, performing single and multiple marker association analyses, and genomic-based predictions of phenotypes. Availability and implementation: The R package qgg is freely available. For latest updates, user guides and example scripts, consult the main page

Neurotoxicant-induced elevation of intracellular calcium (Ca(2+)) and modulation by phystoestrogens were examined in vitro using human neuroblastoma SH-SY5Y cells cultured with amyloid beta-peptide (Abeta) and 1-methyl-4-phenyl-pyridine (MPP+). Although Abeta itself did not increase Ca(2+), it exacerbated the effects of carbachol. The elevation of Ca(2+) caused by the agents in combination could be reduced by pretreatment with the phytoestrogens equol and genistein, as well as by the L-type Ca(2+) channel blocker nifedipine. MPP+ exposure also elevated Ca(2+), an effect blocked by nifedipine but not by the phytoestrogens. As opposed to phytoestrogens, nifedipine was also able to significantly reduce cell death caused by higher concentrations of MPP(+) in the LDH viability assay. The results suggest that phytoestrogens are unlikely to serve as general cellular protectants for neurotoxicants with different mechanisms of action. The concentrations of Abeta and MPP(+) affecting Ca(2+) release did not inhibit cell viability as measured with the LDH release assay. This indicates that mechanisms involved with toxicity can be studied at doses that are not lethal.

Summary: Here, we present an expanded utility of the R package qgg for quantitative genetic and genomic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes five different Bayesian Linear Regression (BLR) models, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study (GWAS) summary statistics. To showcase some of the novel implementations, we analysed two quantitative trait phenotypes, body mass index and standing height from United Kingdom Biobank (UKB). We compared genomic prediction accuracies from single and multiple trait models, showed accurate estimation of genomic parameters, illustrate how a BLR model can be used to fine map potential causal loci, and finally, provide an extension of gene set enrichment analyses based on the BLR framework. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software tools and data formats. Availability: Our methodology is implemented in the publicly available R software package qgg using fast and memory efficient algorithms in C++ and is available from CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available from our accompanied homepage https://qganalytics.com/. Contact: palledr@hst.aau.dk and pso@qgg.au.dk Supplementary information: Supplementary data are available online.