netgwas: An R Package for Network-Based Genome-Wide Association Studies

Graphical models are powerful tools for modeling and making statistical inferences regarding complex associations among variables in multivariate data. In this paper we introduce the R package netgwas, which is designed based on undirected graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium (LD) networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks. The netgwas package deals with species with any chromosome copy number in a unified way, unlike other software. It implements recent improvements in both linkage map construction (Behrouzi and Wit, 2018), and reconstructing conditional independence network for non-Gaussian continuous data, discrete data, and mixed discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely occur in genetics and genomics such as genotype data, and genotype-phenotype data. We demonstrate the value of our package functionality by applying it to various multivariate example datasets taken from the literature. We show, in particular, that our package allows a more realistic analysis of data, as it adjusts for the effect of all other variables while performing pairwise associations. This feature controls for spurious associations between variables that can arise from classical multiple testing approach. This paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. The package uses a parallelization strategy on multi-core processors to speed-up computations for large datasets. In addition, it contains several functions for simulation and visualization. The netgwas package is freely available at https://cran.r-project.org/web/packages/netgwasComment: 32 pages, 9 figures; due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

Pheno2Geno : high-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background: Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers. Results: The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping. We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population. Conclusion: The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.n

Environmental tuning of the genetic control of seed performance : a systems genetics approach

The environmental conditions under which plants grow affect the quality of seeds produced in a genotype-dependent manner. In nature, genotype-by-environment interactions are often observed however little is known about the underlying mechanisms. The combined use of genetic tools and omics data can help to explore the influence of the environment on the genetic control of seed performance. The research presented in this thesis explores genotype-by-environment interaction at the phenotypic with an effort to connect phenotypic changes to changes observed at the metabolome and transcriptome in a systems genetics approach. For this purpose, an Arabidopsis thaliana recombinant inbred lines population derived from the cross between the parental lines Bay-0 and Sha was grown under different conditions, namely standard, high light, high temperature and low phosphate conditions from flowering until seed harvest. The germination properties of the seeds produced under the different environments were investigated and the seed germination QTLs identified displayed large QTL-by-environment interaction. Quantitative changes in primary metabolites in response to the maternal environment were investigated by GC-TOF-MS. Further, mQTLs under the different environments were identified. RNA-seq of the same lines enabled to explore changes in gene expression across genotypes and environments as well as differences in the eQTL landscape under the different maternal environment. The findings of this research show that seed quality is largely influenced by genotype-by-environment interactions which result in large changes at the molecular level. The data generated provide many opportunities to further study.</p

Translational software infrastructure for medical genetics

Diep in de kern van onze cellen zetelt het desoxyribonucleïnezuur (DNA) molecuul die bekend staat als het genoom.DNA codeert de informatie die het leven laat groeien, overleven, diversifiëren en evolueren.Helaas kunnen dezelfde mechanismes die ons laten aanpassen aan een veranderende omgeving ook genetische aandoeningen veroorzaken.Hoewel we in staat zijn een aantal van deze aandoeningen op te sporen door moderne technologische vorderingen, moet er nog veel ontdekt en begrepen worden.Dit proefschrift draagt software infrastructuur aan om de moleculaire oorzaak van genetische aandoeningen te onderzoeken, laat zien hoe nieuwe bevindingen vertaald worden van fundamenteel onderzoek naar nieuwe software voor genoom diagnostiek, en introduceert een raamwerk voor genetische analyses die de automatisering en validatie van nieuwe software ondersteunt voor toepassing in de patientenzorg.Eerst ontwikkelen we datamodellen en software die helpt te bepalen welke gebieden op het genoom verantwoordelijk zijn voor ziektes en andere fysieke kenmerken.Vervolgens trekken we deze principes door naar modelorganismen.Door moleculaire gelijkenissen te gebruiken, ontdekken we nieuwe manieren om nematodes in te zetten voor onderzoek naar menselijke ziektes.Daarnaast kunnen we onze kennis van het genoom en de evolutie gebruiken om te voorspellen hoe pathogeen nieuwe mutaties zijn.Het resultaat is een publieke website waar DNA snel en accuraat gescand kan worden op mogelijk ziekteverwekkende mutaties.Tenslotte presenteren we een compleet systeem voor geautomatiseerde DNA analyse, inclusief een protocol specifiek voor genoom diagnostiek om overzichtelijke patient rapportages te produceren voor medisch experts waarmee een diagnose sneller en makkelijker gesteld kan worden.Deep inside the core of our cells resides the deoxyribonucleic acid (DNA) molecule known as the genome.DNA encodes the information that allows life to grow, survive, diversify and evolve.Unfortunately, the same mechanisms that let us adapt to a changing environment can also cause genetic disorders.While we are able to diagnose a number of these disorders using modern technological advancements, much remains to be discovered and understood.This thesis presents software infrastructure for investigating the molecular etiology of genetic disease using data from model organisms, demonstrates how to translate findings from fundamental research into new software tools for genome diagnostics, and introduces a downstream genome analysis framework that assists the automation and validation of the latest tools for applied patient care.We first develop data models and software to help determine which region of the genome is responsible for diseases and other physical traits.We then extend these principles towards model organisms.By using molecular similarities, we discover new ways to use nematodes for research into human diseases.Additionally, we can use our knowledge of the genome and evolution to predict how pathogenic new mutations are.The result is a public website where DNA can be scanned quickly and accurately for probable pathogenic mutations.Finally, we present a complete system for automated DNA analysis, including a protocol specific for genome diagnostics to produce clear patient reports for medical experts with which a diagnosis is made faster and easier