Systems genetics for evolutionary studies
Prins, Pjotr ; Smant, Geert ; Arends, Danny ; Mulligan, Megan K. ; Williams, Rob W. ; Jansen, Ritsert C. - \ 2019
In: Evolutionary Genomics / Anisimova, Maria, New York : Humana Press Inc. (Methods in Molecular Biology ) - ISBN 9781493990733 - p. 635 - 652.
eQTL - Evolution - GEMMA - GeneNetwork - Genetical genomics - Genomics - LMM - Metabolomics - Network inference - NGS - QTL - R-genes - R/qtl - Systems genetics - xQTL
Systems genetics combines high-throughput genomic data with genetic analysis. In this chapter, we review and discuss application of systems genetics in the context of evolutionary studies, in which high-throughput molecular technologies are being combined with quantitative trait locus (QTL) analysis in segregating populations. The recent explosion of high-throughput data—measuring thousands of RNAs, proteins, and metabolites, using deep sequencing, mass spectrometry, chromatin, methyl-DNA immunoprecipitation, etc.—allows the dissection of causes of genetic variation underlying quantitative phenotypes of all types. To deal with the sheer amount of data, powerful statistical tools are needed to analyze multidimensional relationships and to extract valuable information and new modes and mechanisms of changes both within and between species. In the context of evolutionary computational biology, a well-designed experiment and the right population can help dissect complex traits likely to be under selection using proven statistical methods for associating phenotypic variation with chromosomal locations. Recent evolutionary expression QTL (eQTL) studies focus on gene expression adaptations, mapping the gene expression landscape, and, tentatively, define networks of transcripts and proteins that are jointly modulated sets of eQTL networks. Here, we discuss the possibility of introducing an evolutionary “prior” in the form of gene families displaying evidence of positive selection, and using that prior in the context of an eQTL experiment for elucidating host-pathogen protein-protein interactions. Here we review one exemplar evolutionairy eQTL experiment and discuss experimental design, choice of platforms, analysis methods, scope, and interpretation of results. In brief we highlight how eQTL are defined; how they are used to assemble interacting and causally connected networks of RNAs, proteins, and metabolites; and how some QTLs can be efficiently converted to reasonably well-defined sequence variants.
Restriction associated DNA-genotyping at multiple spatial scales in Arabidopsis lyrata reveals signatures of pathogen-mediated selection
Buckley, James ; Holub, Eric B. ; Koch, Marcus A. ; Vergeer, Philippine ; Mable, Barbara K. - \ 2018
BMC Genomics 19 (2018)1. - ISSN 1471-2164
Arabidopsis lyrata - Balancing selection - Disease resistance - Genome scan - Mating system - Pathogens - Polymorphism - R-genes - RAD-seq
Background: Genome scans based on outlier analyses have revolutionized detection of genes involved in adaptive processes, but reports of some forms of selection, such as balancing selection, are still limited. It is unclear whether high throughput genotyping approaches for identification of single nucleotide polymorphisms have sufficient power to detect modes of selection expected to result in reduced genetic differentiation among populations. In this study, we used Arabidopsis lyrata to investigate whether signatures of balancing selection can be detected based on genomic smoothing of Restriction Associated DNA sequencing (RAD-seq) data. We compared how different sampling approaches (both within and between subspecies) and different background levels of polymorphism (inbreeding or outcrossing populations) affected the ability to detect genomic regions showing key signatures of balancing selection, specifically elevated polymorphism, reduced differentiation and shifts towards intermediate allele frequencies. We then tested whether candidate genes associated with disease resistance (R-gene analogs) were detected more frequently in these regions compared to other regions of the genome. Results: We found that genomic regions showing elevated polymorphism contained a significantly higher density of R-gene analogs predicted to be under pathogen-mediated selection than regions of non-elevated polymorphism, and that many of these also showed evidence for an intermediate site-frequency spectrum based on Tajima's D. However, we found few genomic regions that showed both elevated polymorphism and reduced FST among populations, despite strong background levels of genetic differentiation among populations. This suggests either insufficient power to detect the reduced population structure predicted for genes under balancing selection using sparsely distributed RAD markers, or that other forms of diversifying selection are more common for the R-gene analogs tested. Conclusions: Genome scans based on a small number of individuals sampled from a wide range of populations were sufficient to confirm the relative scarcity of signatures of balancing selection across the genome, but also identified new potential disease resistance candidates within genomic regions showing signatures of balancing selection that would be strong candidates for further sequencing efforts.