RIL-StEp: epistasis analysis of rice recombinant inbred lines (RILs) reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single-nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms

Detecting gene–gene interactions from GWAS using diffusion kernel principal components

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Genetic interactions affecting human gene expression identified by variance association mapping

Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE). Using RNA-sequence data from lymphoblastoid cell lines (LCLs) from the TwinsUK cohort, we identify a candidate set of 508 variance associated SNPs. Exploiting the twin design we show that GxE plays a role in ∼70% of these associations. Further investigation of these loci reveals 57 epistatic interactions that replicated in a smaller dataset, explaining on average 4.3% of phenotypic variance. In 24 cases, more variance is explained by the interaction than their additive contributions. Using molecular phenotypes in this way may provide a route to uncovering genetic interactions underlying more complex traits.DOI: http://dx.doi.org/10.7554/eLife.01381.001

Evolutionary history of the SARS-CoV-2 Gamma variant of concern (P.1): a perfect storm

Our goal was to describe in more detail the evolutionary history of Gamma and two derived lineages (P.1.1 and P.1.2), which are part of the arms race that SARS-CoV-2 wages with its host. A total of 4,977 sequences of the Gamma strain of SARS-CoV-2 from Brazil were analyzed. We detected 194 sites under positive selection in 12 genes/ORFs: Spike, N, M, E, ORF1a, ORF1b, ORF3, ORF6, ORF7a, ORF7b, ORF8, and ORF10. Some diagnostic sites for Gamma lacked a signature of positive selection in our study, but these were not fixed, apparently escaping the action of purifying selection. Our network analyses revealed branches leading to expanding haplotypes with sites under selection only detected when P.1.1 and P.1.2 were considered. The P.1.2 exclusive haplotype H_5 originated from a non-synonymous mutational step (H3509Y) in H_1 of ORF1a. The selected allele, 3509Y, represents an adaptive novelty involving ORF1a of P.1. Finally, we discuss how phenomena such as epistasis and antagonistic pleiotropy could limit the emergence of new alleles (and combinations thereof) in SARS-COV-2 lineages, maintaining infectivity in humans, while providing rapid response capabilities to face the arms race triggered by host immuneresponses

Efficient strategies for epistasis detection in genome-wide data

Genome-Wide Association Studies have been carried out with SNP array technology since 2005, identifying thousands of loci for a great many traits and diseases. There are now large data sources, such as UK biobank, that provide medical and genetic data of hundreds-of-thousands of people. However, there is a shortfall in the heritability explained for the phenotypes that have been assessed. One of the explanations for this deficit is interactions between genes, called epistasis, that are not detected and so part of the causation missed. In this thesis, I carry out a comprehensive review of the large number of available epistasis detection tools in the literature. This is followed by a simulation benchmarking study to assess the ability of a representative group of these tools to detect epistatic interactions. From these tools, BOOST, MDR and MPI3SNP found the most interactions in this simulation study. Next, I set out three possible strategies for searching in biobank scale data in order to find a best practices workflow. These were exhaustive searching, an approach tailored to the tools' strengths and by splitting the data into linkage disequilibrium-based haplotype blocks and reducing the computational load. A simulation study was devised that found a mixed approach, using both BOOST and MDR for different types of interactions. The final pipeline initially uses the BOOST algorithm to find pure epistatic interactions and filter out insignificant pairs of SNPs. Those remaining variants with large single-locus effect sizes are assessed with MDR for impure interactions. Those interactions that are identified are assessed for significance, effect size and heritability explained. Finally, validation is carried out across each interacting pair, incorporating numerous sources of a priori knowledge. This was applied to Atrial Fibrillation, Alzheimer's Disease and Parkinson's Disease, three diseases that have previously been assessed for interactions. Although no statistically significant results were identified, this approach demonstrated an increased amount of heritability explained, showing that some of the missing heritability could be accounted for this way. A downstream analysis method was devised, finding genes in linkage with the interacting loci, applying a number of functional annotations and searching STRING-db for evidence of known interactions. Finally, the study was extended to examine rare variants in rare disease congenital hypothyroidism. As a systemic disorder, it could potentially have pathological interacting mutations. After variant calling, four de novo variants were identified, potentially explaining the condition. Six related interactions were found, with one not present in the parents, so possibly explaining the condition. The mutations, present in TG and PDIA4 have evidence of an interaction in STRING-db and both being involved in thyroid hormone synthesis in the KEGG database. These contributions provide a novel, tested pipeline for identifying epistasis from GWAS data, as well as a corpus of simulated data for future researchers. A robust methodology is applied for testing resulting interactions statistically, as well as an approach for validating interactions by incorporating numerous data sources to find significant commonalities between variants

Using regulatory variants to detect gene-gene interactions identifies networks of genes linked to cell immortalization

The extent to which the impact of regulatory genetic variants may depend on other factors, such as the expression levels of upstream transcription factors, remains poorly understood. Here we report a framework in which regulatory variants are first aggregated into sets, and using these as estimates of the total cis-genetic effects on a gene we model their non-additive interactions with the expression of other genes in the genome. Using 1220 lymphoblastoid cell lines across platforms and independent datasets we identify 74 genes where the impact of their regulatory variant-set is linked to the expression levels of networks of distal genes. We show that these networks are predominantly associated with tumourigenesis pathways, through which immortalised cells are able to rapidly proliferate. We consequently present an approach to define gene interaction networks underlying important cellular pathways such as cell immortalisation

HOW THE MUTATIONAL-SELECTION INTERPLAY ORGANIZES THE FITNESS LANDSCAPE

Fundamental questions posed in classical genetics since early 20th century are still fundamental in today post genomic age. What has changed is the availability of huge amount of molecular genetics information on a broad spectrum of species and a more powerful and rich methodological approach, particularly that one based on statistical mechanics and dynamical system theory which is providing unprecedented prediction power. Here we focus on the behavior of basic life forms such as bacteria and viruses which have small genomes and short generation times. We show that central issues of the evolutionary theory, i.e. how genotype, phenotype and fitness are related, the effect of positive and negative natural selection, the specie formation could be described by simple models which allow predictions and validation using experimental data

Soybean Iron Deficiency Chlorosis: Quantitative Trait Locus Validation and Grain Yield Evaluation

Soybean production in most of the Northern Great Plain area has been challenged by iron deficiency chlorosis (IDC), which is a physiological problem with a plant grown in high pH, calcareous soil. Developing IDC-resistant cultivars is the best approach to meet this challenge. Currently, this approach is limited by lack of knowledge about genetic resources and mechanisms for resistance to IDC. The objectives of this research were to validate quantitative trait loci (QTL) associated with IDC and to evaluate the effect of IDC on yield in soybean cultivars. To validate the QTL, a population of 201 recombinant inbred lines, which was developed from a cross between a cultivated (Glycine max) and a wild (G. soja) soybean line, was grown in a field with calcareous soil (pH 8.5) using a randomized complete block design with four replicates. Phenotypes of IDC were visually scored for individual lines at three-time points during the vegetative growth period starting from the V3 stage. Heritability estimates for IDC scores ranged from 0.26 to 0.71. A linkage map was constructed using 164 SSR markers and covers 2156 cM of the soybean genome a total of 11 QTL for Fe efficiency were detected, with six detected in more than one time points. One of the 11 QTL has the allele from the wild parent enhancing the resistance to IDC. Seven of the QTL were involved in digenic epistasis. Two of the QTL were involved in G-by-E interactions. The epistatic and G-by-E interactions demonstrate the importance of evaluating IDC responses in multiple environments. The validated QTL may contain useful genes for breeding IDC-resistant varieties by pyramiding of the Fe-efficiency alleles. Furthermore, to evaluate the effect of IDC on yield potential, twenty-three soybean cultivars were examined. The results showed the further need for improvement toward better resistance to IDC. The one-year yield test of five cultivars, bearing different levels of resistance to IDC, confirmed the effect of this stress on yield leading to a high yield of resistant cultivars under chlorotic soil and lower when grown on the non-chlorotic soil