scholarly journals Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

Genetics ◽  
2019 ◽  
Vol 211 (3) ◽  
pp. 1105-1122 ◽  
Author(s):  
Nicholas Santantonio ◽  
Jean-Luc Jannink ◽  
Mark Sorrells
Keyword(s):  
Dna Marker ◽  
Breeding Population ◽  
Functional Redundancy ◽  
Epistatic Interactions ◽  
Statistical Framework ◽  
Epistasis Model ◽  
Gene Sets ◽  
Genome Wide ◽  
Genetic Signal ◽  
Allohexaploid Wheat

Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions.

scholarly journals A subfunctionalization epistasis model to evaluate homeologous gene interactions in allopolyploid wheat

2018 ◽  
Author(s):  
Nicholas Santantonio ◽  
Jean-Luc Jannink ◽  
Mark E. Sorrells
Keyword(s):  
Breeding Population ◽  
Functional Redundancy ◽  
Epistatic Interactions ◽  
Statistical Framework ◽  
Epistasis Model ◽  
Gene Sets ◽  
Genome Wide ◽  
Genetic Signal ◽  
Homeologous Gene ◽  
Allohexaploid Wheat

1AbstractHybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq vl.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the non-additive genetic signal, but are less important than other epistatic interactions.

scholarly journals TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders

2018 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Na Cai ◽  
Vladimir I. Vladimirov ◽  
Anna Docherty ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Computational Burden ◽  
Gene Sets ◽  
Genome Wide ◽  
Genetic Signal ◽  
Meta Analyses ◽  
Combine Information ◽  
Or Genes

ABSTRACTGenetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), e.g. across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred as transcriptomic wide association analysis (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effect of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular bases of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to it quadratic (in the number of SNPs) computational burden for computing LD across large regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that i) has a linear computational burden, ii) uses a large and diverse reference panel (33K subjects), iii) is competitive (adjusts for background enrichment in gene TWAS statistics) and iv) is applicable as-is to ethnically mixed cohorts. To underline its potential for increasing the power to uncover genetic signals over the state-of-the-art and commonly used non-transcriptomics methods, e.g. MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

scholarly journals MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

2020 ◽  
Author(s):  
Sisheng Liu ◽  
Jinpeng Liu ◽  
Yanqi Xie ◽  
Tingting Zhai ◽  
Eugene W Hinderer ◽  
...  
Keyword(s):  
Mutation Rate ◽  
De Novo ◽  
R Package ◽  
Supplementary Information ◽  
Driver Mutations ◽  
Mutual Exclusivity ◽  
Statistical Framework ◽  
Gene Sets ◽  
Genome Wide ◽  
Background Mutation Rate

Abstract Motivation Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. Results We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. Availability and implementation MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Two-State Epistasis Model Reduces Missing Heritability of Complex Traits

2015 ◽  
Author(s):  
Kerry L. Bubb ◽  
Christine Queitsch
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Additive Model ◽  
Twin Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Epistatic Interactions ◽  
Epistasis Model ◽  
Genome Wide

ABSTRACTDespite decade-long efforts, the genetic underpinnings of many complex traits and diseases remain largely elusive. It is increasingly recognized that a purely additive model, upon which most genome-wide association studies (GWAS) rely, is insufficient. Although thousands of significant trait-associated loci have been identified, purely additive models leave much of the inferred genetic variance unexplained. Several factors have been invoked to explain the ‘missing heritability’, including epistasis. Accounting for all possible epistatic interactions is computationally complex and requires very large samples. Here, we propose a simple two-state epistasis model, in which individuals show either high or low variant penetrance with respect to a certain trait. The use of this model increases the power to detect additive trait-associated loci. We show that this model is consistent with current GWAS results and improves fit with heritability observations based on twin studies. We suggest that accounting for variant penetrance will significantly increase our power to identify underlying additive loci.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Increasing selection gain and accuracy of harvest prediction models in Jatropha through genome-wide selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriano dos Santos ◽  
Erina Vitório Rodrigues ◽  
Bruno Galvêas Laviola ◽  
Larissa Pereira Ribeiro Teodoro ◽  
Paulo Eduardo Teodoro ◽  
...  
Keyword(s):  
Prediction Models ◽  
Block Design ◽  
Phenotypic Selection ◽  
Breeding Population ◽  
Breeding Cycle ◽  
Selection Cycle ◽  
Genome Wide ◽  
Random Block ◽  
Random Block Design ◽  
Number Of Individuals

AbstractGenome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.

Genome‐wide interaction study reveals epistatic interactions for beef lipid‐related traits in Nellore cattle

2021 ◽  
Author(s):  
S. T. Amorim ◽  
N. B. Stafuzza ◽  
S. Kluska ◽  
E. Peripolli ◽  
A. S. C. Pereira ◽  
...  
Keyword(s):  
Interaction Study ◽  
Epistatic Interactions ◽  
Genome Wide ◽  
Nellore Cattle

scholarly journals Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples

2018 ◽  
Vol 214 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Chiara Fabbri ◽  
Siegfried Kasper ◽  
Alexander Kautzky ◽  
Lucie Bartova ◽  
Markus Dold ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Adenosine Monophosphate ◽  
Research Support ◽  
Antidepressant Treatments ◽  
Advisory Boards ◽  
Gene Sets ◽  
Genome Wide ◽  
A Genome ◽  
Resistant Depression

BackgroundTreatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.AimsTo investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine.MethodA sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set (i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761) participants.ResultsNo individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027).ConclusionsThe identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant treatments and targets for new antidepressants.Declaration of interestD.S. has received grant/research support from GlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. S.M. has been a consultant or served on advisory boards for: AstraZeneca, Bristol-Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, Richter. S.K. has received grant/research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, Janssen and Neuraxpharm. J.Z. has received grant/research support from Lundbeck, Servier, Brainsway and Pfizer, has served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca and Roche and has served on speakers' bureaus for Lundbeck, Roch, Lilly, Servier, Pfizer and Abbott. J.M. is a member of the Board of the Lundbeck International Neuroscience Foundation and of Advisory Board of Servier. A.S. is or has been consultant/speaker for: Abbott, AbbVie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi and Servier. C.M.L. receives research support from RGA UK Services Limited.

scholarly journals Molecular neuroanatomy of anorexia nervosa

2018 ◽  
Author(s):  
Derek Howard ◽  
Priscilla Negraes ◽  
Aristotle N. Voineskos ◽  
Allan S. Kaplan ◽  
Alysson Muotri ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Fetal Brain ◽  
Marker Genes ◽  
Genetic Studies ◽  
Gene Sets ◽  
Genome Wide ◽  
Calcitonin Gene ◽  
Ventral Tegmental ◽  
Deep Brain ◽  
Cell Study

AbstractAnorexia nervosa is a complex eating disorder with genetic, metabolic, and psychosocial underpinnings. Using unbiased genome-wide methods, recent studies have associated a variety of genes with the disorder. We characterized these genes by projecting them into aggregated gene expression data from reference transcriptomic atlases of the prenatal and adult human brain. We found that genes from an induced stem cell study of anorexia nervosa are expressed at higher levels in the lateral parabrachial and the ventral tegmental areas. The adult expression enrichment of the lateral parabrachial is confirmed with genes from two independent genetic studies. In the fetal brain, enrichment of the ventral tegmental area is also observed for the six genes near the only common variant associated with the disorder (rs4622308). We also observed signals in the adult and fetal pontine raphe, but they were not observed when using the genes from the genetic studies. In addition to signals related to calcitonin gene-related peptide neurons and the tachykinin, we found more than the expected number of microglia marker genes within the gene sets. Using mouse transcriptomic data, we identified several anorexia nervosa associated genes that are differentially expressed during food deprivation. While these genes that respond to fasting are not enriched in the gene sets, we highlightRPS26which is proximal to rs4622308. We did not observe expression enrichment in the cingulate cortex or hypothalamus suggesting other targets for deep brain stimulation should be considered for severe cases. This work improves our understanding of the neurobiological causes of anorexia nervosa by suggesting disturbances in subcortical appetitive circuits.

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