Search

We genotyped a population of 618 diploid potato clones derived from six independent potato-breeding programmes from NW-Europe. The diploids were phenotyped for 23 traits, using standardized protocols and common check varieties, enabling us to derive whole population estimators for most traits. We subsequently performed a genome-wide association study (GWAS) to identify quantitative trait loci (QTL) for all traits with SNPs and short-read haplotypes derived from read-backed phasing. In this study, we used a marker platform called PotatoMASH (Potato Multi-Allele Scanning Haplotags); a pooled multiplex amplicon sequencing based approach. Through this method, neighboring SNPs within an amplicon can be combined to generate multiallelic short-read haplotypes (haplotags) that capture recombination history between the constituent SNPs and reflect the allelic diversity of a given locus in a different way than single bi-allelic SNPs. We found a total of 37 unique QTL across both marker types. A core of 10 QTL was detected with SNPs as well as with haplotags. Haplotags allowed to detect an additional 14 QTL not found based on the SNP set. Conversely, the bi-allelic SNP set also found 13 QTL not detectable using the haplotag set. We conclude that both marker types should routinely be used in parallel to maximize the QTL detection power. We report 19 novel QTL for nine traits: Skin Smoothness, Sprout Dormancy, Total Tuber Number, Tuber Length, Yield, Chipping Color, After-cooking Blackening, Cooking Type, and Eye depth., Competing Interests: Conflicts of interest The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this publication., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Garden roses are an economically important horticultural crop worldwide, and two major fungal pathogens, black spot (Diplocarpon rosae F.A. Wolf) and cercospora leaf spot of rose (Rosisphaerella rosicola Pass.), affect both the health and ornamental value of the plant. Most studies on black spot disease resistance have focused on diploid germplasm, and little work has been performed on cercospora leaf spot resistance. With the use of newly developed software tools for autopolyploid genetics, two interconnected tetraploid garden rose F1 populations (phenotyped over the course of 3 years) were used for quantitative trait locus (QTL) analysis of black spot and cercospora leaf spot resistance as well as plant defoliation. QTLs for black spot resistance were mapped to linkage groups (LGs) 1–6. QTLs for cercospora resistance and susceptibility were found in LGs 1, 4, and 5 and for defoliation in LGs 1, 3, and 5. The major locus on LG 5 for black spot resistance coincides with the previously discovered Rdr4 locus inherited from Rosa L. ‘Radbrite’ (Brite Eyes™), the common parent used in these mapping populations. This work is the first report of any QTL for cercospora resistance/susceptibility in tetraploid rose germplasm and the first report of defoliation QTL in roses. A major QTL for cercospora susceptibility coincides with the black spot resistance QTL on LG 5 (Rdr4). A major cercospora resistance QTL was found on LG 1. These populations provide a genetic resource that will further the knowledge base of rose genetics as more traits are studied. Studying more traits from these populations will allow for the stacking of various QTLs for desirable traits.

Garden roses are an economically important horticultural crop worldwide, and two major fungal pathogens, black spot ( Diplocarpon rosae F.A. Wolf) and cercospora leaf spot of rose ( Rosisphaerella rosicola Pass.), affect both the health and ornamental value of the plant. Most studies on black spot disease resistance have focused on diploid germplasm, and little work has been performed on cercospora leaf spot resistance. With the use of newly developed software tools for autopolyploid genetics, two interconnected tetraploid garden rose F 1 populations (phenotyped over the course of 3 years) were used for quantitative trait locus (QTL) analysis of black spot and cercospora leaf spot resistance as well as plant defoliation. QTLs for black spot resistance were mapped to linkage groups (LGs) 1-6. QTLs for cercospora resistance and susceptibility were found in LGs 1, 4, and 5 and for defoliation in LGs 1, 3, and 5. The major locus on LG 5 for black spot resistance coincides with the previously discovered Rdr4 locus inherited from Rosa L. 'Radbrite' (Brite Eyes™), the common parent used in these mapping populations. This work is the first report of any QTL for cercospora resistance/susceptibility in tetraploid rose germplasm and the first report of defoliation QTL in roses. A major QTL for cercospora susceptibility coincides with the black spot resistance QTL on LG 5 ( Rdr4 ). A major cercospora resistance QTL was found on LG 1. These populations provide a genetic resource that will further the knowledge base of rose genetics as more traits are studied. Studying more traits from these populations will allow for the stacking of various QTLs for desirable traits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lau, Gill, Taniguti, Young, Klein, Byrne and Riera-Lizarazu.)

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline. [ABSTRACT FROM AUTHOR]

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community. [ABSTRACT FROM AUTHOR]

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait–variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP. [ABSTRACT FROM AUTHOR]

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses. [ABSTRACT FROM AUTHOR]

Background: The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F1 populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes. Results: Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285–4674 markers per map, with sizes ranging from c. 1643–2292 cM, and mean marker intervals of 0.7–2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1–3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F1 population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits. Conclusions: Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits. [ABSTRACT FROM AUTHOR]

Background: Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry. Methods: Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual's heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods. Results: The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of − 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set. Conclusions: Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels. [ABSTRACT FROM AUTHOR]

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

Almond breeding is increasingly focusing on kernel quality. However, unlike other agronomic traits, the genetic basis of physical and chemical kernel quality traits has been poorly investigated. To address this gap, we conducted a QTL mapping of these traits to enhance our understanding of their genetic control. We phenotyped fruit samples from an F1 population derived from the cross between 'Marcona' and 'Marinada' for up to four years, using conventional and image analysis methods. Additionally, the 91 individuals of the population were genotyped with the almond Axiom™ 60K SNP array, and high-density linkage maps were constructed. These analyses identified several genomic regions of breeding interest. For example, two regions on chromosome one were found to contain QTLs for kernel shape and dimension, while another region at the end of the same chromosome contained QTLs for kernel fatty acid composition. Notably, QTLs for kernel symmetry and kernel shoulder, reported for the first time in this study, were also mapped on chromosome one. These QTLs will serve as a foundation for developing molecular markers linked to kernel physical and chemical quality traits in almonds, facilitating the integration of marker-assisted selection into breeding programs. [ABSTRACT FROM AUTHOR]

Global protein consumption is increasing exponentially, which requires efficient identification of potential, healthy, and simple protein sources to fulfil the demands. The existing sources of animal proteins are high in fat and low in fiber composition, which might cause serious health risks when consumed regularly. Moreover, protein production from animal sources can negatively affect the environment, as it often requires more energy and natural resources and contributes to greenhouse gas emissions. Thus, finding alternative plant-based protein sources becomes indispensable. Rapeseed is an important oilseed crop and the world's third leading oil source. Rapeseed byproducts, such as seed cakes or meals, are considered the best alternative protein source after soybean owing to their promising protein profile (30%–60% crude protein) to supplement dietary requirements. After oil extraction, these rapeseed byproducts can be utilized as food for human consumption and animal feed. However, anti-nutritional factors (ANFs) like glucosinolates, phytic acid, tannins, and sinapines make them unsuitable for direct consumption. Techniques like microbial fermentation, advanced breeding, and genome editing can improve protein quality, reduce ANFs in rapeseed byproducts, and facilitate their usage in the food and feed industry. This review summarizes these approaches and offers the best bio-nutrition breakthroughs to develop nutrient-rich rapeseed byproducts as plant-based protein sources. [ABSTRACT FROM AUTHOR]

We postulated that type 2 diabetes (T2D) predisposes patients to exocrine pancreatic diseases through (epi)genetic mechanisms. We explored the methylome (using MethylationEPIC arrays) of the exocrine pancreas in 141 donors, assessing the impact of T2D. An epigenome-wide association study of T2D identified hypermethylation in an enhancer of the pancreatic lipase–related protein 1 (PNLIPRP1) gene, associated with decreased PNLIPRP1 expression. PNLIPRP1 null variants (found in 191,000 participants in the UK Biobank) were associated with elevated glycemia and LDL cholesterol. Mendelian randomization using 2.5M SNP Omni arrays in 111 donors revealed that T2D was causal of PNLIPRP1 hypermethylation, which in turn was causal of LDL cholesterol. Additional AR42J rat exocrine cell analyses demonstrated that Pnliprp1 knockdown induced acinar-to-ductal metaplasia, a known prepancreatic cancer state, and increased cholesterol levels, reversible with statin. This (epi)genetic study suggests a role for PNLIPRP1 in human metabolism and exocrine pancreatic function, with potential implications for pancreatic diseases. Article Highlights: We performed this study to identify epigenetic changes with type 2 diabetes (T2D) in the pancreas. This study addresses whether T2D induces epigenetic changes that could explain why individuals with T2D are more prone to pancreatic diseases. We found hypermethylation at PNLIPRP1 associated with T2D and identified a role of this gene in cholesterol metabolism. This study has important implications in the prevention of pancreatic diseases, because their molecular mechanisms remain largely unknown. [ABSTRACT FROM AUTHOR]

Introduction: Yam is an important crop for food security in East and West Africa due to its high market value and customer demand. High tuber quality with yield and disease resistance are the main traits for acceptability of yam cultivars across the tropical zone. There has been limited progress in enhancing the production and quality traits of yams, despite the significant socio-economic significance of this crop. Method: To expedite the development of high-quality yam cultivars in Uganda, traits association study was conducted to identify genomic regions associated with key traits such as disease resistance, high yields, and dry matter content. The association mapping was conducted with multi-random mixed linear model (mrMLM) to compute the associations using five genetic models. Results: A total of 16 significant single nucleotide polymorphisms (SNPs) markers were identified to be associated with the traits studied. Gene identification analysis revealed the presence of key putative genes such as Vicilin-like seed storage protein At2g28490 (ARATH)and Growth-regulating factor 1 involved in a variety of functions ranging from storage and gene regulation for disease resistance. Discussion: The results obtained from this work have significant implications for the in-depth analysis of the genetic structure underlying key traits in yam. Additionally, this study emphasizes the identification of SNP variants and genes that may be utilized for genomic-informed selection in order to enhance yield and disease resistance in yams. [ABSTRACT FROM AUTHOR]

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects. [ABSTRACT FROM AUTHOR]

Introduction: Yam is an important crop for food security in East and West Africa due to its high market value and customer demand. High tuber quality with yield and disease resistance are the main traits for acceptability of yam cultivars across the tropical zone. There has been limited progress in enhancing the production and quality traits of yams, despite the significant socio-economic significance of this crop. Method: To expedite the development of high-quality yam cultivars in Uganda, traits association study was conducted to identify genomic regions associated with key traits such as disease resistance, high yields, and dry matter content. The association mapping was conducted with multi-random mixed linear model (mrMLM) to compute the associations using five genetic models. Results: A total of 16 significant single nucleotide polymorphisms (SNPs) markers were identified to be associated with the traits studied. Gene identification analysis revealed the presence of key putative genes such as Vicilin-like seed storage protein At2g28490 (ARATH)and Growth-regulating factor 1 involved in a variety of functions ranging from storage and gene regulation for disease resistance. Discussion: The results obtained from this work have significant implications for the in-depth analysis of the genetic structure underlying key traits in yam. Additionally, this study emphasizes the identification of SNP variants and genes that may be utilized for genomic-informed selection in order to enhance yield and disease resistance in yams. [ABSTRACT FROM AUTHOR]

Background/Objectives: Hybridity authentication is an important component of quality assurance and control (QA/QC) in breeding programs. Here, we introduce HybridQC v1.0, a QA/QC software program specially designed for parental purity and hybridity determination. HybridQC rapidly detects molecular marker polymorphism between parents of a cross and utilizes only the informative markers for hybridity authentication. Methods: HybridQC is written in Python and designed with a graphical user interface (GUI) compatible with Windows operating systems. We demonstrated the QA/QC analysis workflow and functionality of HybridQC using Kompetitive allele-specific PCR (KASP) SNP genotype data for cowpea (Vigna unguiculata). Its performance was validated in other crop data, including sorghum (Sorghum bicolor) and maize (Zea mays). Results: The application efficiently analyzed low-density SNP data from multiple cowpea bi-parental crosses embedded in a single Microsoft Excel file. HybridQC is optimized for the auto-generation of key summary statistics and visualization patterns for marker polymorphism, parental heterozygosity, non-parental alleles, missing data, and F1 hybridity. An added graphical interface correctly depicted marker efficiency and the proportions of true F1 versus self-fertilized progenies in the data sets used. The output of HybridQC was consistent with the results of manual hybridity discernment in sorghum and maize data sets. Conclusions: This application uses QA/QC SNP markers to rapidly verify true F1 progeny. It eliminates the extensive time often required to manually curate and process QA/QC data. This tool will enhance the optimization efforts in breeding programs, contributing to increased genetic gain. [ABSTRACT FROM AUTHOR]

With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops' audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research. [ABSTRACT FROM AUTHOR]

Genome-wide association studies using SNPs aim to capture QTL effects across the genome. However, the results of GWAS vary across studies, even when traits and populations are similar. Fluctuation in estimating SNP effects and variances can be caused by several biological factors such as selection, genetic architecture of trait, and population stratification, and by nonbiological factors, such as method and inflation of the predictor error for SNP effects. The current study aims to compare changes in SNP effects and GEBVs using different training populations. A simulation study was conducted using data from 20,000 individuals generated in the AlphaSimR package in R. Data for a trait with 10 chromosomes were simulated, and linkage disequilibrium was created by random crossing across 1,000 generations. The trait genetic architecture was controlled by 100 loci on each chromosome, accounting for 100% of the genetic variation. Pedigree, phenotypes, and genotypes for all individuals in the population were created, but phenotypes for individuals in the last generation were masked for validation purposes. The training population was then divided into three types comprising all, only odd, and only even animals. Genomic breeding values (GEBV) were estimated using the genomic information by single-step GBLUP. SNP effects and variance were calculated by back-solving the GEBVs. The correlation between all and odd populations was 0.83, all and even populations was 0.81, and odd and even populations were 0.40. Correlations for all 50k SNP effects between all and odd population was 0.75, the all and even population was 0.75, and the odd and even populations was 0.20. Changing the training population data by half led to small changes in accuracy. However, SNP effects fluctuated across populations more than GEBVs. Visual inspection of Manhattan plots showed that the peak regions varied with the training population used. The top regions were more stable and experienced fewer fluctuations, while the intermediate regions varied more often. The largest differences were between the odd and even scenarios. The regions explaining more variance were not the same, and the variations were greater when less data were used for training (odd and even). Overall, the fluctuations in SNP effects in GWAS highlight the complexity of genetic influences on traits, and how sensitive those studies are to small changes in the data. Understanding the factors that contribute to GWAS fluctuations may help improve the design and interpretation of such studies. We expect to derive a novel tool that uses the fluctuations of SNP effects to leverage QTL discovery. [ABSTRACT FROM AUTHOR]

Background: Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. Results: The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. Conclusion: HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding. [ABSTRACT FROM AUTHOR]

Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security1. Here we examined the genetic and phenotypic diversity of the A. E. Watkins landrace collection2 of bread wheat (Triticum aestivum), a major global cereal, by whole-genome re-sequencing of 827 Watkins landraces and 208 modern cultivars and in-depth field evaluation spanning a decade. We found that modern cultivars are derived from two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium-based haplotypes and association genetics analyses link Watkins genomes to the thousands of identified high-resolution quantitative trait loci and significant marker–trait associations. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritized quantitative trait loci in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilizing genetic diversity in crop improvement to achieve sustainable food security.Genomic and phenomic screens of 827 wheat landraces from the A. E. Watkins collection provide insight into the wheat population genetic background, unlocking many agronomic traits and revealing haplotypes that could potentially be used to improve modern wheat cultivars. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus (S. aureus) colonizes humans asymptomatically but can also cause opportunistic infections, ranging from mild skin infections to severe life-threatening conditions. Resistance and tolerance are two ways a host can survive an infection. Resistance is limiting the pathogen burden, while tolerance is limiting the health impact of a given pathogen burden. In previous work, we established that collaborative cross (CC) mouse line CC061 is highly susceptible to Methicillin-resistant S. aureus infection (MRSA, USA300), while CC024 is tolerant. To identify host genes involved in tolerance after S. aureus infection, we crossed CC061 mice and CC024 mice to generate F1 and F2 populations. Survival after MRSA infection in the F1 and F2 generations was 65% and 55% and followed a complex dominant inheritance pattern for the CC024 increased survival phenotype. Colonization in F2 animals was more extreme than in their parents, suggesting successful segregation of genetic factors. We identified a Quantitative Trait Locus (QTL) peak on chromosome 7 for survival and weight change after infection. In this QTL, the WSB/EiJ (WSB) allele was present in CC024 mice and contributed to their MRSA tolerant phenotype. Two genes, C5ar1 and C5ar2, have high-impact variants in this region. C5ar1 and C5ar2 are receptors for the complement factor C5a, an anaphylatoxin that can trigger a massive immune response by binding to these receptors. We hypothesize that C5a may have altered binding to variant receptors in CC024 mice, reducing damage caused by the cytokine storm and resulting in the ability to tolerate a higher pathogen burden and longer survival. Author summary: Staphylococcus aureus causes a wide range of life-threatening diseases in humans. Resistance and tolerance are two ways a host can survive an infection. Resistance is limiting the pathogen burden, while tolerance is limiting the health impact of a given pathogen burden. Tolerance mechanisms are poorly understood in context of host-pathogen interaction. To identify host genes involved in tolerance after S. aureus infection, we crossed CC061 mice and CC024 mice. The genetic factors controlling tolerance were well segregated in the F2 population. Using QTL mapping, we identified a significant peak on chromosome 7 for survival and weight change after infection. Two genes, C5ar1 and C5ar2, have high-impact variants in this region. We hypothesize that C5a may have altered binding to variant receptors in CC024 mice, reducing damage caused by the cytokine storm and resulting in the ability to tolerate a higher pathogen burden and longer survival. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) is mainly caused by Fusarium graminearum (Fg) and is a very widespread disease throughout the world, leading to severe damage to wheat with losses in both grain yield and quality. FHB also leads to mycotoxin contamination in the infected grains, being toxic to humans and animals. In spite of the continuous advancements to elucidate more and more aspects of FHB host resistance, to date, our knowledge about the molecular mechanisms underlying wheat defense response to this pathogen is not comprehensive, most likely due to the complex wheat–Fg interaction. Recently, due to climate changes, such as high temperature and heavy rainfall, FHB has become more frequent and severe worldwide, making it even more urgent to completely understand wheat defense mechanisms. In this review, after a brief description of the first wheat immune response to Fg, we discuss, for each FHB resistance type, from Type I to Type V resistances, the main molecular mechanisms involved, the major quantitative trait loci (QTLs) and candidate genes found. The focus is on multi-omics research helping discover crucial molecular pathways for each resistance type. Finally, according to the emerging examined studies and results, a wheat response model to Fg attack, showing the major interactions in the different FHB resistance types, is proposed. The aim is to establish a useful reference point for the researchers in the field interested to adopt an interdisciplinary omics approach. [ABSTRACT FROM AUTHOR]

Developing high-yielding rice varieties that possess favorable agronomic characteristics and enhanced grain Zn content is crucial in ensuring food security and addressing nutritional needs. This research employed ICIM, IM, and multi-parent population QTL mapping methods to identify important genetic regions associated with traits such as DF, PH, NT, NP, PL, YLD, TGW, GL, GW, Zn, and Fe. Two populations of recombinant inbred lines consisting of 373 lines were phenotyped for agronomic, yield and grain micronutrient traits for three seasons at IRRI, and genotyped by sequencing. Most of the traits demonstrated moderate to high broad-sense heritability. There was a positive relationship between Zn and Fe contents. The principal components and correlation results revealed a significant negative association between YLD and Zn/Fe. ICIM identified 81 QTLs, while IM detected 36 QTLs across populations. The multi-parent population analysis detected 27 QTLs with six of them consistently detected across seasons. We shortlisted eight candidate genes associated with yield QTLs, 19 genes with QTLs for agronomic traits, and 26 genes with Zn and Fe QTLs. Notable candidate genes included CL4 and d35 for YLD, dh1 for DF, OsIRX10, HDT702, sd1 for PH, OsD27 for NP, whereas WFP and OsIPI1 were associated with PL, OsRSR1 and OsMTP1 were associated to TGW. The OsNAS1, OsRZFP34, OsHMP5, OsMTP7, OsC3H33, and OsHMA1 were associated with Fe and Zn QTLs. We identified promising RILs with acceptable yield potential and high grain Zn content from each population. The major effect QTLs, genes and high Zn RILs identified in our study are useful for efficient Zn biofortification of rice. [ABSTRACT FROM AUTHOR]

With the rapid emergence and distribution of red crown rot (RCR) across countries, durable sources of resistance against Calonectria ilicicola in soybean [Glycine max (L.) Merrill] is required to control the disease. We employed two RIL populations for the experiment. We identified 15 and 14 QTLs associated with RCR resistance in ZM6 and MN populations, respectively, totaling 29 QTLs. Six and eight QTLs had phenotypic variation above 10% in ZM6 and MN populations, respectively. We identified six (6) "QTL hotspots" for resistance to RCR from the ZM6 and MN RIL populations on chromosomes 1, 7, 10, 11, 13, and 18. Gene annotations, gene ontology enhancement, and RNA sequencing assessment detected 23 genes located within six "QTL Hotspots" as potential candidate genes that could govern RCR resistance in soybeans. Our data will generally assist breeders in rapidly and effectively incorporating RCR resistance into high-yielding accession through marker-assisted selection. [ABSTRACT FROM AUTHOR]

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat. [ABSTRACT FROM AUTHOR]

Background: Association testing between molecular phenotypes and genomic variants can help to understand how genotype affects phenotype. RNA sequencing provides access to molecular phenotypes such as gene expression and alternative splicing while DNA sequencing or microarray genotyping are the prevailing options to obtain genomic variants. Results: We genotype variants for 74 male Braunvieh cattle from both DNA (~ 13-fold coverage) and deep total RNA sequencing from testis, vas deferens, and epididymis tissue (~ 250 million reads per tissue). We show that RNA sequencing can be used to identify approximately 40% of variants (7–10 million) called from DNA sequencing, with over 80% precision. Within highly expressed coding regions, over 92% of expected variants were called with nearly 98% precision. Allele-specific expression and putative post-transcriptional modifications negatively impact variant genotyping accuracy from RNA sequencing and contribute to RNA-DNA differences. Variants called from RNA sequencing detect roughly 75% of eGenes identified using variants called from DNA sequencing, demonstrating a nearly 2-fold enrichment of eQTL variants. We observe a moderate-to-strong correlation in nominal association p-values (Spearman ρ2 ~ 0.6), although only 9% of eGenes have the same top associated variant. Conclusions: We find hundreds of thousands of RNA-DNA differences in variants called from RNA and DNA sequencing on the same individuals. We identify several highly significant eQTL when using RNA sequencing variant genotypes which are not found with DNA sequencing variant genotypes, suggesting that using RNA sequencing variant genotypes for association testing results in an increased number of false positives. Our findings demonstrate that caution must be exercised beyond filtering for variant quality or imputation accuracy when analysing or imputing variants called from RNA sequencing. [ABSTRACT FROM AUTHOR]

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation. [ABSTRACT FROM AUTHOR]

Cassava (Manihot esculenta Crantz) was introduced to Southeast Asia in the 16th–17th centuries and has since flourished as an industrial crop. Since the 1980s, Thailand has emerged as the leading producer and exporter of cassava products. This growth coincided with the initiation of cassava breeding programs in collaboration with the International Center for Tropical Agriculture (CIAT), focusing on root yield and starch production. The success of Thai cassava breeding programs can be attributed to the incorporation of valuable genetic diversity from international germplasm resources to cross with the local landraces, which has become the genetic foundation of many Thai commercial varieties. Effective evaluation under diverse environmental conditions has led to the release of varieties with high yield stability. A notable success is the development of Kasetsart 50. However, extreme climate change poses significant challenges, including abiotic and biotic stresses that threaten cassava root yield and starch content, leading to a potential decline in starch-based industries. Future directions for cassava breeding must include hybrid development, marker-assisted recurrent breeding, and gene editing, along with high-throughput phenotyping and flower induction. These strategies are essential to achieve breeding objectives focused on drought tolerance and disease resistance, especially for CMD and CBSD. [ABSTRACT FROM AUTHOR]

Background: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. Results: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). Conclusions: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used. [ABSTRACT FROM AUTHOR]

Background: Integration of high throughput DNA genotyping and RNA-sequencing data enables the discovery of genomic regions that regulate gene expression, known as expression quantitative trait loci (eQTL). In pigs, efforts to date have been mainly focused on purebred lines for traits with commercial relevance as such growth and meat quality. However, little is known on genetic variants and mechanisms associated with the robustness of an animal, thus its overall health status. Here, the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred female finishers were studied, with the aim of identifying novel eQTL regulatory regions and transcription factors (TFs) associated with regulation of porcine metabolism and health-related traits. Results: An expression genome-wide association study with 535,896 genotypes and the expression of 12,680 genes in liver, 13,310 genes in lung, 12,650 genes in spleen, and 12,595 genes in muscle resulted in 4,293, 10,630, 4,533, and 6,871 eQTL regions for each of these tissues, respectively. Although only a small fraction of the eQTLs were annotated as cis-eQTLs, these presented a higher number of polymorphisms per region and significantly stronger associations with their target gene compared to trans-eQTLs. Between 20 and 115 eQTL hotspots were identified across the four tissues. Interestingly, these were all enriched for immune-related biological processes. In spleen, two TFs were identified: ERF and ZNF45, with key roles in regulation of gene expression. Conclusions: This study provides a comprehensive analysis with more than 26,000 eQTL regions identified that are now publicly available. The genomic regions and their variants were mostly associated with tissue-specific regulatory roles. However, some shared regions provide new insights into the complex regulation of genes and their interactions that are involved with important traits related to metabolism and immunity. [ABSTRACT FROM AUTHOR]

Peanut stem rot, also known as white mold, poses a significant threat to peanut production. It is typically managed using fungicides and moderately resistant cultivars. Cultivars with higher resistance can reduce fungicide dependency and increase sustainability. This study explores the potential of wild peanut species in stem rot resistance breeding programs by enhancing genetic diversity in cultivated peanut. Through greenhouse and field evaluations, 13 allotetraploid hybrids with Arachis stenosperma as one of the parents showed superior resistance compared to other wild genotypes. The genomic regions that confer the stem rot resistance were further identified by genotyping and phenotyping an F2 population derived from the allotetraploid ValSten1 (A. valida × A. stenosperma)4× and A. hypogaea cv. TifGP-2. A linkage map was constructed from 1926 SNP markers. QTL analysis revealed both beneficial and deleterious loci, with two resistance-associated QTLs derived from A. stenosperma and four susceptibility loci, two from A. stenosperma and two from A. valida. This is the first study that evaluated peanut-compatible wild-derived allotetraploids for stem rot resistance and that identified wild-derived QTLs for resistance to this pathogen. The allotetraploid hybrid ValSten1, that has A. stenosperma as one of the parents, offers a resource for resistance breeding. Markers associated with resistance QTLs can facilitate introgression from ValSten1 into cultivated peanut varieties in future breeding efforts, potentially reducing reliance on chemical control measures. [ABSTRACT FROM AUTHOR]

The blood clam (Tegillarca granosa) is an economically important marine shellfish in the southeast coastal area of China. In T. granosa breeding practice, growth-related traits are most important phenotypic selection index. In order to increase the breeding efficiency, we used a previously published genetic linkage map of T. granosa to map quantitative trait loci (QTL) for growth-related traits in a single full sibling F1 family. Eight growth-related traits including shell length (SL), shell height (SH), shell width (SW), shell weight (Swi), shell umbo width (SUW), as well as outer ligament length (OLL), meat weight (MW) and total weight (TW) of T. granosa were measured for QTL mapping. The estimated phenotypic correlations between any 2 of the 8 growth-related traits were significant (P < 0.001). Twenty-nine QTL were identified for growth-related traits, including 13 QTL with the phenotypic variance of 4.20-15.63% in the female map and 16 QTL of 3.99%-14.58% in the male map. Thirteen amplification fragment length polymorphism (AFLP) markers were validated to closely link to seven QTL except the shell height. This is the first report of QTL mapping on growth-related traits in T. granosa. These QTL and their tightly linked markers present the initial step towards the goal of marker-assisted selection in T. granosa. [ABSTRACT FROM AUTHOR]

Background: Biologically annotated neural networks (BANNs) are feedforward Bayesian neural network models that utilize partially connected architectures based on SNP-set annotations. As an interpretable neural network, BANNs model SNP and SNP-set effects in their input and hidden layers, respectively. Furthermore, the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales. However, its application in genomic prediction has yet to be explored. Results: This study extended the BANNs framework to the area of genomic selection and explored the optimal SNP-set partitioning strategies by using dairy cattle datasets. The SNP-sets were partitioned based on two strategies–gene annotations and 100 kb windows, denoted as BANN_gene and BANN_100kb, respectively. The BANNs model was compared with GBLUP, random forest (RF), BayesB and BayesCπ through five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits, type traits, and one health trait of 6,558, 6,210 and 5,962 Chinese Holsteins, respectively. Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLUP, RF and Bayesian methods. Specifically, the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP, RF, BayesB and BayesCπ across all traits. The average accuracy improvements of BANN_100kb over GBLUP, RF, BayesB and BayesCπ were 4.86%, 3.95%, 3.84% and 1.92%, and the accuracy of BANN_gene was improved by 3.75%, 2.86%, 2.73% and 0.85% compared to GBLUP, RF, BayesB and BayesCπ, respectively across all seven traits. Meanwhile, both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP, RF and Bayesian methods. Conclusion: Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios, and might serve as a promising alternative approach for genomic prediction in dairy cattle. [ABSTRACT FROM AUTHOR]

Genome-wide association mapping studies (GWAS) based on Big Data are a potential approach to improve marker-assisted selection in plant breeding. The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing. Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects, but is hindered by data heterogeneity and lack of interoperability. In this study, we used genomic and phenotypic data sets, focusing on Central European winter wheat populations evaluated for heading date. We explored strategies for integrating these data and subsequently the resulting potential for GWAS. Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols, resulting in high quality integrated phenotypic data. In this context, genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series, which was low in our case. Contrary to expectations, fewer associations between markers and traits were found in the larger combined data than in the individual experimental series. However, the predictive power based on the marker-trait associations of the integrated data set was higher across data sets. Therefore, the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS. The results encourage further efforts to standardize and share data in the plant breeding community.

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain. Adenosine-to-inosine editing is a form of RNA modification observed in the human brain transcriptome. Here the authors question the accuracy of utilizing postmortem samples to reflect the RNA biology of living brains. This is due to significant differences in adenosine-to-inosine editing between living and postmortem brain tissues, with most sites exhibiting higher editing levels postmortem. [ABSTRACT FROM AUTHOR]

A quarter of humanity is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) with a 5–10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n = 63) or did not progress to TB (controls, n = 63). Transcriptomic profiling of monocyte-derived DCs and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Four genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation. Author summary: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a leading cause of mortality globally. Mtb-exposed individuals have heterogeneous outcomes following infection with Mtb, where some progress to TB, while many remain asymptomatic. We hypothesized that genetic variation could partly explain risk of TB, by regulating the expression of genes involved in the control of Mtb infection. In this study in Peru, we recruited former TB patients and Mtb-exposed household contacts of TB patients who remained asymptomatic to define how genetic variation regulates expression of genes in the innate immune system. We identified 4 genetic variants that have different effects on gene expression based on the TB history of the participant. The lead variant regulated expression of a key enzyme (Fumarylacetoacetate Hydrolase; FAH) in tyrosine catabolism. Knocking out the gene in myeloid cells increased susceptibility to Mtb infection. The results implicate FAH as a candidate host factor involved in TB progression. [ABSTRACT FROM AUTHOR]

Background: Body conformation, including withers height, is a major selection criterion in horse breeding and is associated with other important traits, such as health and performance. However, little is known about the genomic background of equine conformation. Therefore, the aim of this study was to use imputed sequence-level genotypes from up to 4891 German Warmblood horses to identify genomic regions associated with withers height and linear conformation traits. Furthermore, the traits were genetically characterised and putative causal variants for withers height were detected. Results: A genome-wide association study (GWAS) for withers height confirmed the presence of a previously known quantitative trait locus (QTL) on Equus caballus (ECA) chromosome 3 close to the LCORL/NCAPG locus, which explained 16% of the phenotypic variance for withers height. An additional significant association signal was detected on ECA1. Further investigations of the region on ECA3 identified a few promising candidate causal variants for withers height, including a nonsense mutation in the coding sequence of the LCORL gene. The estimated heritability for withers height was 0.53 and ranged from 0 to 0.34 for the conformation traits. GWAS identified significantly associated variants for more than half of the investigated conformation traits, among which 13 showed a peak on ECA3 in the same region as withers height. Genetic parameter estimation revealed high genetic correlations between these traits and withers height for the QTL on ECA3. Conclusions: The use of imputed sequence-level genotypes from a large study cohort led to the discovery of novel QTL associated with conformation traits in German Warmblood horses. The results indicate the high relevance of the QTL on ECA3 for various conformation traits, including withers height, and contribute to deciphering causal mutations for body size in horses. [ABSTRACT FROM AUTHOR]

In bread wheat, a literature search gave 228 QTLs for six traits, including resistance against spot blotch and the following five other related traits: (i) stay green; (ii) flag leaf senescence; (iii) green leaf area duration; (iv) green leaf area of the main stem; and (v) black point resistance. These QTLs were used for metaQTL (MQTL) analysis. For this purpose, a consensus map with 72,788 markers was prepared; 69 of the above 228 QTLs, which were suitable for MQTL analysis, were projected on the consensus map. This exercise resulted in the identification of 16 meta-QTLs (MQTLs) located on 11 chromosomes, with the PVE ranging from 5.4% (MQTL7) to 21.8% (MQTL5), and the confidence intervals ranging from 1.5 to 20.7 cM (except five MQTLs with a range of 36.1–57.8 cM). The number of QTLs associated with individual MQTLs ranged from a maximum of 17 in MQTL3 to 8 each in MQTL5 and MQTL8 and 5 each in MQTL7 and MQTL14. The 16 MQTLs, included 12 multi-trait MQTLs; one of the MQTL also overlapped a genomic region carrying the major spot blotch resistance gene Sb1. Of the total 16 MQTLs, 12 MQTLs were also validated through marker-trait associations that were available from earlier genome-wide association studies. The genomic regions associated with MQTLs were also used for the identification of candidate genes (CGs) and led to the identification of 516 CGs encoding 508 proteins; 411 of these proteins are known to be associated with resistance against several biotic stresses. In silico expression analysis of CGs using transcriptome data allowed the identification of 71 differentially expressed CGs, which were examined for further possible studies. The findings of the present study should facilitate fine-mapping and cloning of genes, enabling Marker Assisted Selection. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) can cause serious yield loss and significant mycotoxin contamination, which seriously threaten global food security and safety. Breeding stable and durable cultivars that are resistant to FHB is one of the most effective approaches to controlling this disease. Fhb1 is a well-known genetic locus for FHB resistance, but its resistance is not always effective across diverse wheat genetic backgrounds. To achieve a high and durable level of resistance, the discovery and use of additional quantitative trait loci (QTL) for FHB resistance are essentially needed in breeding programs. In this study, two independent wheat natural populations of different origins were used for mining resistance QTL with a major and stable effect. Using genome-wide association analysis (GWAS), a total of 58 marker–trait associations (MTAs) on chromosomes 1A, 2B, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A and 7B were found to be significant for type II resistance to FHB. These 58 MTAs represent 24 putative QTL. Among these QTL, Qfhb.yzu.3B.1 and Qfhb.yzu.6B.3 were stably detected in the two natural populations across three consecutive experimental years. The favorable haplotypes at the two QTL could significantly reduce the disease severity, either individually or in combination. These two QTL are also additive to Fhb1 in cultivars with different genetic backgrounds. Breeder-friendly markers were designed to differentiate the contrasting alleles at these two loci, thus proving very useful for improving FHB resistance in wheat by marker-assisted selection. [ABSTRACT FROM AUTHOR]

Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses. Schizophrenia research has traditionally overlooked sex differences. Here, the authors show the importance of sex-based analysis across multi-brain regions by identifying sex-specific genes and genetic interactions in schizophrenia and sex-specific risk. [ABSTRACT FROM AUTHOR]

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma. Type 2 inflammation drives the formation of pathologic mucus in patients with asthma. Here, authors reveal a role for intelectin-1 in IL-13-induced mucus properties, and that an ITLN1 eQTL is associated with protection from the formation of mucus plugs in T2-high asthma. [ABSTRACT FROM AUTHOR]

Expression quantitative trait loci (eQTL) studies typically consider exon expression of genes and discard intronic RNA sequencing reads despite their information on RNA metabolism. Here, we quantify genetic effects on exon and intron levels of genes and their ratio in lymphoblastoid cell lines, revealing thousands of cis-QTLs of each type. While genetic effects are often shared between cis-QTL types, 7814 (47%) are not detected as top cis-QTLs at exon levels. We show that exon levels preferentially capture genetic effects on transcriptional regulation, while exon-intron-ratios better detect those on co- and post-transcriptional processes. Considering all cis-QTL types substantially increases (by 71%) the number of colocalizing variants identified by genome-wide association studies (GWAS). It further allows dissecting the potential gene regulatory processes underlying GWAS associations, suggesting comparable contributions by transcriptional (50%) and co- and post-transcriptional regulation (46%) to complex traits. Overall, integrating intronic RNA sequencing reads in eQTL studies expands our understanding of genetic effects on gene regulatory processes. Expression quantitative trait loci (eQTL) studies typically only consider exon expression levels and discard intronic RNA sequencing reads. Here, the authors show that analyzing eQTLs together with QTLs for intron levels and exon-intron ratios expands the number and functional understanding of genetic effects on gene regulatory processes. [ABSTRACT FROM AUTHOR]

Background: Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22–150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle. Results: We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits. Conclusions: This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance. [ABSTRACT FROM AUTHOR]