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Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone. The placenta has been proposed to be potentially relevant to schizophrenia risk. Here, the authors use the genetically predicted transcriptome to identify genes expressed in the placenta that could be involved in schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches. [ABSTRACT FROM AUTHOR]

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans. Here, the authors performed a large-scale in-depth cis-meQTL mapping study in 961 African Americans from the GENOA study, investigating the co-regulation of methylation and gene expression in African Americans. [ABSTRACT FROM AUTHOR]

Background: A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. Results: We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. Conclusions: The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle. [ABSTRACT FROM AUTHOR]

Genomic selection is expected to improve selection efficiency and genetic gain in breeding programs. The objective of this study was to assess the efficacy of predicting the performance of grain sorghum hybrids using genomic information of parental genotypes. One hundred and two public sorghum inbred parents were genotyped using genotyping-by-sequencing. Ninty-nine of the inbreds were crossed to three tester female parents generating a total of 204 hybrids for evaluation at two environments. The hybrids were sorted in to three sets of 77,59 and 68 and evaluated along with two commercial checks using a randomized complete block design in three replications. The sequence analysis generated 66,265 SNP markers that were used to predict the performance of 204 F1 hybrids resulted from crosses between the parents. Both additive (partial model) and additive and dominance (full model) were constructed and tested using various training population (TP) sizes and cross-validation procedures. Increasing TP size from 41 to 163 increased prediction accuracies for all traits. With the partial model, the five-fold cross validated prediction accuracies ranged from 0.03 for thousand kernel weight (TKW) to 0.58 for grain yield (GY) while it ranged from 0.06 for TKW to 0.67 for GY with the full model. The results suggest that genomic prediction could become an effective tool for predicting the performance of sorghum hybrids based on parental genotypes. [ABSTRACT FROM AUTHOR]

Background: This paper describes genomics from two perspectives that are in use in animal breeding and genetics: a statistical perspective concentrating on models for estimating breeding values, and a sequence perspective concentrating on the function of DNA molecules. Main body: This paper reviews the development of genomics in animal breeding and speculates on its future from these two perspectives. From the statistical perspective, genomic data are large sets of markers of ancestry; animal breeding makes use of them while remaining agnostic about their function. From the sequence perspective, genomic data are a source of causative variants; what animal breeding needs is to identify and make use of them. Conclusion: The statistical perspective, in the form of genomic selection, is the more applicable in contemporary breeding. Animal genomics researchers using from the sequence perspective are still working towards this the isolation of causative variants, equipped with new technologies but continuing a decades-long line of research. [ABSTRACT FROM AUTHOR]

Key message: AhyHOF1, likely encoding a WRI1 transcription factor, plays critical roles in peanut oil synthesis. Although increasing the oil content of peanut to meet growing demand has long been a primary aim of breeding programs worldwide, the mining of genetic resources to achieve this objective has obviously lagged behind that of other oil crops. In the present study, we developed an advanced recombinant inbred line population containing 192 F9:11 families derived from parents JH5 and KX01-6. We then constructed a high-resolution genetic map covering 3,706.382 cM, with an average length of 185.32 cM per linkage group, using 2840 polymorphic SNPs. Two stable QTLs, qCOA08_1 and qCOA08_2 having the highest contributions to genetic variation (16.1% and 20.7%, respectively), were simultaneously detected in multiple environments and closely mapped within physical intervals of approximately 2.9 Mb and 1.7 Mb, respectively, on chromosome A08. In addition, combined analysis of whole-genome and transcriptome resequencing data uncovered a strong candidate gene encoding a WRI1 transcription factor and differentially expressed between the two parents. This gene, designated as High Oil Favorable gene 1 in Arachis hypogaea (AhyHOF1), was hypothesized to play roles in oil accumulation. Examination of near-inbred lines of #AhyHOF1/#Ahyhof1 provided further evidence that AhyHOF1 increases oil content, mainly by affecting the contents of several fatty acids. Taken together, our results provide valuable information for cloning the favorable allele for oil content in peanut. In addition, the closely linked polymorphic SNP markers within qCOA08_1 and qCOA08_2 loci may be useful for accelerating marker-assisted selection breeding of peanut. [ABSTRACT FROM AUTHOR]

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses. Here, the authors discover over 15,000 eQTLs in human kidney samples by integrating single-nucleus open chromatin data, resulting in high resolution eQTLs, increased enrichment of GWAS heritability and colocalization, followed by downstream validation. [ABSTRACT FROM AUTHOR]

The Thinopyrum elongatum Fhb7E locus has been proven to confer outstanding resistance to Fusarium Head Blight (FHB) when transferred into wheat, minimizing yield loss and mycotoxin accumulation in grains. Despite their biological relevance and breeding implications, the molecular mechanisms underlying the resistant phenotype associated with Fhb7E have not been fully uncovered. To gain a broader understanding of processes involved in this complex plant–pathogen interaction, we analysed via untargeted metabolomics durum wheat (DW) rachises and grains upon spike inoculation with Fusarium graminearum (Fg) and water. The employment of DW near-isogenic recombinant lines carrying or lacking the Th. elongatum chromosome 7E region including Fhb7E on their 7AL arm, allowed clear-cut distinction between differentially accumulated disease-related metabolites. Besides confirming the rachis as key site of the main metabolic shift in plant response to FHB, and the upregulation of defence pathways (aromatic amino acid, phenylpropanoid, terpenoid) leading to antioxidants and lignin accumulation, novel insights were revealed. Fhb7E conferred constitutive and early-induced defence response, in which specific importance of polyamine biosynthesis, glutathione and vitamin B6 metabolisms, along with presence of multiple routes for deoxynivalenol detoxification, was highlighted. The results suggested Fhb7E to correspond to a compound locus, triggering a multi-faceted plant response to Fg, effectively limiting Fg growth and mycotoxin production. [ABSTRACT FROM AUTHOR]

Summary RNA sequencing (RNA-seq) can be applied to diverse tasks including quantifying gene expression, discovering quantitative trait loci and identifying gene fusion events. Although RNA-seq can detect germline variants, the complexities of variable transcript abundance, target capture and amplification introduce challenging sources of error. Here, we extend DeepVariant, a deep-learning-based variant caller, to learn and account for the unique challenges presented by RNA-seq data. Our DeepVariant RNA-seq model produces highly accurate variant calls from RNA-sequencing data, and outperforms existing approaches such as Platypus and GATK. We examine factors that influence accuracy, how our model addresses RNA editing events and how additional thresholding can be used to facilitate our models' use in a production pipeline. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

Rice is a major cereal that nourishes more than half of the global population. But rice lands are under threat from soil salinity leading to reduced productivity. Salinity stress tolerance is challenging due to the multigenic trait and complex tolerance mechanisms. QTL pyramiding is one of the efficient approaches to stack desired genes and QTLs from multiple donors. This study developed three populations using multiple salt-tolerant introgression lines (ILs) to accumulate beneficial alleles/QTLs and identify improved salt-tolerant breeding lines. To accumulate the salt-tolerant alleles from salt-tolerant donors, we used salt-tolerant ILs of Pokkali and Nona Bokra in the CL151 background (CLPK683 and CLPK757) and Jupiter background (JN100 and JN520), respectively. The QTLs for shoot Na+ and K+ content, qNa2.2, qK2.1, and qK2.2, from JN100 and qK4.1 and qK8.1 from JN520 were targeted using the molecular markers linked to these QTLs. Both phenotypic and genotypic selection methods were employed to advance the populations. The F5 populations of each cross were finally evaluated for seedling-stage salt tolerance. The visual salt injury score (SIS) and shoot Na+ and K+ concentration in selected lines indicated salt tolerance higher than the IL parents. The targeted QTLs were detected in most salt-tolerant lines. The salt-tolerant breeding lines identified in this study constitute a valuable resource for investigating the physiological and molecular basis of salt tolerance and identifying superior alleles of salt-tolerance genes for use in breeding programs. [ABSTRACT FROM AUTHOR]

Resistance to European canker (Neonectria ditissima) in apple is currently one of the most important breeding targets for commercial production in Sweden. Previous research has identified significant genetic variation in susceptibility to the disease, with the local Swedish cultivar 'Aroma' considered as one of the most resistant cultivars. Identification of genetic regions underlying the resistance of this cultivar would be a valuable tool for future breeding. Thus, we performed Bayesian quantitative trait loci (QTL) mapping for resistance to European canker in a full-sib family of 'Aroma' × 'Discovery'. Mapping was performed with the area under the disease progression curves (AUDPCs) from all seven (AUDPC_All7) and the first four assessments (AUDPC_First4), and three parameters of a sigmoid growth model for lesion length. As a scale for the effect of the different parameters, historic phenotypic data from screenings of a genetically diverse germplasm was compiled and re-analyzed. The parametrization of the data on lesion growth increased the number of QTL that could be identified with high statistical power, and provided some insight into their roles during different stages of disease development in the current experimental setup. Five QTL regions with strong or decisive evidence were identified on linkage groups 1, 8, 15, and 16. The QTL regions could be assigned to either of the parameters lesion length at the first assessment ('LL_A1'), the maximal lesion growth rate (lesion length doubling time, 't_gen'), and the lesion length at girdling ('LL_G'). Three of these QTL were traced along the pedigrees of some known relatives of the FS family, and discussed in relation to future crosses for breeding and genetic research. [ABSTRACT FROM AUTHOR]

Late blight, caused by oomycetes Phytophthora infestans is one of the most challenging fungal diseases to manage in tomato plants (Solanum lycopersicum L.). Toward managing the disease, conventional breeding has successfully introgressed genetic loci conferring disease resistance from various wild relatives of tomato into commercial varieties. The cataloging of disease-associated SNP markers and a deeper understanding of disease-resistance mechanisms are needed to keep up with the demand for commercial varieties resistant against emerging pathogen strains. To this end, we performed transcriptome sequencing to evaluate the gene expression dynamics of tomato varieties, resistant and susceptible to Phytophthora infection. Further integrating the transcriptome dataset with large-scale public genomic data of varieties with known disease phenotypes, a panel of single nucleotide polymorphism (SNP) markers correlated with disease resistance was identified. These SNPs were then validated on 31 lines with contrasting phenotypes for late blight. The identified SNPs are located on genes coding for a putative cysteine-rich transmembrane module (CYSTM), Solyc09g098310, and a nucleotide-binding site–leucine-rich repeat protein, Solyc09g098100, close to the well-studied Ph-3 resistance locus known to have a role in plant immunity against fungal infections. The panel of SNPs generated by this study using transcriptome sequencing showing correlation with disease resistance across a broad set of plant material can be used as markers for molecular screening in tomato breeding. [ABSTRACT FROM AUTHOR]

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs., (© 2024. The Author(s).)

Realizing the full potential of plant synthetic biology both to elucidate the relationship between genotype and phenotype and to apply these insights to engineer traits requires rapidly iterating through design-build-test cycles. However, the months-long process of transgenesis, the long generation times, and the size-based limitations on experimentation have stymied progress by limiting the speed and scale of these cycles. Herein, we review a representative sample of recent studies that demonstrate a variety of rapid prototyping technologies that overcome some of these bottlenecks and accelerate progress. However, each of them has caveats that limit their broad utility. Their complementary strengths and weaknesses point to the intriguing possibility that these strategies could be combined in the future to enable rapid and scalable deployment of synthetic biology in plants., (© 2024 The Authors. New Phytologist © 2024 New Phytologist Foundation.)

Studying the interplay between genetic variation, epigenetic changes, and regulation of gene expression is crucial to understand the modification of cellular states in various conditions, including immune diseases. In this study, we characterize the cell-specificity in three key cells of the human immune system by building cis maps of regulatory regions with coordinated activity (CRDs) from ChIP-seq peaks and methylation data. We find that only 33% of CRD-gene associations are shared between cell types, revealing how similarly located regulatory regions provide cell-specific modulation of gene activity. We emphasize important biological mechanisms, as most of our associations are enriched in cell-specific transcription factor binding sites, blood-traits, and immune disease-associated loci. Notably, we show that CRD-QTLs aid in interpreting GWAS findings and help prioritize variants for testing functional hypotheses within human complex diseases. Additionally, we map trans CRD regulatory associations, and among 207 trans-eQTLs discovered, 46 overlap with the QTLGen Consortium meta-analysis in whole blood, showing that mapping functional regulatory units using population genomics allows discovering important mechanisms in the regulation of gene expression in immune cells. Finally, we constitute a comprehensive resource describing multi-omics changes to gain a greater understanding of cell-type specific regulatory mechanisms of immunity. An analysis of cis-regulatory domains (CRDs) among monocytes, neutrophils, and T cells derived from ChIP-seq peaks and methylation data suggests cell-type specific regulatory mechanisms of immunity. [ABSTRACT FROM AUTHOR]

Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity. Here the authors used an evidence-based strategy to prioritize causal pleiotropic variants of autoimmune diseases, and revealed that rs4728142 modulates aberrant IRF5 alternative promoter usage by ZBTB3-mediated chromatin looping. [ABSTRACT FROM AUTHOR]

Sunflower (Helianthus annuus L.) seed dormancy at harvest is an undesirable characteristic regarding productive systems. From a commercial point of view, the dormancy generates production and marketing problem with a negative impact on crop yields. To overcome this problem, we studied different treatments, including the application of growth regulators. In addition, as the interaction of various hormones in different sunflower fruit parts is not fully understood regarding regulation of dormancy and germination processes, we also (1) characterized the dormancy level of sunflower seeds of Xi3 and X274 inbred lines; (2) assessed the effect of exogenous growth regulators on dormancy release; (3) evaluated the endogenous content of abscisic acid (ABA), gibberellins (GAs), 12‐oxo‐phytodienoic acid (OPDA), jasmonic acid (JA), salicylic acid (SA), auxins, and cytokinins (CKs) in embryonic axis, cotyledons, pericarp, and seed coat‐endosperm; and (4) analyzed the kinetics of the accumulation of these phytohormones in Xi3 embryonic axis during early imbibition (3 to 12 h) of cypselas treated, or not, with growth regulators effective in the dormancy release. We were able to classify the two sunflower lines studied according to dormancy level: The Xi3 line was dormant and X274 line was non‐dormant at harvest. The results showed that the dormancy of Xi3 seeds is associated with a high level of ABA, OPDA, and JA, while germination of X274 dry seeds at harvest time is related to high levels of GA1. Exogenous treatment with GA3 and Ethephon overcame Xi3 seed dormancy through the changes in endogenous hormonal profiles of the embryonic axis. The exogenous application of GA3 induced JA, SA, and CKs accumulation and to modify the ABA/GA1 ratio, whereas Ethephon treatments mediated the ABA/GA1 ratio with the consequent Xi3 seed germination. Thus, the interplay of different phytohormones during early imbibition time seems to be the responsible for breaking sunflower seed dormancy. Core Ideas: Sunflower Xi3 seed dormancy imposition is associated with a high ABA, JA, and OPDA levels in embryo.X274 seed germination may be related to high levels of GA1 in embryo.GA3 (10,000 ppm) and Ethephon (10,000 ppm) are effective to break sunflower seed dormancy.GA3 exogenous treatment modified JA, SA, CKs content and ABA/GA1 ratio in Xi3 embryonic axis during early imbibition.Ethephon modified the ABA/GA1 ratio in embryonic axis with the consequent breaking of dormancy in Xi3 seeds. [ABSTRACT FROM AUTHOR]

Threshability, defined here as the propensity of grains to lose their hull after harvest, is a key trait in naked barley (Hordeum vulgare L.). While threshability is a defining characteristic of naked grains and has been found to be associated with grain size and shape, its genetic architecture is poorly described. The goals of this study were to identify quantitative trait loci associated with threshability and evaluate their utility as covariates in genomic prediction models. A genome‐wide association study identified two loci on chromosomes 2H and 3H associated with threshability. The locus on chromosome 2H accounted for 9.9% of the phenotypic variance explained (PVE). The locus on chromosomes 3H accounted for 7.8% of the PVE. With effects on threshability of 0.18 and 0.29 for each marker, respectively, these markers could have a limited impact when implemented in marker‐assisted selection. Predictive ability for threshability was 0.842 using a structured genomic best linear unbiased prediction model. Incorporation of the markers with significant associations as covariates in the model did not improve predictive ability. Predictive ability was improved by the use of a multi‐trait model including grain test weight. The high predictive ability for threshability overall indicate that genomic selection would be useful in selection for threshability in naked barley. Core Ideas: Two markers were identified with significant associations to threshability.Predictive ability for threshability was 0.842 using a structured gBLUP model.Predictive ability was not increased by inclusion of identified QTL as covariates.Predictive ability was increased using a multi‐trait prediction model. [ABSTRACT FROM AUTHOR]

Simple Summary: Milk and other dairy products are commonly consumed in many parts of the world. Dairy cattle, having millions of milk trait records, make excellent model species for understanding the genetics controlling the production of milk. This manuscript gives a summary of the current understanding of the genetic signals for milk production, in terms of the biological pathways they are involved with, and highlights a number of methods that can be used to identify the genes and variants underlying these signals. Knowledge of these variants will improve the ability of farmers and animal breeding companies to increase the rate of genetic gain for milk traits and enable the use of technologies such as gene editing. Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation. [ABSTRACT FROM AUTHOR]

Simple Summary: The accurate prediction of growth traits in genomic selection (GS) is essential for pig breeding. Here, we performed GS using variants identified with three genome-wide association study methods on four growth-related traits in Yorkshire and Landrace pigs. A total of 1485 loci related to these traits and 24 candidate genes were mapped. Compared with using 60K SNP-chip data, GS with the pre-selected variants significantly improved prediction accuracies by 4 to 46% in genomic best linear unbiased prediction (GBLUP) models, and 5 to 27% in a two-kernel based GBLUP model for the four traits. Improving the prediction accuracies of economically important traits in genomic selection (GS) is a main objective for researchers and breeders in the livestock industry. This study aims at utilizing potentially functional SNPs and QTLs identified with various genome-wide association study (GWAS) models in GS of pig growth traits. We used three well-established GWAS methods, including the mixed linear model, Bayesian model and meta-analysis, as well as 60K SNP-chip and whole genome sequence (WGS) data from 1734 Yorkshire and 1123 Landrace pigs to detect SNPs related to four growth traits: average daily gain, backfat thickness, body weight and birth weight. A total of 1485 significant loci and 24 candidate genes which are involved in skeletal muscle development, fatty deposition, lipid metabolism and insulin resistance were identified. Compared with using all SNP-chip data, GS with the pre-selected functional SNPs in the standard genomic best linear unbiased prediction (GBLUP), and a two-kernel based GBLUP model yielded average gains in accuracy by 4 to 46% (from 0.19 ± 0.07 to 0.56 ± 0.07) and 5 to 27% (from 0.16 ± 0.06 to 0.57 ± 0.05) for the four traits, respectively, suggesting that the prioritization of preselected functional markers in GS models had the potential to improve prediction accuracies for certain traits in livestock breeding. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) is primarily caused by Fusarium graminearum and severely reduces wheat yield, causing mycotoxin contamination in grains and derived products. F. graminearum-secreted chemical toxins stably accumulate in plant cells, disturbing host metabolic homeostasis. We determined the potential mechanisms underlying FHB resistance and susceptibility in wheat. Three representative wheat varieties (Sumai 3, Yangmai 158, and Annong 8455) were inoculated with F. graminearum and their metabolite changes were assessed and compared. In total, 365 differentiated metabolites were successfully identified. Amino acids and derivatives, carbohydrates, flavonoids, hydroxycinnamate derivatives, lipids, and nucleotides constituted the major changes in response to fungal infection. Changes in defense-associated metabolites, such as flavonoids and hydroxycinnamate derivatives, were dynamic and differed among the varieties. Nucleotide and amino acid metabolism and the tricarboxylic acid cycle were more active in the highly and moderately resistant varieties than in the highly susceptible variety. We demonstrated that two plant-derived metabolites, phenylalanine and malate, significantly suppressed F. graminearum growth. The genes encoding the biosynthetic enzymes for these two metabolites were upregulated in wheat spike during F. graminearum infection. Thus, our findings uncovered the metabolic basis of resistance and susceptibility of wheat to F. graminearum and provided insights into engineering metabolic pathways to enhance FHB resistance in wheat. [ABSTRACT FROM AUTHOR]

Genetic analyses of mammalian gametogenesis and fertility have the potential to inform about two important and interrelated clinical areas: infertility and contraception. Here, we address the genetics and genomics underlying gamete formation, productivity and function in the context of reproductive success in mammalian systems, primarily mouse and human. Although much is known about the specific genes and proteins required for meiotic processes and sperm function, we know relatively little about other gametic determinants of overall fertility, such as regulation of gamete numbers, duration of gamete production, and gamete selection and function in fertilization. As fertility is not a binary trait, attention is now appropriately focused on the oligogenic, quantitative aspects of reproduction. Multiparent mouse populations, created by complex crossing strategies, exhibit genetic diversity similar to human populations and will be valuable resources for genetic discovery, helping to overcome current limitations to our knowledge of mammalian reproductive genetics. Finally, we discuss how what we know about the genomics of reproduction can ultimately be brought to the clinic, informing our concepts of human fertility and infertility, and improving assisted reproductive technologies. [ABSTRACT FROM AUTHOR]

Soybean is an excellent source of plant protein. To provide a higher quality meal product, soybean producers desire to improve soybean nutritional profiles. Quantitative trait loci (QTL) mapping can identify markers associated with variation in seed protein and seed oil concentration, and confirmation of QTL is crucial to improving the efficacy of marker-assisted selection (MAS). The objectives of this study were to identify QTL for seed protein and seed oil concentration in a relevant genetic pedigree of the cross 'Essex × Williams 82' recombinant inbred line (RIL) population. A total of 302 RIL and 12,730 SNP markers were used to identify QTL-controlling seed quality traits. Novel QTL were identified, and validation tests for loci detected in the earlier generation RIL were performed. Seed protein and seed oil concentration had high heritability across multiple environments but were negatively correlated (r = −0.69, p < 0.05). Genotype and genotype × environment interaction was significant (p < 0.05) for seed protein and seed oil concentration. The study references data from a previous year in one location and focuses on a one-year study of the population in three locations. A total of 27 QTL for protein and oil were detected. The QTL explained 3.1–9.8% of the variation in seed protein concentration and 3.2–14.1% of the variation in seed oil concentration. Several QTL were confirmed, and a protein QTL for consideration as a technically confirmed QTL was located on Gm 7 in the genome. [ABSTRACT FROM AUTHOR]

Although nitrogen (N) is the most limiting nutrient for agricultural production, its overuse is associated with environmental pollution, increased concentration of greenhouse gases, and several human and animal health implications. These implications are greatly affected by biochemical transformations and losses of N such as volatilization, leaching, runoff, and denitrification. Half of the globally produced N fertilizers are used to grow three major cereals—rice, wheat, and maize—and their current level of N recovery is approximately 30–50%. The continuously increasing application of N fertilizers, despite lower recovery of cereals, can further intensify the environmental and health implications of leftover N. To address these implications, the improvement in N use efficiency (NUE) by adopting efficient agronomic practices and modern breeding and biotechnological tools for developing N efficient cultivars requires immediate attention. Conventional and marker-assisted selection methods can be used to map quantitative trait loci, and their introgression in elite germplasm leads to the creation of cultivars with better NUE. Moreover, gene-editing technology gives the opportunity to develop high-yielding cultivars with improved N utilization capacity. The most reliable and cheap methods include agronomic practices such as site-specific N management, enhanced use efficiency fertilizers, resource conservation practices, precision farming, and nano-fertilizers that can help farmers to reduce the environmental losses of N from the soil–plant system, thus improving NUE. Our review illuminates insights into recent advances in local and scientific soil and crop management technologies, along with conventional and modern breeding technologies on how to increase NUE that can help reduce linked N pollution and health implications. [ABSTRACT FROM AUTHOR]

The post-reproductive phase or menopause in females is triggered by a physiological timer that depends on a threshold of follicle number in the ovary. Curiously, reproductive senescence appears to be decoupled from chronological age and is instead thought to be a function of physiological ageing. Ovarian ageing is associated with a decrease in oocyte developmental competence, attributed to a concomitant increase in meiotic errors. Although many biological hallmarks of general ageing are well characterized, the precise mechanisms underlying the programmed ageing of the female reproductive system remain elusive. In particular, the molecular pathways linking the external menopause trigger to the internal oocyte chromosome segregation machinery that controls fertility outcomes is unclear. However, recent large scale genomics studies have begun to provide insights into this process. Next-generation sequencing integrated with systems biology offers the advantage of sampling large datasets to uncover molecular pathways associated with a phenotype such as ageing. In this mini-review, we discuss findings from these studies that are crucial for advancing female reproductive senescence research. Targets identified in these studies can inform future animal models for menopause. We present three potential hypotheses for how external pathways governing ovarian ageing can influence meiotic chromosome segregation, with evidence from both animal models and molecular targets revealed from genomics studies. Although still in incipient stages, we discuss the potential of genomics studies combined with epigenetic age acceleration models for providing a predictive toolkit of biomarkers controlling menopause onset in women. We also speculate on future research directions to investigate extending female reproductive lifespan, such as comparative genomics in model systems that lack menopause. Novel genomics insights from such organisms are predicted to provide clues to preserving female fertility. [ABSTRACT FROM AUTHOR]

Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high‐density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single‐step GBLUP (ssGBLUP), multi‐trait GBLUP (MT‐GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single‐population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium‐density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT‐GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high‐density genotype data, and the wGBLUP and MT‐GBLUP methods are promising for joint GP in pig breeding. [ABSTRACT FROM AUTHOR]

Genetics for blood pressure (BP) in human and animals has been partitioned into two separate specialties. However, this divide is mechanistically-misleading. BP physiology is mechanistically participated by products of quantitative trait loci (QTLs). The key to unlocking its mechanistic mystery lies in the past with mammalian ancestors before humans existed. By pivoting from effects to causes, physiological mechanisms determining BP by six QTLs have been implicated. Our work relies on congenic knock-in genetics in vivo using rat models, and has reproduced the physiological outcome based on a QTL being molecularly equal to one gene. A gene dose for a QTL is irrelevant to physiological BP controls in causation. Together, QTLs join one another as a group in modularized Mendelian fashion to achieve polygenicity. Mechanistically, QTLs in the same module appear to function in a common pathway. Each is involved in a different step in the pathway toward polygenic hypertension. This work has implicated previously-concealed components of these pathways. This emerging concept is a departure from the human-centric precept that the level of QTL expressions, not physiology, would ultimately determine BP. The modularity/pathway paradigm breaks a unique conceptual ground for unravelling the physiological mechanisms of polygenic and quantitative traits like BP. [ABSTRACT FROM AUTHOR]

The parasitic weed, Striga is amajor biological constraint to cereal production in sub-Saharan Africa (SSA) and threatens food and nutrition security. Two hundred and twenty-three (223) F2:3 mapping population involving individuals derived from TZdEI 352 x TZEI 916 were phenotyped for four Striga-adaptive traits and genotyped using the Diversity Arrays Technology (DArT) to determine the genomic regions responsible for Striga resistance in maize. After removing distorted SNP markers, a genetic linkage map was constructed using 1,918 DArTseq markers which covered 2092.1 cM. Using the inclusive composite interval mapping method in IciMapping, twenty-three QTLs influencing Striga resistance traits were identified across four Striga-infested environments with five stable QTLs (qGY4, qSC2.1, qSC2.2, qSC5, and qSC6) detected inmore than one environment. The variations explained by the QTLs ranged from4.1% (qSD2.3) to 14.4% (qSC7.1). SixQTLs each with significant additive x environment interactionswere also identified for grain yield and Striga damage. Gene annotation revealed candidate genes underlying the QTLs, including the gene models GRMZM2G077002 and GRMZM2G404973 which encode the GATA transcription factors, GRMZM2G178998 and GRMZM2G134073 encoding the NAC transcription factors, GRMZM2G053868 and GRMZM2G157068 which encode the nitrate transporter protein and GRMZM2G371033 encoding the SBP-transcription factor. These candidate genes play crucial roles in plant growth and developmental processes and defense functions. This study provides further insights into the genetic mechanisms of resistance to Striga parasitism in maize. The QTL detected in more than one environment would be useful for further fine-mapping andmarker-assisted selection for the development of Striga resistant and high-yielding maize cultivars. [ABSTRACT FROM AUTHOR]

Cercospora leaf spot (CLS) (Cercospora rosicola) is a major fungal disease of roses (Rosa sp.) in the southeastern U.S. Developing CLS-resistant cultivars offers a potential solution to reduce pesticide use. Yet, no work has been performed on CLS resistance. This study aimed to identify QTLs and to characterize alleles for resistance to CLS. The study used pedigree-based QTL analysis to dissect the genetic basis of CLS resistance using two multiparental diploid rose populations (TX2WOB and TX2WSE) evaluated across five years in two Texas locations. A total 38 QTLs were identified across both populations and distributed over all linkage groups. Three QTLs on LG3, LG4, and LG6 were consistently mapped over multiple environments. The LG3 QTL was mapped in a region between 18.9 and 27.8 Mbp on the Rosa chinensis genome assembly. This QTL explained 13 to 25% of phenotypic variance. The LG4 QTL detected in the TX2WOB population spanned a 35.2 to 39.7 Mbp region with phenotypic variance explained (PVE) up to 48%. The LG6 QTL detected in the TX2WSE population was localized to 17.9 to 33.6 Mbp interval with PVE up to 36%. Also, this study found multiple degrees of favorable allele effects (q-allele) associated with decreasing CLS at major loci. Ancestors 'OB', 'Violette', and PP-M4-4 were sources of resistance q-alleles. These results will aid breeders in parental selection to develop CLS-resistant rose cultivars. Ultimately, high throughput DNA tests that target major loci for CLS could be developed for routine use in a DNA-informed breeding program. [ABSTRACT FROM AUTHOR]

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Genome-wide association studies (GWAS) allow the identification of associations between genetic variants and important phenotypes in domestic animals, including disease-resistance traits. Whole Genome Sequencing (WGS) data can help increase the resolution and statistical power of association mapping. Here, we conduced GWAS to asses he facultative intracellular bacterium Piscirickettsia salmonis, which affects farmed rainbow trout, Oncorhynchus mykiss, in Chile using imputed genotypes at the sequence level and searched for candidate genes located in genomic regions associated with the trait. A total of 2130 rainbow trout were intraperitoneally challenged with P. salmonis under controlled conditions and genotyped using a 57K single nucleotide polymorphism (SNP) panel. Genotype imputation was performed in all the genotyped animals using WGS data from 102 individuals. A total of 488,979 imputed WGS variants were available in the 2130 individuals after quality control. GWAS revealed genome-wide significant quantitative trait loci (QTL) in Omy02, Omy03, Omy25, Omy26 and Omy27 for time to death and in Omy26 for binary survival. Twenty-four (24) candidate genes associated with P. salmonis resistance were identified, which were mainly related to phagocytosis, innate immune response, inflammation, oxidative response, lipid metabolism and apoptotic process. Our results provide further knowledge on the genetic variants and genes associated with resistance to intracellular bacterial infection in rainbow trout. [ABSTRACT FROM AUTHOR]

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results. [ABSTRACT FROM AUTHOR]

TropCRD (www.tropical-resources.org.cn) is organized on a crop basis with current Manihot esculenta Crantz, Hevea brasiliensis, Saccharum officinarum L., Ananas comosus, Mangifera indica L. and other tropical crop species. TropCRD is currently divided into four modules: genome module, variation module, molecular marker module and tools module. These data and tools are available for further research by geneticists and tropical crop breeders, including the application of marker-assisted selection techniques and genomic selective breeding to tropical crop breeding. It provides important support for tropical crop breeding research, and also provides valuable resources for obtaining and analyzing the genome sequence data, genetic variation and functional annotation of tropical crops. [ABSTRACT FROM AUTHOR]

Globally, abiotic stresses, such as temperature (heat or cold), water (drought and flooding), and salinity, cause significant losses in crop production and have adverse effects on plant growth and development. A variety of DNA-based molecular markers, such as SSRs, RFLPs, AFLPs, SNPs, etc., have been used to screen germplasms for stress tolerance and the QTL mapping of stress-related genes. Such molecular-marker-assisted selection strategies can quicken the development of tolerant/resistant cultivars to withstand abiotic stresses. Oilseeds such as rapeseed, mustard, peanuts, soybeans, sunflower, safflower, sesame, flaxseed, and castor are the most important source of edible oil worldwide. Although oilseed crops are known for their capacity to withstand abiotic challenges, there is a significant difference between actual and potential yields due to the adaptation and tolerance to severe abiotic pressures. This review summarizes the applications of molecular markers to date to achieve abiotic stress tolerance in major oilseed crops. The molecular markers that have been reported for genetic diversity studies and the mapping and tagging of genes/QTLs for drought, heavy metal stress, salinity, flooding, cold and heat stress, and their application in the MAS are presented. [ABSTRACT FROM AUTHOR]

The recent advancement of bioinformatics tools and next-generation sequencing (NGS) has created enormous opportunities for a thorough understanding of quantitative (complex) traits in plants. One of the key drivers facilitating the mining of beneficial alleles for climate adaptation and increased crop productivity is multi-parental mapping populations. In this article, the current status and opportunities of multi-parental mapping populations for breeding and genetics are discussed. The paper particularly focuses on nested association mapping (NAM) population. NAM is primarily made up of inbred lines derived from crossing/backcrossing a common parent(s) to multiple diversity donor lines. NAM has high genetic diversity and low population structure, making it ideal for discovering genes and elucidating the genetic architecture of quantitative traits in plants. Because NAM lines are immortal, they allow for repeated measurements and multi-environment testing, which can be used in traditional functional genomics. Overall, the NAM population is a simple but powerful genetic resource that can be used as a gene mapping tool as well as a platform for evaluating models used in plant breeding. [ABSTRACT FROM AUTHOR]

Since its introduction in 2001, genomic selection (GS) has progressed rapidly. As a research and application hot topic, GS has led to a revolution in the field of animal and plant breeding. Thanks to its ability to overcome the shortcomings of traditional breeding methods, GS has garnered increasing attention. Both theoretical and practical breeding studies have revealed the higher accuracy of GS than that of traditional breeding, which can accelerate genetic gain. In recent years, many GS studies have been conducted on aquaculture species, which have shown that GS produces higher prediction accuracy than traditional pedigree‐based method. The present study reviews the principles and processes, preconditions, advantages, analytical methods and factors influencing GS as well as the progress of research in aquaculture into these aspects. Furthermore, future directions of GS in aquaculture are also discussed, which should expand its application to more aquaculture species. [ABSTRACT FROM AUTHOR]

Common bean is one of the most important legumes produced and consumed worldwide because it is a highly valuable food for the human diet. However, its production is mainly carried out by small farmers, who obtain average grain yields below the potential yield of the species. In this sense, numerous mapping studies have been conducted to identify quantitative trait loci (QTL) associated with yield components in common bean. Meta-QTL (MQTL) analysis is a useful approach to combine data sets and for creating consensus positions for the QTL detected in independent studies. Consequently, the objective of this study was to perform a MQTL analysis to identify the most reliable and stable genomic regions associated with yield-related traits of common bean. A total of 667 QTL associated with yield-related traits reported in 21 different studies were collected. A total of 42 MQTL associated with yield-related traits were identified, in which the average confidence interval (CI) of the MQTL was 3.41 times lower than the CIs of the original QTL. Most of the MQTL (28) identified in this study contain QTL associated with yield and phenological traits; therefore, these MQTL can be useful in common bean breeding programs. Finally, a total of 18 candidate genes were identified and associated with grain yield within these MQTL, with functions related to ubiquitin ligase complex, response to auxin, and translation elongation factor activity. [ABSTRACT FROM AUTHOR]

The complicated interactions between genetic background, environment and lifestyle factors make it difficult to study the genetic basis of complex phenotypes, such as cognition and anxiety levels, in humans. However, environmental and other factors can be tightly controlled in mouse studies. The Collaborative Cross (CC) is a mouse genetic reference population whose common genetic and phenotypic diversity is on par with that of humans. Therefore, we leveraged the power of the CC to assess 52 behavioral measures associated with locomotor activity, anxiety level, learning and memory. This is the first application of the CC in novel object recognition tests, Morris water maze tasks, and fear conditioning tests. We found substantial continuous behavioral variations across the CC strains tested, and mapped six quantitative trait loci (QTLs) which influenced these traits, defining candidate genetic variants underlying these QTLs. Overall, our findings highlight the potential of the CC population in behavioral genetic research, while the identified genomic loci and genes driving the variation of relevant behavioral traits provide a foundation for further studies. [ABSTRACT FROM AUTHOR]

• The review covers livestock genomics of South Asian cattle, buffalo, sheep, goat, pig, camel, horse, yak, mithun, and poultry. • Advancements in next-generation sequencing (NGS) technologies led to the discovery of numerous SNP arrays for different livestock species. • The application of genomics in animal breeding has had the biggest economic impact on livestock production. Livestock plays a central role in sustaining human livelihood in South Asia. There are numerous and distinct livestock species in South Asian countries. Several of them have experienced genetic development in recent years due to the application of genomic technologies and effective breeding programs. This review discusses genomic studies on cattle, buffalo, sheep, goat, pig, horse, camel, yak, mithun, and poultry. The frontiers covered in this review are genetic diversity, admixture studies, selection signature research, QTL discovery, genome-wide association studies (GWAS), and genomic selection. The review concludes with recommendations for South Asian livestock systems to increasingly leverage genomic technologies, based on the lessons learned from the numerous case studies. This paper aims to present a comprehensive analysis of the dichotomy in the South Asian livestock sector and argues that a realistic approach to genomics in livestock can ensure long-term genetic advancements. [ABSTRACT FROM AUTHOR]

Variation in immune homeostasis, the state in which the immune system is maintained in the absence of stimulation, is highly variable across populations. This variation is attributed to both genetic and environmental factors. However, the identity and function of specific regulators have been difficult to identify in humans. We evaluated homeostatic antibody levels in the serum of the Collaborative Cross (CC) mouse genetic reference population. We found heritable variation in all antibody isotypes and subtypes measured. We identified 4 quantitative trait loci (QTL) associated with 3 IgG subtypes: IgG1, IgG2b, and IgG2c. While 3 of these QTL map to genome regions of known immunological significance (major histocompatibility and immunoglobulin heavy chain locus), Qih1 (associated with variation in IgG1) mapped to a novel locus on Chromosome 18. We further associated this locus with B cell proportions in the spleen and identify Methyl-CpG binding domain protein 1 under this locus as a novel regulator of homeostatic IgG1 levels in the serum and marginal zone B cells (MZB) in the spleen, consistent with a role in MZB differentiation to antibody secreting cells. Author summary: The baseline immune state is highly variable across populations, which is a function of both environmental and genetics factors. Here, we have used the Collaborative Cross, a genetically diverse mouse population, to study the role of genetic variation on baseline serum antibody concentrations. We identified several regions of the genome that were associated with variation in serum concentrations of multiple antibody subtypes. Most of which are in regions of the genome that have been shown previously to impact immune responses. We also identified a novel region of the genome associated with variation in IgG1 and further identify Methyl-CpG binding domain protein 1 (MBD1) as a regulator of both baseline IgG1 and B cell homeostasis. Given that MBD1 is an epigenetic regulator of differentiation in other contexts, further studies of MBD1 in B cell homeostasis may define previously unknown pathways involved in marginal zone B cell differentiation. [ABSTRACT FROM AUTHOR]

Although high pollinator visitation is crucial to ensure the yields of pollinatordependent crops, the quantitative trait loci (QTL) controlling nectar volume in sunflower (Helianthus annuus L.), a pollinator preference trait, have yet to be identified. To address this, a recombinant inbred line mapping population, derived from lines with contrasting nectar volume, was used to identify loci responsible for the phenotype. As a result, linkage mapping and QTL analysis discovered major loci on chromosomes 2 and 16 that are associated with variation in nectar volume in sunflower. Increased nectar volume is also associated with increased sugars and total energy available per floret. The regions on chromosomes 2 and 16 associated with the nectar phenotype exhibit indications of chromosome structural variation, such that the phenotype is associated with rearrangements affecting regions containing hundreds of genes. Candidate genes underlying QTL on chromosomes 9 and 16 are homologous to genes with nectary function in Arabidopsis. These results have implications for sunflower breeding, to enhance pollination efficiency in sunflower, as well as current and future studies on sunflower evolution. [ABSTRACT FROM AUTHOR]

Background: Escalation in industrialization and anthropogenic activity have resulted in an increase of pollutants released into the environment. Of these pollutants, heavy metals such as copper are particularly concerning due to their bio-accumulative nature. Due to its highly heterogeneous distribution and its dual nature as an essential micronutrient and toxic element, the genetic basis of copper tolerance is likely shaped by a complex interplay of genetic and environmental factors. Results: In this study, we utilized the natural variation present in multiple populations of Drosophila melanogaster collected across Europe to screen for variation in copper tolerance. We found that latitude and the degree of urbanization at the collection sites, rather than any other combination of environmental factors, were linked to copper tolerance. While previously identified copper-related genes were not differentially expressed in tolerant vs. sensitive strains, genes involved in metabolism, reproduction, and protease induction contributed to the differential stress response. Additionally, the greatest transcriptomic and physiological responses to copper toxicity were seen in the midgut, where we found that preservation of gut acidity is strongly linked to greater tolerance. Finally, we identified transposable element insertions likely to play a role in copper stress response. Conclusions: Overall, by combining genome-wide approaches with environmental association analysis, and functional analysis of candidate genes, our study provides a unique perspective on the genetic and environmental factors that shape copper tolerance in natural D. melanogaster populations and identifies new genes, transposable elements, and physiological traits involved in this complex phenotype. [ABSTRACT FROM AUTHOR]

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL mapbased cloning. [ABSTRACT FROM AUTHOR]