Quantitative Trait Loci Research Articles

Advancements in Lentil Genomics for Enhanced Crop Breeding: A Review

Lentil (Lens culinaris Medik) is an essential pulse crop that is widely grown for its high nutritional value, notably its high protein content, making it an important dietary component for vegetarians and vegans. Despite being the world's fifth most produced pulse, with large contributions from Canada and India, lentil production confronts obstacles such as poor productivity due to limited genetic improvement against biotic and abiotic stresses under rainfed cultivation conditions. Recent advances in lentil genetics and genomics, such as the discovery of genes related to yield, disease resistance, and nutritional content, have boosted breeding efforts to generate improved lentil varieties. The use of contemporary genomic techniques like molecular markers, marker-assisted selection (MAS), genomic selection (GS), and next-generation sequencing (NGS) technology has sped up the discovery of quantitative trait loci (QTLs) and the production of novel cultivars with superior agronomic characteristics. Databases such as NCBI and ENA, as well as specialized resources like KnowPulse, provide critical genomic data, while the creation of lentil genome assemblies, notably the CDC Redberry variety, has improved our understanding of lentil genetics. These resources help to solve the constraints of traditional breeding, particularly for complex characteristics impacted by genotype-environment interactions, opening the way for more robust and productive lentil varieties. Although the application of advanced tools such as genetic engineering, cisgenesis, and genome editing has moved more slowly in lentils than in other crops, their potential to improve lentil output is encouraging. Recent studies on lentil genomes, together with the creation of increased genetic resources and cutting-edge techniques, offer the ability to overcome production constraints and dramatically increase lentil production and quality throughout the world.

Rpv10.2: A Haplotype Variant of Locus Rpv10 Enables New Combinations for Pyramiding Downy Mildew Resistance Traits in Grapevine.

In viticulture, pathogens like the oomycete Plasmopara viticola, the causal agent of downy mildew, can cause severe yield loss and require extensive application of plant protection chemicals. Breeders are generating pathogen-resistant varieties exploiting American and Asian wild Vitis germplasm as sources of resistance. Several loci mediating resistance to P. viticola have been identified in the past but may be overcome by specifically adapted strains of the pathogen. Aiming to find and characterize novel loci, a cross population with Vitis amurensis ancestry was investigated searching for resistance-correlated quantitative trait loci (QTL). As a prerequisite, a genetic map was generated by analyzing the 244 F1 individuals derived from a cross of the downy mildew susceptible Vitis vinifera cultivar 'Tigvoasa' and the resistant V. amurensis pBC1 breeding line We 90-06-12. This genetic map is based on the information from 627 molecular markers including 56 simple sequence repeats and 571 rhAmpSeq markers. A phenotypic characterization of the progeny showed a clear segregation of the resistance traits in the F1 population after an experimental inoculation of leaf discs with downy mildew. Combining genetic and phenotypic data, an analysis for QTL revealed a major locus on linkage Group 9 that correlates strongly with the resistance to downy mildew. The locus was mapped to a region of about 80 kb on the PN40024 (12x.V2) grapevine reference genome. This genomic region co-localizes with the formerly identified locus Rpv10 from the grapevine cultivar 'Solaris'. As we found different allele sizes of the locus-linked SSR markers than those characterizing the known Rpv10 locus and differences in the sequence of a candidate gene, it was regarded as a haplotype variant and named Rpv10.2.

Identification of Quantitative Trait Loci Associated with Plant Adaptation Traits Using Nested Association Mapping Population.

This study evaluated 290 recombinant inbred lines (RILs) of the nested association mapping (NAM) population from the UK. The population derived from 24 families, where a common parent was "Paragon," one of the UK's spring wheat cultivar standards. All genotypes were tested in two regions of Kazakhstan at the Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, Almaty region, Southeast Kazakhstan, 2019-2022 years) and Alexandr Barayev Scientific-Production Center for Grain Farming (SPCGF, Shortandy, Akmola region, Northern Kazakhstan, 2019-2022 years). The studied traits consisted of plant adaptation-related traits, including heading date (HD, days), seed maturation date (SMD, days), plant height (PH, cm), and peduncle length (PL, cm). In addition, the yield per m2 was analyzed in both regions. Based on a field evaluation of the population in northern and southeastern Kazakhstan and using 10,448 polymorphic SNP (single-nucleotide polymorphism) markers, the genome-wide association study (GWAS) allowed for detecting 74 QTLs in four studied agronomic traits (HD, SMD, PH, and PL). The literature survey suggested that 16 of the 74 QTLs identified in our study had also been detected in previous QTL mapping studies and GWASs for all studied traits. The results will be used for further studies related to the adaptation and productivity of wheat in breeding projects for higher grain productivity.

Age-dependent genetic architectures of chicken body weight explored by multidimensional GWAS and molQTL analyses

Chicken body weight (BW) is a critical trait in breeding. Although genetic variants associated with BW have been investigated by genome-wide association studies (GWAS), the contributions of causal variants and their molecular mechanisms remain largely unclear in chickens. In this study, we construct a comprehensive genetic atlas of chicken BW by an integrative analysis of 30 age points and 5 quantitative trait loci (QTL) across 27 tissues. We find that chicken growth is a cumulative non-linear process, which can be divided into three distinct stages. Our GWAS analysis reveals that BW-related genetic variations show ordered patterns in these three stages. Genetic variations in chromosome 1 may regulate the overall growth process, likely by modulating the hypothalamus-specific expression of SLC25A30 and retina-specific expression of NEK3. Moreover, genetic variations in chromosome 4 and chromosome 27 may play dominant roles in regulating BW during stage Ⅱ (8–22 weeks) and stage Ⅲ (23–72 weeks), respectively. In summary, our study presents a comprehensive genetic atlas regulating developmental stage-specific changes in chicken BW, thus providing important resources for genomic selection in breeding programs.

Genome-wide association analysis of grain yield and Striga hermonthica and S. asiatica resistance in tropical and sub-tropical maize populations

BackgroundGenetic improvement for Striga hermonthica (Sh) and S. asiatica (Sa) resistance is the most economical and effective control method to enhance the productivity of maize and other major cereal crops. Hence, identification of quantitative trait loci (QTL) associated with Striga resistance and economic traits will guide the pace and precision of resistance breeding in maize. The objective of this study was to undertake a genome-wide association analysis of grain yield and Sh and Sa resistance among tropical and sub-tropical maize populations to identify putative genetic markers and genes for resistance breeding. 126 maize genotypes were evaluated under controlled environment conditions using artificial infestation of Sh and Sa. The test genotypes were profiled for grain yield (GY), Striga emergence counts at 8 (SEC8) and 10 (SEC10) weeks after planting, and Striga damage rate scores at 8 (SDR8) and 10 (SDR10) weeks after planting. Population structure analysis and genome-wide association mapping were undertaken based on 16,000 single nucleotide polymorphism (SNP) markers.ResultsA linkage disequilibrium (LD) analysis in 798,675 marker pairs revealed that 21.52% of pairs were in significant linkage (P < 0.001). Across the chromosomes, the LD between SNPs decayed below a critical level (r2 = 0.1) at a map distance of 0.19 Mbp. The genome-wide association study identified 50 significant loci associated with Sh resistance and 22 significant loci linked to Sa resistance, corresponding to 39 and 19 candidate genes, respectively.ConclusionThe study found non-significant QTL associated with dual resistance to the two examined Striga species Some of the detected genes reportedly conditioned insect and pathogen resistance, plant cell development, variable senescence, and pollen fertility. The markers detected in the present study for Sa resistance were reported for the first time. The gene Zm00001eb219710 was pleiotropic, and conditioned GY and SEC10, while Zm00001eb165170 affected SDR8 and SDR10, and Zm00001eb112030 conditioned SDR8 and SDR10 associated with Sh resistance. The candidate genes may facilitate simultaneous selection for Sh and Sa resistance and grain yield in maize after further validation and introgression in breeding pipelines. Overall, we recommend breeding maize specifically for resistance to each Striga species using germplasm adapted to the endemic region of each parasite.

Variance quantitative trait loci reveal gene-gene interactions which alter blood traits.

Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the "missing heritability" of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ∼450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ∼140,000 NIH All of Us and ∼70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

Signatures of selection analyses reveal genomic differences among three heavy pig breeds that constitute the genetic backbone of a dry-cured ham production system

The Italian pig farming industry is unique in its focus on raising heavy pigs primarily for the production of high-quality dry-cured hams. These products require pigs to be slaughtered at a live weight of around 170 kg at 9 months of age. The primary breeds used in this system are Italian Duroc, Italian Landrace, and Italian Large White which are crossed to produce lines that meet standard requirements. Over the past four decades, selection and breeding programmes for these breeds have been subjected to distinct selective pressures to highlight the characteristics of each breed. In this study, we investigated the genome of these breeds by analysing high-density single nucleotide polymorphism data from over 9 000 pigs to scan for signatures of selection using four different methods, two within breeds and two across breeds. This allowed to identify the genomic regions that differentiate these breeds as well as any relevant genes and biological terms. On a global scale, we found that the Italian Duroc breed exhibited a higher genetic differentiation from the Italian Landrace and Italian Large White breeds, with a pairwise FST value of 0.20 compared with the 0.13 between Italian Landrace and Italian Large White. This may reflect either their different origins or the different breeding goals, which are more similar for the Italian Landrace and Italian Large White breeds. Despite these genetic differences at a global level, few signatures of selection regions reached complete fixation, possibly due to challenges in detecting selection linked to quantitative polygenic traits. The differences among the three breeds are confirmed by the low level of overlap in the regions detected. Genetic enrichment analyses of the three breeds revealed pathways and genes related to various productive traits associated with growth and fat deposition. This may indicate a common selection direction aimed at enhancing specific production traits, though different biological mechanisms are likely targeted by the same directional selection in these three breeds. Therefore, these genes may play a critical role in determining the distinctive characteristics of Italian Duroc, Italian Landrace, and Italian Large White, and potentially influence the traits in crossbred pigs derived from them. Overall, the insights gained from this study will contribute to understanding how directional selection has shaped the genome of these heavy pig breeds and to better address selection strategies aimed at enhancing the meat processing industry linked with dry-cured ham production chains.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

Genetic dissection of stem and leaf rachis prickles in diploid rose using a pedigree-based QTL analysis.

Prickles are often deemed undesirable traits in many crops, including roses (Rosa sp.), and there is demand for rose cultivars with no or very few prickles. This study aims to identify new and/or validate reported quantitative trait loci (QTLs) associated with stem and leaf rachis prickle density, characterize the effects of functional haplotypes for major QTLs, and identify the sources of QTL-alleles associated with increased/decreased prickle density in roses. QTL mapping using pedigree-based analysis (PBA), and haplotype analysis were conducted on two multi-parental diploid rose populations (TX2WOB and TX2WSE). Twelve QTLs were identified on linkage groups (LGs) 2, 3, 4, and 6. The major QTLs for the stem prickle density were located between 42.25 and 45.66 Mbp on chromosome 3 of the Rosa chinensis genome assembly, with individual QTLs explaining 18 to 49% of phenotypic variance (PVE). The remaining mapped QTLs were minor. As for the rachis prickle density, several QTLs were detected on LG3, 4, and 6 with PVE 8 to 17%. Also, this study identified that ancestors R. wichurana 'Basye's Thornless', 'Old Blush', and the pollen parent of M4-4 were common sources of favorable alleles (q) associated with decreased prickle density, whereas 'Little Chief' and 'Srche Europy' were the source of unfavorable alleles (Q) in the TX2WOB and TX2WSE populations, respectively. The outcomes of this work complement other studies to locate factors that affect prickle density. These results can also be utilized to develop high-throughput DNA tests and apply parental selection to develop prickle-free rose cultivars.

Identification of Hotspot Regions for Candidate Genes Associated with Peanut (Arachis hypogaea L.) Pod and Seed Size on Chromosome A05

The size of peanut pods and seeds, which directly affects yield and quality, also has significant implications for mechanized production and market efficiency. Identifying relevant loci and mining candidate genes is crucial for cultivating high-yield peanut varieties. In this study, we employed advanced generation recombinant inbred lines developed by crossbreeding Huayu 44 and DF12 as the experimental material. Quantitative trait locus (QTL) mapping for traits related to pod and seed size was conducted across six environments. A total of 44 QTLs were detected, distributed on chromosomes A02, A05, B04, B08, and B10. An enrichment region for multiple QTLs was also identified on chromosome A05 (19.28~52.32 cm). In this region, 10 KASP markers were developed, narrowing the enrichment area to two candidate gene hotspot regions of 600.9 kb and 721.2 kb. By combining gene prediction and functional annotation within the intervals, 10 candidate genes, including those encoding cytochrome P450 protein, polyamine synthase, mannose-1-phosphate guanylyltransferase, pentatricopeptide repeat protein, and E2F transcription factor, were identified as regulators of pod and seed size. This study provides technical support for the genetic improvement and key gene identification of peanut pod and seed size.

Advancements in breeding for high oil content in maize (Zea mays. L)

Maize is one of the world’s most important food crops, supplying&gt;5% deity energy. Due to its wider adaptability, high yield potential, maize is widely utilized as food and animals world worldwide. It enriched with caloric starch and the high energy in its oil. The kernel oil content in commercial high-oil maize hybrids averages ∼8%, far lower than that in developed high-oil maize lines (as high as 20%). The maize seed/kernel consisted of endosperm (82%) and the germ (embryo and scutellum) (12%). The germ region makes up 80–84% of kernel oil, with the aleurone and endosperm making up 12% and 5%, respectively. High-oil maize may serve as a valuable forage maize germplasm for breeding. Maize oil is also considered as high-quality oil for human health due to the high proportion of polyun-saturated fatty acids. Selection for high oil increases the proportion of germ and further content of germ oil. The problems that over-shadow the successful production of high-oil corn are low grain yield potential, physiological cost of oil synthesis, low seed vigor, low kernel weight, shorter seed longevity, and poor germination of high-oil corn lines. It is necessary to make efforts to generate nested populations or use marker-assisted recurrent selection (MARS) to accumulate the advantageous quantitative trait loci (QTLs) in the pa-rental lines. The oil content of maize inbreeds and hybrids could be further improved with the use of transgenes, genomics, and metabolic engineering technologies in new insight for the development of high oil maize breeding and strategies.

A newly developed 20 K SNP array reveals QTLs for disease resistance to Cryptocaryon irritans in tiger pufferfish (Takifugu rubripes)

This study describes the development and application of a 20 K single nucleotide polymorphism (SNP) array in fugu (Takifugu rubripes). The main goal of this newly developed SNP array is to enhance selective breeding practice via the incorporation of genomic information. Fugu is one of the economic aquaculture species and mainly threatened by parasite disease. The performance of the array was evaluated in a farmed pufferfish population challenged with Cryptocaryon irritans and benchmarked against whole-genome sequencing of a subset of the animals. The array showed very high call rates, consistency between technical replicates, and concordance with genome-resequencing data. Heritability estimates using pedigree and SNP datasets demonstrated a significant genetic component of resistance to C. irritans, with heritability estimates ranging from 0.53 to 0.67, and prediction accuracies were significantly improved with the use of genomic data. Genome-wide association study identified two putative quantitative trait loci (QTLs) on chromosomes 3 and 17. Our findings suggest resistance to C. irritans can be rapidly increased in fugu populations by selective breeding, further expedited via the application of genetic markers. Therefore, they represent a valuable resource for selective breeding programmes aimed at enhancing disease resistance in fugu aquaculture.

Integrative multi-omics summary-based mendelian randomization identifies key oxidative stress-related genes as therapeutic targets for atrial fibrillation and flutter.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with substantial morbidity and mortality. Oxidative stress (OS) has been implicated in the pathogenesis of AF, suggesting that targeting OS-related genes could offer novel therapeutic opportunities. This study aimed to identify causal OS-related genes contributing to AF through a comprehensive multi-omics Summary-based Mendelian Randomization (SMR) approach. This study integrated data from genome-wide association studies (GWAS) with methylation quantitative trait loci (mQTL), expression QTL (eQTL), and protein QTL (pQTL) to explore the relationships between oxidative stress-related (OS-related) genes and AF risk. Genes associated with oxidative stress and AF were obtained from the Nielsen et al. study (discovery) and the FinnGen study (replication). The SMR analysis and HEIDI test were utilized to assess causal associations, followed by Bayesian co-localization analysis (PPH4 > 0.5) to confirm shared causal variants. Multi-omics data were employed to analyze the associations within mQTL-eQTL pathways. A two-sample MR analysis was conducted for sensitivity verification. The significance of findings was determined using a false discovery rate (FDR) < 0.05 and p_HEIDI > 0.01. At the DNA methylation level, 19 CpG sites near 7 unique genes were found to have causal effects on AF and strong co-localization evidence support (PPH4 > 0.70). At the gene expression level, six oxidative stress-related genes from eQTLGen and three from GTEx (v8), including TNFSF10, CDKN1A, ALOX15, TTN, PTK2, ALB, KCNJ5, and CASQ2, were found to have causal effects on AF in the sensitivity and co-localization analyses (PPH4 > 0.50). At the circulating protein level, both ALAD (OR 0.898, 95% CI 0.845-0.954, PPH4 = 0.67) and APOH (OR 0.896, 95% CI 0.844-0.952, PPH4 = 0.93) were associated with a lower risk of AF, and APOH was validated in the replication group. After integrating the multi-omics data between mQTL and eQTL, we identified two oxidative stress-related genes, TTN and CASQ2. The methylation of cg09915519 and cg10087519 in TTN was associated with higher expression of TTN and a lower risk of AF, which aligns with the negative effect of TTN gene expression on AF risk. TTN may play a protective role in AF. This study identified several OS-related genes, particularly TTN, as having causal roles in AF, which were verified across three-omics pathways. The findings underscore the importance of these genes in AF pathogenesis and highlight their potential as therapeutic targets. The integration of multi-omics data provides a comprehensive understanding of the molecular mechanisms underlying AF, paving the way for targeted therapeutic strategies.

Identifying genetic variants associated with chromatin looping and genome function

Here we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the RNASET2 locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying previously uncharacterized QTLs linked to cell-specific gene expression and connectivity.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Of buds and bits: a meta-QTL study identifies stable QTL for berry quality and yield traits in cranberry mapping populations (Vaccinium macrocarpon Ait.).

For nearly two centuries, cranberry (Vaccinium macrocarpon Ait.) breeders have improved fruit quality and yield by selecting traits on fruiting stems, termed "reproductive uprights." Crop improvement is accelerating rapidly in contemporary breeding programs due to modern genetic tools and high-throughput phenotyping methods, improving selection efficiency and accuracy. We conducted genotypic evaluation on 29 primary traits encompassing fruit quality, yield, and chemical composition in two full-sib cranberry breeding populations-CNJ02 (n = 168) and CNJ04 (n = 67)-over 3 years. Genetic characterization was further performed on 11 secondary traits derived from these primary traits. For CNJ02, 170 major quantitative trait loci (QTL; R 2 ≥ 0.10) were found with interval mapping, 150 major QTL were found with model mapping, and 9 QTL were found to be stable across multiple years. In CNJ04, 69 major QTL were found with interval mapping, 81 major QTL were found with model mapping, and 4 QTL were found to be stable across multiple years. Meta-QTL represent stable genomic regions consistent across multiple years, populations, studies, or traits. Seven multi-trait meta-QTL were found in CNJ02, one in CNJ04, and one in the combined analysis of both populations. A total of 22 meta-QTL were identified in cross-study, cross-population analysis using digital traits for berry shape and size (8 meta-QTL), digital images for berry color (2 meta-QTL), and three-study cross-analysis (12 meta-QTL). Together, these meta-QTL anchor high-throughput fruit quality phenotyping techniques to traditional phenotyping methods, validating state-of-the-art methods in cranberry phenotyping that will improve breeding accuracy, efficiency, and genetic gain in this globally significant fruit crop.

Fine-mapping genomic loci refines bipolar disorder risk genes.

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

QTL Mapping of Melampsora Leaf Rust Resistance and Yield Component Traits in the SalixF1 Hybrid Common Parent Population

ABSTRACTThe first step in trait introgression is to identify and assess novel sources of variation. For shrub willow (Salix) breeders, there is an abundance of understudied species within a genus that readily hybridizes. Breeding targets in shrub willow center on traits contributing to biomass yield for bioenergy. These include stem biomass, insect and pathogen resistance, and leaf architecture traits. More specifically, breeding for durable resistance to willow leaf rust (Melampsora spp.) is of particular importance as the pathogen can significantly reduce biomass yields in commercial production. The Salix F1 hybrid common parent population (Salix F1 HCP) was created to characterize the variation among eight species‐hybrid families and map QTL for targeted traits. A female and male S. purpurea were used as common parents in crosses made to male S. suchowensis, S. viminalis, S. koriyanagi, and S. udensis and female S. viminalis, S. integra, S. suchowensis to produce eight families that were planted in field trials at Cornell AgriTech in Geneva, NY and phenotyped. Using 16 previously described parental backcross linkage maps and two newly generated S. purpurea consensus maps, we identified 215 QTL across all eight families and in every parent. These included 15 leaf rust severity, 61 herbivory, 65 leaf architecture, and 74 yield component QTL, resulting in 50 unique overlapping regions within the population. These genetic loci serve as an important foundation for future shrub willow breeding, and each interspecific family was identified as a novel source of useful alleles for trait introgression into high yielding cultivars.

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations

Background Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. Methods The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). Results A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. Conclusion The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

QTL mapping for wheat ferulic acid concentration using 50 K SNP chip in a recombinant inbred line population of Zhongmai 578/Jimai 22

AbstractBackground and ObjectivesFerulic acid is a prominent bioactive compound found in wheat grains, known for its beneficial health effects, prompting significant interest from breeders and producers. The aim of this study was to identify new quantitative trait loci (QTL) to aid in the development of wheat varieties with increased ferulic acid concentration (FAC).FindingsIn this study, a recombinant inbred line population, resulting from a cross between Zhongmai 578 and Jimai 22, was evaluated in five different environments. Genotyping was performed using the wheat 50 K single‐nucleotide polymorphism (SNP) array. Three stable QTL, named QFAC.caas‐2D, QFAC.caas‐3B, and QFAC.caas‐4D, were identified. These QTL explained 4.24%–7.09%, 3.7%–4.57%, and 3.20%–5.06% of the phenotypic variances, respectively. Furthermore, three SNPs closely associated with above QTL were successfully converted into kompetitive allele‐specific polymerase chain reaction (KASP) markers.ConclusionsFAC is a complex trait governed by multiple minor‐effect QTL. The successful development of KASP markers opens up avenues for marker‐assisted selection in breeding programs.Significance and NoveltyThis study establishes a genetic foundation for understanding the genetic basis of FAC in wheat. The identified QTL and developed KASP markers offer valuable insights for quality breeding initiatives and the production of functional wheat‐based foods.