Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses.

Current genome-wide association studies (GWAS) of plasma proteomes provide additional possibilities for finding new drug targets for inflammatory dermatoses. We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel potential drug targets for inflammatory dermatoses. We performed MR and colocalization analysis using genetic variation as instrumental variables to determine the causal relationship between circulating plasma proteins and inflammatory dermatoses. 5 plasma proteins were found to be causally associated with dermatitis eczematosa, SLE, urticaria and psoriasis using cis-pQTLs as instrumental variables, but not found in AD and LP. 19 candidate genes with high colocalization evidence were identified. These potential drug targets still require more research and rigorous validation in future trials.

Identification of functional enhancer variants associated with type I diabetes in CD4+ T cells.

Type I diabetes is an autoimmune disease mediated by T-cell destruction of ß cells in pancreatic islets. Currently, there is no known cure, and treatment consists of daily insulin injections. Genome-wide association studies and twin studies have indicated a strong genetic heritability for type I diabetes and implicated several genes. As most strongly associated variants are noncoding, there is still a lack of identification of functional and, therefore, likely causal variants. Given that many of these genetic variants reside in enhancer elements, we have tested 121 CD4+ T-cell enhancer variants associated with T1D. We found four to be functional through massively parallel reporter assays. Three of the enhancer variants weaken activity, while the fourth strengthens activity. We link these to their cognate genes using 3D genome architecture or eQTL data and validate them using CRISPR editing. Validated target genes include CLEC16A and SOCS1. While these genes have been previously implicated in type 1 diabetes and other autoimmune diseases, we show that enhancers controlling their expression harbor functional variants. These variants, therefore, may act as causal type 1 diabetic variants.

Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population.

BACKGROUND: Carcass weight (HCW) and marbling (MARB) are critical for meat quality and market value in beef cattle. In composite breeds like Brangus, which meld the genetics of Angus and Brahman, SNP-based analyses have illuminated some genetic influences on these traits, but they fall short in fully capturing the nuanced effects of breed of origin alleles (BOA) on these traits. Focus on the impacts of BOA on phenotypic features within Brangus populations can result in a more profound understanding of the specific influences of Angus and Brahman genetics. Moreover, the consideration of BOA becomes particularly significant when evaluating dominance effects contributing to heterosis in crossbred populations. BOA provides a more comprehensive measure of heterosis due to its ability to differentiate the distinct genetic contributions originating from each parent breed. This detailed understanding of genetic effects is essential for making informed breeding decisions to optimize the benefits of heterosis in composite breeds like Brangus. OBJECTIVE: This study aims to identify quantitative trait loci (QTL) influencing HCW and MARB by utilizing SNP and BOA information, incorporating additive, dominance, and overdominance effects within a multi-generational Brangus commercial herd. METHODS: We analyzed phenotypic data from 1,066 genotyped Brangus steers. BOA inference was performed using LAMP-LD software using Angus and Brahman reference sets. SNP-based and BOA-based GWAS were then conducted considering additive, dominance, and overdominance models. RESULTS: The study identified numerous QTLs for HCW and MARB. A notable QTL for HCW was associated to the SGCB gene, pivotal for muscle growth, and was identified solely in the BOA GWAS. Several BOA GWAS QTLs exhibited a dominance effect underscoring their importance in estimating heterosis. CONCLUSIONS: Our findings demonstrate that SNP-based methods may not detect all genetic variation affecting economically important traits in composite breeds. BOA inclusion in genomic evaluations is crucial for identifying genetic regions contributing to trait variation and for understanding the dominance value underpinning heterosis. By considering BOA, we gain a deeper understanding of genetic interactions and heterosis, which is integral to advancing breeding programs. The incorporation of BOA is recommended for comprehensive genomic evaluations to optimize trait improvements in crossbred cattle populations.

Genome-wide association study for cashmere traits in Inner Mongolia cashmere goat population reveals new candidate genes and haplotypes.

BACKGROUND: The cashmere goat industry is one of the main pillars of animal husbandry in Inner Mongolia Autonomous Region, and plays an irreplaceable role in local economic development. With the change in feeding methods and environment, the cashmere produced by Inner Mongolia cashmere goats shows a tendency of coarser, and the cashmere yield can not meet the consumption demand of people. However, the genetic basis behind these changes is not fully understood. We measured cashmere traits, including cashmere yield (CY), cashmere diameter (CD), cashmere thickness (CT), and fleece length (FL) traits for four consecutive years, and utilized Genome-wide association study of four cashmere traits in Inner Mongolia cashmere goats was carried out using new genomics tools to infer genomic regions and functional loci associated with cashmere traits and to construct haplotypes that significantly affect cashmere traits. RESULTS: We estimated the genetic parameters of cashmere traits in Inner Mongolia cashmere goats. The heritability of cashmere yield, cashmere diameter, and fleece length traits of Inner Mongolia cashmere goats were 0.229, 0.359, and 0.250, which belonged to the medium heritability traits (0.2 ~ 0.4). The cashmere thickness trait has a low heritability of 0.053. We detected 151 genome-wide significantly associated SNPs with four cashmere traits on different chromosomes, which were very close to the chromosomes of 392 genes (located within the gene or within ± 500 kb). Notch3, BMPR1B, and CCNA2 have direct functional associations with fibroblasts and follicle stem cells, which play important roles in hair follicle growth and development. Based on GO functional annotation and KEGG enrichment analysis, potential candidate genes were associated with pathways of hair follicle genesis and development (Notch, P13K-Akt, TGF-beta, Cell cycle, Wnt, MAPK). We calculated the effective allele number of the Inner Mongolia cashmere goat population to be 1.109-1.998, the dominant genotypes of most SNPs were wild-type, the polymorphic information content of 57 SNPs were low polymorphism (0 < PIC < 0.25), and the polymorphic information content of 79 SNPs were moderate polymorphism (0.25 < PIC < 0.50). We analyzed the association of SNPs with phenotypes and found that the homozygous mutant type of SNP1 and SNP3 was associated with the highest cashmere yield, the heterozygous mutant type of SNP30 was associated with the lowest cashmere thickness, the wild type of SNP76, SNP77, SNP78, SNP80, and SNP81 was associated with the highest cashmere thickness, and the wild type type of SNP137 was associated with the highest fleece length. 21 haplotype blocks and 68 haplotype combinations were constructed. Haplotypes A2A2, B2B2, C2C2, and D4D4 were associated with increased cashmere yield, haplotypes E2E2, F1F1, G5G5, and G1G5 were associated with decreased cashmere fineness, haplotypes H2H2 was associated with increased cashmere thickness, haplotypes I1I1, I1I2, J1J4, L5L3, N3N2, N3N3, O2O1, P2P2, and Q3Q3 were associated with increased cashmere length. We verified the polymorphism of 8 SNPs by KASP, and found that chr7_g.102631194A > G, chr10_g.82715068 T > C, chr1_g.124483769C > T, chr24_g.12811352C > T, chr6_g.114111249A > G, and chr6_g.115606026 T > C were significantly genotyped in verified populations (P < 0.05). CONCLUSIONS: In conclusion, the genetic effect of single SNP on phenotypes is small, and SNPs are more inclined to be inherited as a whole. By constructing haplotypes from SNPs that are significantly associated with cashmere traits, it will help to reveal the complex and potential causal variations in cashmere traits of Inner Mongolia cashmere goats. This will be a valuable resource for genomics and breeding of the cashmere goat.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. Methods: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. Results: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Conclusion: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Androgen receptor-mediated pharmacogenomic expression quantitative trait loci: implications for breast cancer response to AR-targeting therapy.

BACKGROUND: Endocrine therapy is the most important treatment modality of breast cancer patients whose tumors express the estrogen receptor α (ERα). The androgen receptor (AR) is also expressed in the vast majority (80-90%) of ERα-positive tumors. AR-targeting drugs are not used in clinical practice, but have been evaluated in multiple trials and preclinical studies. METHODS: We performed a genome-wide study to identify hormone/drug-induced single nucleotide polymorphism (SNP) genotype - dependent gene-expression, known as PGx-eQTL, mediated by either an AR agonist (dihydrotestosterone) or a partial antagonist (enzalutamide), utilizing a previously well characterized lymphoblastic cell line panel. The association of the identified SNPs-gene pairs with breast cancer phenotypes were then examined using three genome-wide association (GWAS) studies that we have published and other studies from the GWAS catalog. RESULTS: We identified 13 DHT-mediated PGx-eQTL loci and 23 Enz-mediated PGx-eQTL loci that were associated with breast cancer outcomes post ER antagonist or aromatase inhibitors (AI) treatment, or with pharmacodynamic (PD) effects of AIs. An additional 30 loci were found to be associated with cancer risk and sex-hormone binding globulin levels. The top loci involved the genes IDH2 and TMEM9, the expression of which were suppressed by DHT in a PGx-eQTL SNP genotype-dependent manner. Both of these genes were overexpressed in breast cancer and were associated with a poorer prognosis. Therefore, suppression of these genes by AR agonists may benefit patients with minor allele genotypes for these SNPs. CONCLUSIONS: We identified AR-related PGx-eQTL SNP-gene pairs that were associated with risks, outcomes and PD effects of endocrine therapy that may provide potential biomarkers for individualized treatment of breast cancer.

GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions.

KEY MESSAGE: Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Pentatricopeptide repeat 153 (PPR153) restores maize C-type cytoplasmic male sterility in conjunction with RF4.

Maize (Zea mays L.) C-type cytoplasmic male sterility (CMS-C) is a highly used CMS system for maize commercial hybrid seed production. Rf4 is the major dominant restorer gene for CMS-C. Inbreds were recently discovered which contain the restoring Rf4 allele yet are unable to restore fertility due to the lack of an additional gene required for Rf4's restoration. To find this additional gene, QTL mapping and positional cloning were performed using an inbred that contained Rf4 but was incapable of restoring CMS-C. The QTL was mapped to a 738-kb interval on chromosome 2, which contains a Pentatricopeptide Repeat (PPR) gene cluster. Allele content comparisons of the inbreds identified three potential candidate genes responsible for fertility restoration in CMS-C. Complementation via transformation of these three candidate genes showed that PPR153 (Zm00001eb114660) is required for Rf4 to restore fertility to tassels. The PPR153 sequence is present in B73 genome, but it is not capable of restoring CMS-C without Rf4. Analysis using NAM lines revealed that Rf4 requires the presence of PPR153 to restore CMS-C in diverse germplasms. This research uncovers a major CMS-C genetic restoration pathway and can be used for identifying inbreds suitable for maize hybrid production with CMS-C cytoplasm.

Map-Based Cloning of the Causal Gene for a Seed Dormancy Quantitative Trait Locus in Barley.

Seed dormancy is an important agronomic trait in cereal crops. Throughout the domestication of cereals, seed dormancy has been reduced to obtain uniform germination. However, grain crops must retain moderate levels of seed dormancy to prevent problems such as preharvest sprouting in wheat (Triticum aestivum) and barley (Hordeum vulgare). To produce modern cultivars with the appropriate seed dormancy levels, it is important to identify the genes responsible for seed dormancy. With recent advances in sequencing technology, several causal genes for seed dormancy quantitative trait loci (QTLs) have been identified in barley and wheat. Here, we present a method to identify causal genes for seed dormancy QTLs in barley, a method that is also applicable to other cereals.

Genome-Wide Association Study of Preharvest Sprouting in Wheat.

Genome-wide association study (GWAS) is widely used to characterize genes or quantitative trait loci (QTLs) associated with preharvest sprouting and seed dormancy. GWAS can identify both previously discovered and novel QTLs across diverse genetic panels. The high-throughput SNP arrays or next-generation sequencing technologies have facilitated the identification of numerous genetic markers, thereby significantly enhancing the resolution of GWAS. Although various methods have been developed, the fundamental principles underlying these techniques remain constant. Here, we provide a basic technological flow to perform seed dormancy assay, followed by GWAS using population structure control, and compared it with previous identified QTLs and genes.

Expression genome-wide association study identifies key regulatory variants enriched with metabolic and immune functions in four porcine tissues.

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

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population.

KEY MESSAGE: The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Beef Cattle Genome Project: Advances in Genome Sequencing, Assembly, and Functional Genes Discovery.

Beef is a major global source of protein, playing an essential role in the human diet. The worldwide production and consumption of beef continue to rise, reflecting a significant trend. However, despite the critical importance of beef cattle resources in agriculture, the diversity of cattle breeds faces severe challenges, with many breeds at risk of extinction. The initiation of the Beef Cattle Genome Project is crucial. By constructing a high-precision functional annotation map of their genome, it becomes possible to analyze the genetic mechanisms underlying important traits in beef cattle, laying a solid foundation for breeding more efficient and productive cattle breeds. This review details advances in genome sequencing and assembly technologies, iterative upgrades of the beef cattle reference genome, and its application in pan-genome research. Additionally, it summarizes relevant studies on the discovery of functional genes associated with key traits in beef cattle, such as growth, meat quality, reproduction, polled traits, disease resistance, and environmental adaptability. Finally, the review explores the potential of telomere-to-telomere (T2T) genome assembly, structural variations (SVs), and multi-omics techniques in future beef cattle genetic breeding. These advancements collectively offer promising avenues for enhancing beef cattle breeding and improving genetic traits.

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

KEY MESSAGE: Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56 kb to 6.37 Mb, with a median of 921 kb, of which 27% had a CI lower than 500 kb and 53% had a CI lower than 1 Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Identification of candidate gene associated with maize northern leaf blight resistance in a multi-parent population.

KEY MESSAGE: QTL mapping combined with genome-wide association studies, revealed a potential candidate gene for  resistance to northern leaf blight in the tropical CATETO-related maize line YML226, providing a basis for marker-assisted selection of maize varieties Northern leaf blight (NLB) is a foliar disease that can cause severe yield losses in maize. Identifying and utilizing NLB-resistant genes is the most effective way to prevent and control this disease. In this study, five important inbred lines of maize were used as parental lines to construct a multi-parent population for the identification of NLB-resistant loci. QTL mapping and GWAS analysis revealed that QTL qtl_YML226_1, which had the largest phenotypic variance explanation (PVE) of 9.28%, and SNP 5-49,193,921 were co-located in the CATETO-related line YML226. This locus was associated with the candidate gene Zm00001d014471, which encodes a pentatricopeptide repeat (PPR) protein. In the coding region of Zm00001d014471, YML226 had more specific SNPs than the other parental lines. qRT-PCR showed that the relative expressions of Zm00001d014471 in inoculated and uninoculated leaves of YML226 were significantly higher, indicating that the expression of the candidate gene was correlated with NLB resistance. The analysis showed that the higher expression level in YML226 might be caused by SNP mutations. This study identified NLB resistance candidate loci and genes in the tropical maize inbred line YML226 derived from the CATETO germplasm, thereby providing a theoretical basis for using modern marker-assisted breeding techniques to select genetic resources resistant to NLB.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Genome-wide association study reveals marker-trait associations for major agronomic traits in proso millet (Panicum miliaceum L.).

MAIN CONCLUSION: The pilot-scale genome-wide association study in the US proso millet identified twenty marker-trait associations for five morpho-agronomic traits identifying genomic regions for future studies (e.g. molecular breeding and map-based cloning). Proso millet (Panicum miliaceum L.) is an ancient grain recognized for its excellent water-use efficiency and short growing season. It is an indispensable part of the winter wheat-based dryland cropping system in the High Plains of the USA. Its grains are endowed with high nutritional and health-promoting properties, making it increasingly popular in the global market for healthy grains. There is a dearth of genomic resources in proso millet for developing molecular tools to complement conventional breeding for developing high-yielding varieties. Genome-wide association study (GWAS) is a widely used method to dissect the genetics of complex traits. In this pilot study of the first-ever GWAS in the US proso millet, 71 globally diverse genotypes of 109 the US proso millet core collection were evaluated for five major morpho-agronomic traits at two locations in western Nebraska, and GWAS was conducted to identify single nucleotide polymorphisms (SNPs) associated with these traits. Analysis of variance showed that there was a significant difference among the genotypes, and all five traits were also found to be highly correlated with each other. Sequence reads from genotyping-by-sequencing (GBS) were used to identify 11,147 high-quality bi-allelic SNPs. Population structure analysis with those SNPs showed stratification within the core collection. The GWAS identified twenty marker-trait associations (MTAs) for the five traits. Twenty-nine putative candidate genes associated with the five traits were also identified. These genomic regions can be used to develop genetic markers for marker-assisted selection in proso millet breeding.

Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis.

Rice blast disease is the most devastating disease constraining crop productivity. Vertical resistance to blast disease is widely studied despite its instability. Clusters of genes or QTLs conferring blast resistance that offer durable horizontal resistance are important in resistance breeding. In this study, we aimed to refine the reported QTLs and identify stable meta-QTLs (MQTLs) associated with rice blast resistance. A total of 435 QTLs were used to project 71 MQTLs across all the rice chromosomes. As many as 199 putative rice blast resistance genes were identified within 53 MQTL regions. The genes included 48 characterized resistance gene analogs and related proteins, such as NBS-LRR type, LRR receptor-like kinase, NB-ARC domain, pathogenesis-related TF/ERF domain, elicitor-induced defense and proteins involved in defense signaling. MQTL regions with clusters of RGA were also identified. Fifteen highly significant MQTLs included 29 candidate genes and genes characterized for blast resistance, such as Piz, Nbs-Pi9, pi55-1, pi55-2, Pi3/Pi5-1, Pi3/Pi5-2, Pikh, Pi54, Pik/Pikm/Pikp, Pb1 and Pb2. Furthermore, the candidate genes (42) were associated with differential expression (in silico) in compatible and incompatible reactions upon disease infection. Moreover, nearly half of the genes within the MQTL regions were orthologous to those in O. sativa indica, Z. mays and A. thaliana, which confirmed their significance. The peak markers within three significant MQTLs differentiated blast-resistant and susceptible lines and serve as potential surrogates for the selection of blast-resistant lines. These MQTLs are potential candidates for durable and broad-spectrum rice blast resistance and could be utilized in blast resistance breeding.

Mining for QTL controlling maize low-phosphorus response genes combined with deep resequencing of RIL parental genomes and in silico GWAS analysis.

KEY MESSAGE: Extensive and comprehensive phenotypic data from a maize RIL population under both low- and normal-Pi treatments were used to conduct QTL mapping. Additionally, we integrated parental resequencing data from the RIL population, GWAS results, and transcriptome data to identify candidate genes associated with low-Pi stress in maize. Phosphorus (Pi) is one of the essential nutrients that greatly affect the maize yield. However, the genes underlying the QTL controlling maize low-Pi response remain largely unknown. In this study, a total of 38 traits at both seedling and maturity stages were evaluated under low- and normal-Pi conditions using a RIL population constructed from X178 (tolerant) and 9782 (sensitive), and most traits varied significantly between low- and normal-Pi treatments. Twenty-nine QTLs specific to low-Pi conditions were identified after excluding those with common intervals under both low- and normal-Pi conditions. Furthermore, 45 additional QTLs were identified based on the index value ((Trait_under_LowPi-Trait_under_NormalPi)/Trait_under_NormalPi) of each trait. These 74 QTLs collectively were classified as Pi-dependent QTLs. Additionally, 39 Pi-dependent QTLs were clustered in nine HotspotQTLs. The Pi-dependent QTL interval contained 19,613 unique genes, 6,999 of which exhibited sequence differences with non-synonymous mutation sites between X178 and 9782. Combined with in silico GWAS results, 277 consistent candidate genes were identified, with 124 genes located within the HotspotQTL intervals. The transcriptome analysis revealed that 21 genes, including the Pi transporter ZmPT7 and the strigolactones pathway-related gene ZmPDR1, exhibited consistent low-Pi stress response patterns across various maize inbred lines or tissues. It is noteworthy that ZmPDR1 in maize roots can be sharply up-regulated by low-Pi stress, suggesting its potential importance as a candidate gene for responding to low-Pi stress through the strigolactones pathway.