Significant Significant Single Nucleotide Polymorphisms Research Articles

Genome wide association studies on three-dimensional aortic geometric indices measured with magnetic resonance imaging

Abstract Introduction To date, three-dimensional properties of the aorta have not been comprehensively quantified in a population level dataset, nor has there been a genome-wide association study (GWAS) to identify significant single nucleotide polymorphisms (SNPs) associated with geometric properties of the aorta. Methods We segmented the thoracic aorta of 48,045 subjects in the UK Biobank using a U-Net convolutional neural network. We then computed geometric measurements (centerline length, arch height, arch width, arch height-width ratio, curvature, and mean, minimum and maximum diameter) across six subsegments of the aorta. Centerline length was allometrically indexed to body height. Using generalized linear models, we performed GWAS on each geometric phenotype adjusted for the following covariates: (1) age, (2) sex, and (3) ten genetic principal components. SNPs with a minor allele frequency < 0.05 were excluded. We performed LD-based clumping with an r2 threshold of 0.001 to identify significant loci and used LDSC to compute the genetic heritability of each geometric phenotype. Results After performing LD clumping, we identified 457 significant(P<5e-8) SNPs across 36 aortic geometric parameters. GWAS-based heritability analyses showed a mean heritability of 13.8 (9.9) % across all aortic geometric parameters, with all intercepts being close to 1, suggesting minimal genomic inflation. Maximum aorta diameter exhibited the highest observed heritability of 34.5 (3.0) %. Allometric centerline length showed a heritability of 13.6 (1.5) %. Similarly, arch height and width showed heritability of 14.6 (1.5) %, and 13.64 (1.5) % respectively. Aortic curvature showed limited heritability at 8.54 (1.2) %, with arch curvature being further reduced to 7.6 (1.20) %. Conclusion To our knowledge, this was the first study that investigated the genetic architecture of aortic centerline length, aortic curvature, and aortic tortuosity. These results suggest that aortic remodeling associated with aging and pathological conditions has important genetic contributions. SNPs associated with aortic geometry may identify therapeutic targets to prevent aortic remodeling, which should be the focus of future work.significant_snpsheritability

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize.

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Identification of Genomic Regions Associated with Powdery Mildew Resistance in Watermelon through Genome-Wide Association Study.

Watermelon (Citrullus spp.) is an economically important crop globally, but it is susceptible to various diseases, including powdery mildew. Previous studies have identified genetic factors associated with powdery mildew resistance. However, further research using diverse genetic approaches is necessary to elucidate the underlying genetic mechanisms of this resistance. In this study, the germplasm collection comprising highly homozygous inbred lines was employed, which enabled the accumulation of consistent data and improved the reliability of the genome-wide association study (GWAS) findings. Our investigation identified two significant single-nucleotide polymorphisms (SNPs), pm2.1 and pm3.1, which were strongly associated with disease resistance. Moreover, several candidate genes were revealed within the linkage disequilibrium (LD) blocks surrounding the significant SNPs. In conclusion, the identification of significant SNPs and their additive effects, combined with the discovery of relevant candidate genes, expanded our understanding of the genetic basis of disease resistance and can pave the way for the development of more resilient watermelon cultivars through marker-assisted selection.

Genome-Wide Analysis of Fruit Color and Carotenoid Content in Capsicum Core Collection.

This study investigated carotenoid content and fruit color variation in 306 pepper accessions from diverse Capsicum species. Red-fruited accessions were predominant (245 accessions), followed by orange (35) and yellow (20). Carotenoid profiles varied significantly across accessions, with capsanthin showing the highest mean concentration (239.12 μg/g), followed by β-cryptoxanthin (63.70 μg/g) and zeaxanthin (63.25 μg/g). Total carotenoid content ranged from 7.09 to 2566.67 μg/g, emphasizing the diversity within the dataset. Correlation analysis revealed complex relationships between carotenoids, with strong positive correlations observed between total carotenoids and capsanthin (r = 0.94 ***), β-cryptoxanthin (r = 0.87 ***), and zeaxanthin (r = 0.84 ***). Principal component analysis (PCA) identified two distinct carotenoid groups, accounting for 67.6% of the total variance. A genome-wide association study (GWAS) identified 91 significant single nucleotide polymorphisms (SNPs) associated with fruit color (15 SNPs) and carotenoid content (76 SNPs). These SNPs were distributed across all chromosomes, with varying numbers on each. Among individual carotenoids, α-carotene was associated with 28 SNPs, while other carotenoids showed different numbers of associated SNPs. Candidate genes encoding diverse proteins were identified near significant SNPs, potentially contributing to fruit color variation and carotenoid accumulation. These included pentatricopeptide repeat-containing proteins, mitochondrial proton/calcium exchangers, E3 ubiquitin-protein ligase SINAT2, histone-lysine N-methyltransferase, sucrose synthase, and various enzymes involved in metabolic processes. Seven SNPs exhibited pleiotropic effects on multiple carotenoids, particularly β-cryptoxanthin and capsanthin. The findings of this study provide insights into the genetic architecture of carotenoid biosynthesis and fruit color in peppers, offering valuable resources for targeted breeding programs aimed at enhancing the nutritional and sensory attributes of pepper varieties.

Deciphering Trachinotus ovatus resistance to Streptococcus agalactiae:GWAS identification of key SNPs and candidate genes

The genetic basis of resistance in Trachinotus ovatus to Streptococcus agalactiae, a prevalent pathogen in warm-water fish, was explored in this study. T. ovatus, an economically significant marine fish, is widely distributed in tropical and subtropical oceans. This research involved the whole-genome resequencing of 200 T. ovatus individuals and a genome-wide association study (GWAS) to identify significant single nucleotide polymorphisms (SNPs) associated with resistance to S. agalactiae. Methods included infection experiments with S. agalactiae, quality control of DNA samples, high-throughput sequencing, SNP detection and quality control, analysis of population genetic structure, linkage disequilibrium (LD) analysis, GWAS, screening of candidate genes, and genotyping of significant SNPs. Results indicated a significant association of specific SNP loci with resistance in T. ovatus to S. agalactiae, notably SNP18597184 on chromosome LG9, associated with the NTRK3 and IFNK genes involved in neural development, survival, and immune response.

Genome-wide association study provided insights into the polled phenotype and polled intersex syndrome (PIS) in goats

BackgroundBreeding polled goats is a welfare-friendly approach for horn removal in comparison to invasive methods. To gain a comprehensive understanding of the genetic basis underlying polledness in goats, we conducted whole-genome sequencing of 106 Xinong Saanen dairy goats, including 33 horned individuals, 70 polled individuals, and 3 polled intersexuality syndrome (PIS) individuals.MethodsThe present study employed a genome-wide association study (GWAS) and linkage disequilibrium (LD) analysis to precisely map the genetic locus underlying the polled phenotype in goats.ResultsThe analysis conducted in our study revealed a total of 320 genome-wide significant single nucleotide polymorphisms (SNPs) associated with the horned/polled phenotype in goats. These SNPs exhibited two distinct peaks on chromosome 1, spanning from 128,817,052 to 133,005,441 bp and from 150,336,143 to 150,808,639 bp. The present study identified three genome-wide significant SNPs, namely Chr1:129789816, Chr1:129791507, and Chr1:129791577, as potential markers of PIS-affected goats. The results of our LD analysis suggested a potential association between MRPS22 and infertile intersex individuals, as well as a potential association between ERG and the polled trait in goats.ConclusionWe have successfully identified three marker SNPs closely linked to PIS, as well as several candidate genes associated with the polled trait in goats. These results may contribute to the development of SNP chips for early prediction of PIS in goats, thereby facilitating breeding programs aimed at producing fertile herds with polled traits.

Mendelian randomization analysis of psoriasis and psoriatic arthritis associated with risks of ulcerative colitis.

This study is designed to explore the potential causal relationship between psoriasis and psoriatic arthritis (PsA) while investigating the genetic basis shared by these inflammatory diseases. Significant single nucleotide polymorphisms (SNPs) associated with UC, psoriasis, and PsA were selected as genetic instrumental variables using Genome-Wide Association Study (GWAS) datasets. Additionally, Mendelian randomization (MR) methods, including inverse-variance weighting (IVW), MR-Egger regression, and Weighted Median (WME), were utilized to evaluate the causal relationships between these diseases. Moreover, sensitivity analysis and heterogeneity testing were conducted to validate the stability of the results. A total of 123 significant SNPs associated with psoriasis, PsA, and UC were identified as genetic instrumental variables based on GWAS datasets. The analysis revealed a 36% increased risk of UC with psoriasis (odds ratio [OR]=1.350, 95% confidence interval [CI]=1.065-1.729, P=0.012) and a 32.9% increased risk of UC with PsA (OR=1.329, 95% CI=1.176-1.592, P<0.001). Further analysis showed a 43.5% increased risk of psoriasis with UC (OR=1.435, 95% CI=1.274-1.831, P<0.001) and a 45.8% increased risk of PsA with UC (OR=1.458, 95% CI=1.166-1.822, P=0.0013). In addition, sensitivity analysis and heterogeneity testing demonstrated the high stability of these results. Particularly, neither MR-Egger regression analysis nor leave-one-out analysis revealed significant heterogeneity or pleiotropy bias, indicating the reliability of these causal estimates. Moreover, the use of the MR-PRESSO further confirmed the positive correlation between psoriasis and UC, and the corrected estimates remained consistent with IVW analysis results after excluding potential outlier SNPs, enhancing the credibility of the analysis. This study strengthens the understanding of the genetic and causal relationships among UC, psoriasis, and PsA through GWAS and MR methods, revealing the genetic basis they may share. These findings not only provide a novel perspective on the comorbidity mechanisms of these diseases but also offer a valuable reference for the development of future treatment strategies and intervention measures.

Genome-wide association study comparison analysis based on Hanwoo full-sib family.

The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and genome-wide association study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking. This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits. A total of 16 significant single nucleotide polymorphism (SNP) markers were identified in Group A, comprising 7 for carcass weight (CWT), 3 for back fat thickness (BFT), and 6 for marbling score (MS). In Group B, 7 significant SNP markers were identified, including 3 for CWT, 1 for eye muscle area, 1 for BFT, and 2 for MS. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while protein-protein interaction analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A. GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes.

Genome-wide association study of stem structural characteristics that extracted by a high-throughput phenotypic analysis "LabelmeP rice" in rice.

Stem is important for assimilating transport and plant strength; however, less is known about the genetic basis of its structural characteristics. In this study, a high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles, which allows the establishment of a stem cross-section phenotype dataset containing anatomical information of 1738 images from hand-cut transections of stems collected from 387 rice germplasm accessions grown over two successive seasons. Then, the phenotypic diversity of the rice accessions was evaluated. Genome-wide association studies identified 94, 83, and 66 significant single nucleotide polymorphisms (SNPs) for the assayed traits in 2 years and their best linear unbiased estimates, respectively. These SNPs can be integrated into 29 quantitative trait loci (QTL), and 11 of them were common in 2 years, while correlated traits shared 19. In addition, 173 candidate genes were identified, and six located at significant SNPs were repeatedly detected and annotated with a potential function in stem development. By using three introgression lines (chromosome segment substitution lines), four of the 29 QTLs were validated. LOC_Os01g70200, located on the QTL uq1.4, is detected for the area of small vascular bundles (SVB) and the rate of large vascular bundles number to SVB number. Besides, the CRISPR/Cas9 editing approach has elucidated the function of the candidate gene LOC_Os06g46340 in stem development. In conclusion, the results present a time- and cost-effective method that provides convenience for extracting rice stem anatomical traits and the candidate genes/QTL, which would help improve rice.

Genome-wide association study identifies genomic regions associated with key reproductive traits in Korean Hanwoo cows.

Conducting genome-wide association studies (GWAS) for reproductive traits in Hanwoo cattle, including age at first calving (AFC), calving interval (CI), gestation length (GL), and number of artificial inseminations per conception (NAIPC), is of paramount significance. These analyses provided a thorough exploration of the genetic basis of these traits, facilitating the identification of key markers for targeted trait improvement. Breeders can optimize their selection strategies, leading to more efficient and sustainable breeding programs, by incorporating genetic insights. This impact extends beyond individual traits and contributes to the overall productivity and profitability of the Hanwoo beef cattle industry. Ultimately, GWAS is essential in ensuring the long-term genetic resilience and adaptability of Hanwoo cattle populations. The primary goal of this study was to identify significant single nucleotide polymorphisms (SNPs) or quantitative trait loci (QTLs) associated with the studied reproductive traits and subsequently map the underlying genes that hold promise for trait improvement. A genome-wide association study of reproductive traits identified 68 significant single nucleotide polymorphisms (SNPs) distributed across 29 Bos taurus autosomes (BTA). Among them, BTA14 exhibited the highest number of identified SNPs (25), whereas BTA6, BTA7, BTA8, BTA10, BTA13, BTA17, and BTA20 exhibited 8, 5, 5, 3, 8, 2, and 12 significant SNPs, respectively. Annotation of candidate genes within a 500kb region surrounding the significant SNPs led to the identification of ten candidate genes relevant to age at first calving. These genes were: FANCG, UNC13B, TESK1, TLN1, and CREB3 on BTA8; FAM110B, UBXN2B, SDCBP, and TOX on BTA14; and MAP3K1 on BTA20. Additionally, APBA3, TCF12, and ZFR2, located on BTA7 and BTA10, were associated with the calving interval; PAX1, SGCD, and HAND1, located on BTA7 and BTA13, were linked to gestation length; and RBM47, UBE2K, and GPX8, located on BTA6 and BTA20, were linked to the number of artificial inseminations per conception in Hanwoo cows. The findings of this study enhance our knowledge of the genetic factors that influence reproductive traits in Hanwoo cattle populations and provide a foundation for future breeding strategies focused on improving desirable traits in beef cattle. This research offers new evidence and insights into the genetic variants and genome regions associated with reproductive traits and contributes valuable information to guide future efforts in cattle breeding.

Genome-wide association study identifies a novel candidate gene for egg production traits in chickens.

Qingyuan partridge chicken is a renowned indigenous yellow broiler breed in China. Egg production traits are important economic traits for chickens. With the decreasing cost of whole genome resequencing, identifying candidate genes with more precision has become possible. In order to identify molecular markers and candidate genes associated with egg production traits, we conducted genome-wide association studies based on the resequencing data of 287 female Qingyuan partridge chickens. For each hen, age at first egg and egg laying rate were recorded and calculated, respectively. With a univariate linear mixed model, we detected one genome-wide significant single nucleotide polymorphism (SNP) and three chromosome-wide significant SNPs associated with egg laying rate. MTA2 is highly likely to be a functional gene for egg laying rate. Our study identifies MTA2 as the first time to be associated with egg laying rate. Findings in our study will advance our understanding of the genetic basis of egg production and have the potential to improve the efficiency of genomic selection in chickens.

Integration of genome-wide association and genomic prediction for dissecting seed protein and amino acid in foxtail millet

ContextThe exploitation and utilization of genetic variation in crop germplasm benefit genomics-informed breeding programs. The integration of genome-wide association study (GWAS) and genomic prediction (GP) holds promise for illustrating genetic basis of phenotypic variation. Currently, the use of this integrative approach for dissecting grain protein and amino acid traits remains limited in crop species. ObjectiveThe research objectives were to identify significant single nucleotide polymorphisms (SNPs) associated with seed protein and amino acids and to assess the genomic predictive ability for nutritional traits in natural population of foxtail millet. MethodsIn this study, the concentrations of seed protein and 17 amino acids were assessed in a training population consisting of 238 diverse accessions of foxtail millet. The significant SNPs associated with these phenotypes were identified by GWAS with a mix-linear model. Extensive cross-validation was performed to evaluate the accuracy of genomic prediction of seed nutritional traits. Furthermore, the genomic prediction model was optimized and externally validated in 211 untested materials with the most significant SNPs obtained from GWAS. ResultsA significant genotype variance was detected for seed protein and amino acids and broad-sense heritability (h2) ranged from 0.45 to 0.87 (P < 0.05). The GWAS identified 39 significant SNPs associated with 15 amino acids (p < 1e-06), explaining an average of 12.2% phenotypic variation per locus. The number of significant SNPs ranged from 1 (Asp, Ile, Glu) to 23 for Phe amino acid, including six SNPs associated with one or multiple amino acids. In the training population, the prediction accuracies of protein and amino acids, tested using the 10-fold cross-validation model with whole genome-wide SNPs, ranged from 0.23 (Glu) to 0.60 (Lys) with an average value of 0.44%. Population structure had little effect on the prediction accuracy. In an empirical validation experiment involving 211 untested accessions, the averaged prediction accuracy increased by 12.3% across all traits when using the 1000 top SNPs obtained from GWAS, compared with the same number of randomly selected SNPs from the entire genome. ConclusionsLarge variations of seed protein and amino acids were found in a natural population of foxtail millet. GWAS identified 39 significant SNPs associated with 15 amino acids, and six SNPs were linked to more than one trait. The 10-fold cross-validation model produced the relatively higher prediction accuracies in seed quality traits of diverse accessions. By applying the significant SNPs from GWAS to the untested accessions, the use of 1000 GWAS-top SNPs resulted in more powerful predictions for complex nutritional traits than those using the same number of randomly selected SNPs across the genome. SignificanceThe research highlights the importance and effectiveness of integrative GWAS and genomic prediction in revealing the genetic architecture of seed protein and amino acid variation in foxtail millet. The results provide valuable insights into assessment and improvement of germplasm resources for advancing precision crop phenotyping and enhancing breeding programs for crop nutritional traits.

Genome-wide association study of growth curve parameters reveals novel genomic regions and candidate genes associated with metatarsal bone traits in chickens

The growth and development of chicken bones have an enormous impact on the health and production performance of chickens. However, the development pattern and genetic regulation of the chicken skeleton are poorly understood. This study aimed to evaluate metatarsal bone growth and development patterns in chickens via non-linear models, and to identify the genetic determinants of metatarsal bone traits using a genome-wide association study (GWAS) based on growth curve parameters. Data on metatarsal length (MeL) and metatarsal circumference (MeC) were obtained from 471 F2 chickens (generated by crossing broiler sires, derived from a line selected for high abdominal fat, with Baier layer dams) at 4, 6, 8, 10, and 12 weeks of age. Four non-linear models (Gompertz, Logistic, von Bertalanffy, and Brody) were used to fit the MeL and MeC growth curves. Subsequently, the estimated growth curve parameters of the mature MeL or MeC (A), time-scale parameter (b), and maturity rate (K) from the non-linear models were utilized as substitutes for the original bone data in GWAS. The Logistic and Brody models displayed the best goodness-of-fit for MeL and MeC, respectively. Single-trait and multi-trait GWASs based on the growth curve parameters of the Logistic and Brody models revealed 4 618 significant single nucleotide polymorphisms (SNPs), annotated to 332 genes, associated with metatarsal bone traits. The majority of these significant SNPs were located on Gallus gallus chromosome (GGA) 1 (167.433–176.318 Mb), GGA2 (96.791–103.543 Mb), GGA4 (65.003–83.104 Mb) and GGA6 (64.685–95.285 Mb). Notably, we identified 12 novel GWAS loci associated with chicken metatarsal bone traits, encompassing 35 candidate genes. In summary, the combination of single-trait and multi-trait GWASs based on growth curve parameters uncovered numerous genomic regions and candidate genes associated with chicken bone traits. The findings benefit an in-depth understanding of the genetic architecture underlying metatarsal growth and development in chickens.

Genome-Wide Association Study to Identify Possible Candidate Genes of Snap Bean Leaf and Pod Color.

Color can be an indicator of plant health, quality, and productivity, and is useful to researchers to understand plant nutritional content in their studies. Color may be related to chlorophyll content and photosynthetic activity and provides information for those studying diseases and mineral nutrition because every nutrient deficiency and many diseases produce symptoms that affect color. In order to identify significant loci related to both leaf and pod color in a snap bean (Phaseolus vulgaris L.) diversity panel, a genome-wide association study (GWAS) was carried out. Leaf color in one and pod traits in multiple environments were characterized using a colorimeter. L*a*b* color data were recorded and used to calculate chroma (C*) and hue angle (H°). Leaves were evaluated at three positions (lower, middle, and upper) in the canopy and both pod exterior and interior colors were obtained. GWAS was conducted using two reference genomes that represent the Andean (G19833) and Middle American (5-593) domestication centers. Narrow sense heritabilities were calculated using the mixed linear model (MLM) method in genome association and prediction integrated tool (GAPIT), and significant single nucleotide polymorphisms (SNPs) for each color parameter were obtained using the Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) GWAS model with two principal components (PCAs). In comparison to pod color traits, narrow sense heritabilities of leaf traits were low and similar for both reference genomes. Generally, narrow sense heritability for all traits was highest in the lower, followed by middle, and then upper leaf positions. Heritability for both pod interior and exterior color traits was higher using the G19833 reference genome compared to 5-593 when evaluated by year and means across years. Forty-five significant SNPs associated with leaf traits and 872 associated with pods, totaling 917 significant SNPs were identified. Only one SNP was found in common for both leaf and pod traits on Pv03 in the 5-593 reference genome. One-hundred thirteen significant SNPs, 30 in leaves and 83 in pods had phenotypic variation explained (PVE) of 10% or greater. Fourteen SNPs (four from G19833 and ten from 5-593) with ≥10 PVE%, large SNP effect, and largest p-value for L* and H° pod exterior was identified on Pv01, Pv02, Pv03, and Pv08. More SNPs were associated with pod traits than with leaf traits. The pod interior did not exhibit colors produced by anthocyanins or flavonols which allowed the differentiation of potential candidate genes associated with chloroplast and photosynthetic activity compared to the pod exterior where candidate genes related to both flavonoids and photosynthesis affected color. Several SNPs were associated with known qualitative genes including the wax pod locus (y), persistent color (pc), purple pods (V), and two genes expressed in seeds but not previously reported to affect other plant tissues (B and J). An evaluation of significant SNPs within annotated genes found a number, within a 200 kb window, involved in both flavonoid and photosynthetic biosynthetic pathways.

Targeted improvement of plant‐based protein: Genome‐wide association mapping of a lentil (Lens culinaris Medik.) diversity panel

Societal Impact StatementThe world is increasingly looking to plant‐based sources to meet its protein needs. Multiple factors are driving this progression, ranging from nutritional and ethical considerations to climate change and population growth. As a pulse crop, lentil is ideal to help meet this change in demand. However, plant‐based proteins have limiting amino acids and lower protein digestibility compared to animal‐based proteins. This research identifies genetic markers that can be used to accelerate breeding of protein quality traits in lentil to ultimately help meet the rising demand in high‐quality plant‐based protein and bolster global food and nutritional security.Summary Lentil (Lens culinaris Medik.) contains ~25% high‐quality protein in addition to high concentrations of prebiotic carbohydrates and micronutrients, such as folate, iron, zinc, and selenium. As animal‐based protein's economic and environmental costs rise, plant‐based protein sources, such as lentil, will become increasingly important to global food systems. Consequently, evaluating and targeting protein quality traits for genomic‐assisted breeding is a valuable objective for lentil breeding programs. A diversity panel of 183 breeding lines was analyzed for protein quality traits, including amino acids and protein digestibility. Genotyping‐by‐sequencing (GBS) data were used to assess population structure and conduct genome‐wide association studies (GWAS). Genes in local linkage disequilibrium (LD) with significant single nucleotide polymorphism (SNP) markers were identified and categorized by homology. Protein quality traits showed a wide range of variation. Repeatability estimates were low to moderate across traits. Twelve traits were strongly correlated with each other (r &gt; .7). Admixture analysis identified six ancestral subpopulations, which also demonstrated clustering in principal component analysis. Ten different traits had significant SNP associations; two loci were shared across multiple traits. Twenty‐seven candidate genes, including glutathione S‐transferase, protease family, and gibberellin 2‐beta‐dioxygenase genes, were identified. This paper identifies SNP markers associated with lentil protein quality traits. Once validated, these SNPs could accelerate lentil protein quality breeding efforts. By targeting lentil's limiting amino acids (methionine and cysteine) and protein digestibility through marker‐assisted selection, the nutritional value of lentil's protein content could be increased without the need to alter total protein content.

Genome-Wide Association Analysis-Based Mining of Quality Genes Related to Linoleic and Linolenic Acids in Soybean

Soybean fat contains five principal fatty acids, and its fatty acid composition and nutritional value depend on the type of soybean oil, storage duration, and conditions. Among the fat contents, polyunsaturated fatty acids, such as linoleic acid and linolenic acid, play an essential role in maintaining human life activities; thus, increasing the proportions of the linoleic acid and linolenic acid contents can help improve the nutritional value of soybean oil. Our laboratory completed SLAF-seq whole genome sequencing of the natural population (292 soybean varieties) in the previous growth period. In this study, genome-wide association analysis (GWAS) was performed based on the natural population genotypic data and three-year phenotypic data of soybean linoleic acid and linolenic acid contents, and a significant single nucleotide polymorphisms (SNPs) locus (Gm13_10009679) associated with soybean oleic acid content was repeatedly detected over a span of 3 years using the GLM model and MLM model. Additionally, another significant SNP locus (Gm19_41366844) correlated with soybean linolenic acid was identified through the same models. Genes within the 100 Kb interval upstream and downstream of the SNP loci were scanned and analyzed for their functional annotation and enrichment, and one gene related to soybean linoleic acid synthesis (Glyma.13G035600) and one gene related to linolenic acid synthesis (Glyma.19G147400) were screened. The expressions of the candidate genes were verified using qRT-PCR, and based on the verification results, it was hypothesized that Glyma.13G035600 and Glyma.19G147400 positively regulate linoleic acid and linolenic acid synthesis and accumulation, respectively. The above study lays the foundation for further validating gene functions, and analyzing the regulatory mechanisms of linoleic acid and linolenic acid synthesis and accumulation in soybean.

Genome-Wide Association Studies and Runs of Homozygosity to Identify Reproduction-Related Genes in Yorkshire Pig Population.

Reproductive traits hold considerable economic importance in pig breeding and production. However, candidate genes underpinning the reproductive traits are still poorly identified. In the present study, we executed a genome-wide association study (GWAS) and runs of homozygosity (ROH) analysis using the PorcineSNP50 BeadChip array for 585 Yorkshire pigs. Results from the GWAS identified two genome-wide significant and eighteen suggestive significant single nucleotide polymorphisms (SNPs) associated with seven reproductive traits. Furthermore, we identified candidate genes, including ELMO1, AOAH, INSIG2, NUP205, LYPLAL1, RPL34, LIPH, RNF7, GRK7, ETV5, FYN, and SLC30A5, which were chosen due to adjoining significant SNPs and their functions in immunity, fertilization, embryonic development, and sperm quality. Several genes were found in ROH islands associated with spermatozoa, development of the fetus, mature eggs, and litter size, including INSL6, TAF4B, E2F7, RTL1, CDKN1C, and GDF9. This study will provide insight into the genetic basis for pig reproductive traits, facilitating reproduction improvement using the marker-based selection methods.

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize.

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population.

Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.

Delineation of novel genomic loci and putative candidate genes associated with seed iron and zinc content in lentil (Lens culinaris Medik.)

The use of molecular breeding approaches for development of lentil genotypes biofortified with essential micro-nutrients such as iron and zinc, could serve as a promising solution to address the problem of global malnutrition. Thus, genome-wide association study (GWAS) strategy was adopted in this study to identify the genomic regions associated with seed iron and zinc content in lentil. A panel of 95 diverse lentil genotypes, grown across three different geographical locations and evaluated for seed iron and zinc content, exhibited a wide range of variation. Genotyping-by-sequencing (GBS) analysis of the panel identified 33,745 significant single nucleotide polymorphisms (SNPs) that were distributed across all the 7 lentil chromosomes. Association mapping revealed 23 SNPs associated with seed iron content that were distributed across all the chromosomes except chromosome 3. Similarly, 14 SNPs associated with seed zinc content were also identified that were distributed across chromosomes 1, 2, 4, 5 and 6. Further, 80 genes were identified in the proximity of iron associated markers and 36 genes were identified in the proximity of zinc associated markers. Functional annotation of these genes revealed their putative involvement in iron and zinc metabolism. For seed iron content, two highly significant SNPs were found to be located within two putative candidate genes namely iron-sulfur cluster assembly (ISCA) and flavin binding monooxygenase (FMO) respectively. For zinc content, a highly significant SNP was detected in a gene encoding UPF0678 fatty acid-binding protein. Expression analysis of these genes and their putative interacting partners suggests their involvement in iron and zinc metabolism in lentil. Overall, in this study we have identified markers, putative candidate genes and predicted putative interacting protein partners significantly associated with iron and zinc metabolism that could be utilized in future breeding studies of lentil for nutrient biofortification.