Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

The Risk of Organ Specific Graft-Versus-Host Disease Can Be Predicted by the Multiple Single Nucleotide Polymorphism Based Predictive Models.

Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers are amongst the most common markers of choice for studies of diversity and relationships in horticultural species. We have used 11 SSR and 35 SNP markers derived from transcriptome sequencing projects to fingerprint 48 accessions of a collection of brinjal (Solanum melongena), gboma (S. macrocarpon) and scarlet (S. aethiopicum) eggplant complexes, which also include their respective wild relatives S. incanum, S. dasyphyllum and S. anguivi. All SSR and SNP markers were polymorphic and 34 and 36 different genetic fingerprints were obtained with SSRs and SNPs, respectively. When combining both markers all accessions but two had different genetic profiles. Although on average SSRs were more informative than SNPs, with a higher number of alleles, genotypes and polymorphic information content (PIC), and expected heterozygosity (H e ) values, SNPs have proved highly informative in our materials. Low observed heterozygosity (H o ) and high fixation index (f) values confirm the high degree of homozygosity of eggplants. Genetic identities within groups of each complex were higher than with groups of other complexes, although differences in the ranks of genetic identity values among groups were observed between SSR and SNP markers. For low and intermediate values of pair-wise SNP genetic distances, a moderate correlation between SSR and SNP genetic distances was observed (r2 = 0.592), but for high SNP genetic distances the correlation was low (r2 = 0.080). The differences among markers resulted in different phenogram topologies, with a different eggplant complex being basal (gboma eggplant for SSRs and brinjal eggplant for SNPs) to the two others. Overall the results reveal that both types of markers are complementary for eggplant fingerprinting and that interpretation of relationships among groups may be greatly affected by the type of marker used.

Hundreds of common genetic variants related to the risk of human disease, such as diabetes, hypertension, bipolar, and Crohn’s disease, have been successfully discovered by Genomewide Association Studies (GWAS) (Barret et al., 2008; Hindorff, 2009; Thomas et al., 1991; WTCCC, 2007). Current GWAS are based on the strategy of linkage disequilibrium (LD) mapping, in which a sufficient number of single nucleotide polymorphism (SNP) markers are selectively genotyped to capture the genetic variation of the whole genome. However, there are two major issues related to the results of GWAS. First, the results only explain a small fraction of the heritability of complex diseases. One of the reasons may be that many functional variants, in particular rare variants, which are not directly genotyped in GWAS, have a weak LD with SNP markers, and hence are missed by GWAS (Iyengar et al., 2004; Manolio et al., 2009). Second, the identified associations in GWAS are often inconsistent between different populations. The reason for this may be the varied LD structures between markers and underlying causal variants among populations, resulting in associations can only be observed in specific populations.

Capsinoids have similar biological effects as capsaicinoids, including anticancer and anti-obesity properties. The Capsicum chinense ‘SNU11-001’ variety of pepper was previously reported to contain high levels of capsinoids due to a mutation in the putative aminotransferase (pAMT) gene, which leads to production of the capsinoid precursor vanillyl alcohol. Here, to develop Capsicum annuum pepper varieties with high capsinoid contents, marker-assisted backcrossing was performed during backcross breeding. For foreground selection, plants carrying the pAMT/pamt genotype were selected from BC1F1 and BC2F1 populations using SCAR markers derived from the unique pamt mutation of ‘SNU11-001.’ To obtain background selection markers, 412 single nucleotide polymorphism (SNP) markers were screened to identify SNP markers polymorphic between the ‘Shinhong’ paternal lines and ‘SNU11-001.’ Of the 412 SNP markers, 204 polymorphic SNP markers evenly distributed in pepper genome were selected. BC1F1 and BC2F1 plants carrying the pAMT/pamt genotype were subjected to background selection using these SNP markers. Multiple genotypes were analyzed using a high-throughput genotyping system. As a result, one BC1F1 plant 84 % similar to the recurrent parent and seven BC2F1 plants showing more than 96 % recovery of the recurrent parent genotype were selected. Genetic backgrounds of the selected BC2F1 plants were evaluated using the genotype-by-sequencing (GBS) method to confirm the background selection results from the SNP marker set. GBS results showed that the recovery rate and positions of introgressed segments were well matched between two methods, demonstrating MABC can be successfully performed with only ~200 SNP markers.

Soybean cyst nematode (SCN, Heterodera glycines) has become the major yield-limiting biological factor in soybean production. Common bean is also a good host of SCN, and its production is challenged by this emerging pest in many regions such as the upper Midwest USA. The use of host genetic resistance has been the most effective and environmentally friendly method to manage SCN. The objectives of this study were to evaluate the SCN resistance in the USDA common bean core collection and conduct a genome-wide association study (GWAS) of single nucleotide polymorphism (SNP) markers with SCN resistance. A total of 315 accessions of the USDA common bean core collection were evaluated for resistance to SCN HG Type 0 (race 6). The common bean core set was genotyped with the BARCBean6K_3 Infinium BeadChips, consisting of 4,654 SNPs. Results showed that 15 accessions were resistant to SCN with a Female Index (FI) at 4.8 to 9.4, and 62 accessions were moderately resistant (10 < FI < 30) to HG Type 0. The association study showed that 11 SNP markers, located on chromosomes Pv04, 07, 09, and 11, were strongly associated with resistance to HG Type 0. GWAS was also conducted for resistance to HG Type 2.5.7 and HG Type 1.2.3.5.6.7 based on the public dataset (N = 276), consisting of a diverse set of common bean accessions genotyped with the BARCBean6K_3 chip. Six SNPs associated with HG Type 2.5.7 resistance on Pv 01, 02, 03, and 07, and 12 SNPs with HG Type 1.2.3.5.6.7 resistance on Pv 01, 03, 06, 07, 09, 10, and 11 were detected. The accuracy of genomic prediction (GP) was 0.36 to 0.49 for resistance to the three SCN HG types, indicating that genomic selection (GS) of SCN resistance is feasible. This study provides basic information for developing SCN-resistant common bean cultivars, using the USDA core germ plasm accessions. The SNP markers can be used in molecular breeding in common beans through marker-assisted selection (MAS) and GS.

ABSTRACTWe examined the number, genomic coverage, and discrimination abilities of single nucleotide polymorphism (SNP) markers required to provide equivalent measures of genetic distance compared to previously assayed simple sequence repeat (SSR) loci among inbred lines of maize (Zea mays L). A diverse set of public inbred lines was profiled with 768 public SNP markers and subsets of 601, 446, 305, 204, 165, 83, and 42 SNP markers were selected based on maintenance of expected heterozygosity (He) and/or even genomic coverage. The effectiveness of each SNP marker set was evaluated by comparison with standard SSR marker sets and pedigree distance values. Each marker set was evaluated using the full set of data for 80 diverse public inbred lines, a subset of inbred lines selected to represent the diversity of the full range of inbreds, and a subset of more closely related Stiff Stalk Synthetic (SSS) inbreds. A set of 305 SNP markers with mean He of greater than 0.4 had comparable or lower genetic distance sampling variance compared with SSR markers and resulted in r values comparable to larger SNP sets when pair‐wise Roger's distances were correlated with distance estimates generated from pedigree or SSR data. We found that, where SNP markers are selected to maintain high He and even genome coverage, then data from only 2 to 3 times the number of SNP markers are needed to reveal associations among lines compared with SSR markers.

Flowering time is an important developmental stage in plants, influenced by multiple genes and environmental factors. Understanding its genetic basis and interaction with the environment facilitates the development of improved varieties adapted to different environments. Conventional Genome-Wide Association Studies (GWAS) have been widely used to associate genetic markers with heritable traits, but they do not inherently capture interactions among single nucleotide polymorphisms (SNPs) or between SNPs and the environment. Machine Learning (ML) approaches can model these interactions and improve trait prediction even in the presence of noise and missing data. In this study, multi-environment lentil (Lens culinaris Medik.) data were analysed using GWAS and two widely used ML models, Random Forest and XGBoost, to identify genetic markers associated with flowering time. Model interpretability was enhanced using Explainable AI (XAI) techniques, including SHapley Additive exPlanations. GWAS identified eight significant loci across chromosomes one, two, five and seven, with the most significant SNP located at Chr2_530433205, while ML approaches identified nine markers on chromosomes one, two, three, five and seven, with the most significant SNP at Chr7_523220088. The majority of the identified markers were linked to candidate genes for flowering, while ML also identified potential epistasis. These findings highlight ML as a powerful complementary tool to GWAS for trait association.

Stored grains of common bean (Phaseolus vulgaris L.) develop the hard‐to‐cook trait (HTC), which is manifested in a prolonged cooking time, thereby imposing time and energy constraints. The objective of this study was to determine variation in cooking time among common bean genotypes and to identify single nucleotide polymorphism (SNP) markers associated with cooking time.Seeds of 222 common bean accessions sourced from Kenyan institutions were multiplied in the Jomo Kenyatta University of Agriculture and Technology (JKUAT) field in 2019. The freshly harvested seeds and those stored at 35°C and 50% red haricot (RH) for 4 months for accelerated aging were soaked in distilled water for 16 h and evaluated for cooking time using the finger‐pressing method. The accessions were also genotyped to determine variation in SNP markers using Diversity Arrays Technology Sequencing (DArTseq). Genome‐wide association study (GWAS) analysis was conducted to identify SNPs significantly associated with cooking time.The study revealed significant differences (p ≤ 0.05) within and between fresh and aged bean accessions. Fresh seeds had a lower cooking time with a mean of 40.8 min and ranged from 28.1 to 72.2 min, whereas aged seeds had a higher average cooking time of 54.1 min and ranged from 32.1 to 96.3 min. GWAS identified a region in Chromosome 10 to be significantly (p ≤ 0.05) associated with the cooking time of aged seeds. Consequently, two potential candidate genes Phvul.010G038000 and Phvul.010G038100 were revealed. The characterized common bean accessions and the identified SNP markers can be utilized in breeding programs to improve the cooking quality of the common bean.

High-throughput genotyping arrays provide a standardized resource for plant breeding communities that are useful for a breadth of applications including high-density genetic mapping, genome-wide association studies (GWAS), genomic selection (GS), complex trait dissection, and studying patterns of genomic diversity among cultivars and wild accessions. We have developed the CottonSNP63K, an Illumina Infinium array containing assays for 45,104 putative intraspecific single nucleotide polymorphism (SNP) markers for use within the cultivated cotton species Gossypium hirsutum L. and 17,954 putative interspecific SNP markers for use with crosses of other cotton species with G. hirsutum. The SNPs on the array were developed from 13 different discovery sets that represent a diverse range of G. hirsutum germplasm and five other species: G. barbadense L., G. tomentosum Nuttal × Seemann, G. mustelinum Miers × Watt, G. armourianum Kearny, and G. longicalyx J.B. Hutchinson and Lee. The array was validated with 1,156 samples to generate cluster positions to facilitate automated analysis of 38,822 polymorphic markers. Two high-density genetic maps containing a total of 22,829 SNPs were generated for two F2 mapping populations, one intraspecific and one interspecific, and 3,533 SNP markers were co-occurring in both maps. The produced intraspecific genetic map is the first saturated map that associates into 26 linkage groups corresponding to the number of cotton chromosomes for a cross between two G. hirsutum lines. The linkage maps were shown to have high levels of collinearity to the JGI G. raimondii Ulbrich reference genome sequence. The CottonSNP63K array, cluster file and associated marker sequences constitute a major new resource for the global cotton research community.

Genome-wide association studies (GWAS) have successfully identified many genetic variants that are associated with many complex traits. For example, GWAS can be useful for understanding the genetic basis of physical activity (PA). To date, however, there have been only a few GWAS regarding PA. In this article, we overview some practical issues for more efficient GWAS for PA: phenotype definition of PA, the analysis method, population stratification, replication, and sample size. We discuss these issues within a large-scale GWA data set from the Korea Association REsource (KARE) project, including 8,842 samples and 352,228 single nucleotide polymorphism (SNP) markers. Information on PA was obtained from questionnaires, and GWA analysis was performed to find genetic associations between PA and SNP markers in the Korean population.

Scots pine (Pinus silvestris), is one of the dominant species in Poland and one of the main forest tree species in northern and central Europe. This species is of great economic importance. The Scots pine is highly adaptable to changing environmental conditions. A number of ecotypes have been characterized and the formation of these ecotypes are related with development of different phenotypic characteristics; morphological, physiological, and ecological. Molecular studies, based on DNA polymorphism, have been used for more than 20 years to analyze the genetic diversity of the Scots pine population. The most popular are microsatellite markers due to the fact of wide availability and high polymorphism. However, the use of these markers is also associated with certain limitations, due to complex mutation models or high incidence of homoplasia. These features are prompting scientists to look for alternative types of markers such as, for example, SNP. In our study we conducted a comparison of the basic parameters of genetic variability of two Scots pine stands (25 and 24 trees in each) for 20 SNP markers and 4 microsatellite markers. For the 20 SNP loci the observed heterozygosity (Ho) was equal to 0.34 for both stands and the expected heterozygosity (He) was equal to 0.34 for the first stand and 0.37 for the second. No statistically significant genetic distance was observed between them. For the microsatellite markers observed heterozygosity (Ho) was 0.81 and 0.74, and the expected heterozygosity (He) was equal to 0.85 and 0.85 respectively for the stands, and similarly no statistical significant genetic distance was observed. Literature data of different genetic markers showed the higher informativeness of randomly chosen microsatellites than single nucleotide polymorphism (SNP) markers for study population differentiation. But some analyzes confirm that the appropriate number of SNP markers can be more informative for population structure inference.

Joint genome-wide association study for milk fatty acid traits in Chinese and Danish Holstein populations

An F2 population was developed from a cross between a mur-cytoplasmic male sterile broccoli line and a restorer Chinese kale line. Phenotypic analysis of F2 plants indicated that the pollen fertility is controlled by two genes and segregated in a duplicate gene interaction mode with a ratio of 15:1. A total of 236 single nucleotide polymorphism (SNP) markers were developed utilizing 1,448 primers designed for production of expressed sequence tag (EST)-SNP markers of Raphanus sativus and analyzed by the dot-blot technique in 205 F2 individuals. A linkage map was constructed with a total of 142 markers and these markers were assigned to nine linkage groups together with simple sequence repeat markers mapped previously on the published linkage maps of Brassica oleracea. The linkage map spanned 909 cM with an average marker distance of 6.4 cM. A fertility restorer locus (Rfm1) was mapped on LG1, corresponding to chromosome 3, along with a flower color locus at a distance of 25 cM. SNP markers flanking the Rfm1 locus were BoCL2642s at a distance of 2.5 cM on one side and BoCL2901s at a distance of 7.5 cM on the other side. All the SNP markers showed homology with Arabidopsis thaliana and Brassica rapa genome sequences. Three pentatricopeptide repeat genes of the P-subfamily, particularly expressed in buds of the restorer line, were identified and these genes could be potential candidate fertility restorer genes.

Cytoplasmic male sterility (CMS), which is a maternally inherited trait and controlled by novel chimeric genes in the mitochondrial genome, plays a pivotal role in the production of hybrid seed. In cotton, no PCR-based marker has been developed to discriminate CMS-D8 (from Gossypium trilobum) from its normal Upland cotton (AD1, Gossypium hirsutum) cytoplasm. The objective of the current study was to develop PCR-based single nucleotide polymorphic (SNP) markers from mitochondrial genes for the CMS-D8 cytoplasm. DNA sequence variation in mitochondrial genes involved in the oxidative phosphorylation chain including ATP synthase subunit 1, 4, 6, 8 and 9, and cytochrome c oxidase 1, 2 and 3 subunits were identified by comparing CMS-D8, its isogenic maintainer and restorer lines on the same nuclear genetic background. An allelic specific PCR (AS-PCR) was utilized for SNP typing by incorporating artificial mismatched nucleotides into the third or fourth base from the 3' terminus in both the specific and nonspecific primers. The result indicated that the method modifying allele-specific primers was successful in obtaining eight SNP markers out of eight SNPs using eight primer pairs to discriminate two alleles between AD1 and CMS-D8 cytoplasms. Two of the SNPs for atp1 and cox1 could also be used in combination to discriminate between CMS-D8 and CMS-D2 cytoplasms. Additionally, a PCR-based marker from a nine nucleotide insertion-deletion (InDel) sequence (AATTGTTTT) at the 59-67 bp positions from the start codon of atp6, which is present in the CMS and restorer lines with the D8 cytoplasm but absent in the maintainer line with the AD1 cytoplasm, was also developed. A SNP marker for two nucleotide substitutions (AA in AD1 cytoplasm to CT in CMS-D8 cytoplasm) in the intron (1,506 bp) of cox2 gene was also developed. These PCR-based SNP markers should be useful in discriminating CMS-D8 and AD1 cytoplasms, or those with CMS-D2 cytoplasm as a rapid, simple, inexpensive, and reliable genotyping tool to assist hybrid cotton breeding.

Fruit acidity is a major determinant of consumer-perceived fruit quality in peach. The non-acid peaches are preferred in the market and this trait is usually selected in commercial breeding programs. A major gene (D/d) located on chromosome 5 of peach has been described for this character, where the non-acid character is determined by the dominant D allele. The corresponding physical location estimated through linkage analysis is 0.9 Mb of the chromosome 5. In our study, we identified a SNP from re-sequencing 129 varieties through genome-wide association studies, and then genotyped a collection of 436 varieties on the Sequenom MassArray platform to verify the single nucleotide polymorphism (SNP) marker for this character. Two hundred and twelve accessions from 224 accessions tasting acid are TT. Two hundred accessions from 212 accessions tasting non-acid are TC or CC. The accuracy rate was 94.5 %. Based on the SNP marker, we also designed primers used for PCR to identify non-acid peaches. This approach was validated on 169 cultivars and 84 offspring. Data were always consistent and 160 cultivars (94.7 %) and 80 offspring (95.2 %) fit the expectations. These three methods of selecting non-acid plants are convenient and readily useful for peach molecular assisted selection.