Adjacent Single Nucleotide Polymorphisms Research Articles

Improving DNA mixtures analysis using compound markers composed of InDels and SNPs screened from the whole genome with next-generation sequencing.

Next-generation sequencing (NGS) allows for better identification of insertion and deletion polymorphisms (InDels) and their combination with adjacent single nucleotide polymorphisms (SNPs) to form compound markers. These markers can improve the polymorphism of microhaplotypes (MHs) within the same length range, and thus, boost the efficiency of DNA mixture analysis. In this study, we screened InDels and SNPs across the whole genome and selected highly polymorphic markers composed of InDels and/or SNPs within 300bp. Further, we successfully developed and evaluated an NGS-based panel comprising 55 loci, of which 24 were composed of both SNPs and InDels. Analysis of 124 unrelated Southern Han Chinese revealed an average effective number of alleles (Ae ) of 7.52 for this panel. The cumulative power of discrimination and cumulative probability of exclusion values of the 55 loci were 1-2.37×10-73 and 1-1.19×10-28 , respectively. Additionally, this panel exhibited high allele detection rates of over 97% in each of the 21 artificial mixtures involving from two to six contributors at different mixing ratios. We used EuroForMix to calculate the likelihood ratio (LR) and evaluate the evidence strength provided by this panel, and it could assess evidence strength with LR, distinguishing real and noncontributors. In conclusion, our panel holds great potential for detecting and analyzing DNA mixtures in forensic applications, with the capability to enhance routine mixture analysis.

Development of a novel panel for blood identification based on blood-specific CpG-linked SNP markers.

Blood-containing mixtures often appear in murder and robbery cases, and their identification plays a significant role in solving crimes. In recent years, the co-detection of DNA methylation markers (CpG) and single nucleotide polymorphism (SNP) markers has been shown to be a promising tool for the identification of semen and its donor. However, similar research on blood stains that are frequently found at crime scenes has not yet been reported. In this study, we employed blood-specific CpG-linked SNP markers (CpG-SNP) for blood-specific genotyping and the linking of blood and its donor. The tissue-specific CpG markers were screened from the literature and further verified by combining bisulfite conversion with amplification-refractory mutation system (ARMS) technology. Meanwhile, adjacent SNP markers with a minor allele frequency (MAF) greater than 0.1 were selected within 400 bp upstream and downstream of the CpG markers. SNP genotyping was performed using SNaPshot technology on a capillary electrophoresis (CE) platform. Finally, a multiplex panel, including 19 blood-specific CpG linked to 23 SNP markers, as well as 1 semen-specific CpG, 1 vaginal secretion-specific CpG, and 1 saliva-specific CpG marker, was constructed successfully. The panel showed good tissue specificity and blood stains stored at room temperature for up to nine months and moderately degraded (4 < DI < 10) could be effectively identified. Moreover, it could also be detected when blood content in the mixed stains was as low as 1%. In addition, 15 ng of DNA used for bisulfite conversion was required for obtaining a complete profile. The cumulative discrimination power of the panel among the Han population of northern China could reach 0.999983. This is the first investigation conducted for the simultaneous identification of blood and its donor regardless of other body fluids included in mixed stains. The successful construction of the panel will play a vital role in the comprehensive analysis of blood-containing mixtures in forensic practice.

Linkage disequilibrium, historical effective population size and haplotype blocks in selected native sheep breeds

This study was conducted to investigate linkage disequilibrium (LD), historical effective population size (Ne) and haplotype blocks structure in genomes of nine Polish conserved sheep breeds or varieties: Black-headed (BH), Colored Mountain Sheep (CMS), Old-type Merino (MPOT), Podhale Zackel (PZ), Polish Merino of Coloured Variety (MPC), Polish Mountain Sheep (PMS), Świniarka (SW), Uhruska (UHR) and Wrzosówka (WRZ), based on single nucleotide polymorphisms (SNPs) from the OvineSNP50 DNA Analysis Kit (Illumina, San Diego, CA). LD analysis was performed based on the pairwise r2 statistic of SNPs at a distance up to 5 Mb. At a short distance, up to 100 kb, the lower average r2 was observed in PZ (0.10) breed and the highest average r2 was observed in MPC (0.21) breed. At a distance of 4–5 Mb, the highest average r2 was observed in SW (0.08) breed and the lowest average r2 was observed in PZ (0.02) and PMS (0.02) breeds. All studied Polish sheep breeds, showed mean r2 > 0.3 (‘useful’ LD level for QTL mapping) at shorter distances than average distances between adjacent SNP markers and only for MPC, SW, UH, WRZ breeds mean r2 > 0.2 (high enough to attain an accuracy of 0.85 for genomic breeding value estimation) was observed at similar or longer distances than average distances between adjacent SNP markers. Obtained results showed that the Illumina OvineSNP50 BeadChip assay is not a suitable tool to perform reliable genome-wide association for all studied Polish sheep breeds and only for MPC, SW, UH and WRZ it is potentially possible to perform reliable genomic breeding value estimation. For most of the studied sheep breeds, effective population size 5 generations ago was close or less to the FAO recommended Ne of 50 per generation required to maintain fitness in breed. From 2,051 (SW) to 1,265 (PZ) haplotype block structures, spanning from 148.9 (SW) to 26.3 (PZ) of the genome were detected. Across all studied sheep breeds the highest average block length was observed in MPC (82.5 ± 114 kb) breed and the lowest average block length was observed in PZ (20.9 ± 43 kb) breed. For all studied Polish sheep breeds, as many as 280 haplotype block structures with overlapping segments were detected, within which 182 genes were identified.

Feet and legs malformation in Nellore cattle: genetic analysis and prioritization of GWAS results.

Beef cattle affected by feet and legs malformations (FLM) cannot perform their productive and reproductive functions satisfactorily, resulting in significant economic losses. Accelerated weight gain in young animals due to increased fat deposition can lead to ligaments, tendon and joint strain and promote gene expression patterns that lead to changes in the normal architecture of the feet and legs. The possible correlated response in the FLM due to yearling weight (YW) selection suggest that this second trait could be used as an indirect selection criterion. Therefore, FLM breeding values and the genetic correlation between FLM and yearling weight (YW) were estimated for 295,031 Nellore animals by fitting a linear-threshold model in a Bayesian approach. A genome-wide association study was performed to identify genomic windows and positional candidate genes associated with FLM. The effects of single nucleotide polymorphisms (SNPs) on FLM phenotypes (affected or unaffected) were estimated using the weighted single-step genomic BLUP method, based on genotypes of 12,537 animals for 461,057 SNPs. Twelve non-overlapping windows of 20 adjacent SNPs explaining more than 1% of the additive genetic variance were selected for candidate gene annotation. Functional and gene prioritization analysis of candidate genes identified six genes (ATG7, EXT1, ITGA1, PPARD, SCUBE3, and SHOX) that may play a role in FLM expression due to their known role in skeletal muscle development, aberrant bone growth, lipid metabolism, intramuscular fat deposition and skeletogenesis. Identifying genes linked to foot and leg malformations enables selective breeding for healthier herds by reducing the occurrence of these conditions. Genetic markers can be used to develop tests that identify carriers of these mutations, assisting breeders in making informed breeding decisions to minimize the incidence of malformations in future generations, resulting in greater productivity and animal welfare.

Linkage disequilibrium and effective population size in Indian goat breeds

Genomic linkage disequilibrium (LD) is an important population diversity measure and an indicator determining the efficacy of genomic applications. The present population genomic study was carried out to comprehensively characterise the extent and pattern of LD in the two divergent Indian goat breeds namely, Attappady Black and Malabari goats reared under small holder production system. The LD was estimated using genome-wide single nucleotide polymorphism (SNP) marker data obtained by Illumina SNP50 goat BeadChip genotyping. The LD between adjacent SNPs across all autosomes measured as correlation coefficient (r2) in the studied population was low and mean ± standard deviation of LD were 0.0657 ± 0.0921 and 0.0661 ± 0.0911 for Attappady Black and Malabari goats, respectively. The LD varied significantly (p < 0.0001) among chromosomes, interaction between chromosome and breed and among marker intervals. The LD (r2) of 0.2 and 0.3 were extended up to 20 kb and 10 kb marker intervals, respectively. Based on the genomic data, Ne was estimated as 512.8198 in Malabari and 371.2220 in Attappady Black goats at 17 generations back. The weak LD observed in Indian goat breeds indicates increased diversity of these Indian goat breeds and also implies that denser SNP panel might be more beneficial for genomic applications.

Genome-wide study of linkage disequilibrium, population structure, and inbreeding in Iranian indigenous sheep breeds.

Knowledge of linkage disequilibrium (LD), genetic structure and genetic diversity are some key parameters to study the breeding history of indigenous small ruminants. In this study, the OvineSNP50 Bead Chip array was used to estimate and compare LD, genetic diversity, effective population size (Ne) and genomic inbreeding in 186 individuals, from three Iranian indigenous sheep breeds consisting of Baluchi (n = 96), Lori-Bakhtiari (n = 47) and Zel (n = 47). The results of principal component analysis (PCA) revealed that all animals were allocated to the groups that they sampled and the admixture analysis revealed that the structure within the populations is best explained when separated into three groups (K = 3). The average r2 values estimated between adjacent single nucleotide polymorphisms (SNPs) at distances up to 10Kb, were 0.388±0.324, 0.353±0.311, and 0.333±0.309 for Baluchi, Lori-Bakhtiari and Zel, respectively. Estimation of genetic diversity and effective population size (Ne) showed that the Zel breed had the highest heterozygosity and Ne, whereas the lowest value was found in Baluchi breed. Estimation of genomic inbreeding using FROH (based on the long stretches of consecutive homozygous genotypes) showed the highest inbreeding coefficient in Baluchi and the lowest in Zel breed that could be due to higher pressure of artificial selection on Baluchi breed. The results of genomic inbreeding and Ne showed an increase in sharing haplotypes in Baluchi, leading to the enlargement of LD and the consequences of linkage disequilibrium and haplotype blocks confirmed this point. Also, the persistence of the LD phase between Zel and Lori-Bakhtiari was highest indicating that these two breeds would be combined in a multi-breed training population in genomic selection studies.

Population analysis reveals the roles of DNA methylation in tomato domestication and metabolic diversity.

DNA methylation is an important epigenetic marker, yet its diversity and consequences in tomato breeding at the population level are largely unknown. We performed whole-genome bisulfite sequencing (WGBS), RNA sequencing, and metabolic profiling on a population comprising wild tomatoes, landraces, and cultivars. A total of 8,375 differentially methylated regions (DMRs) were identified, with methylation levels progressively decreasing from domestication to improvement. We found that over 20% of DMRs overlapped with selective sweeps. Moreover, more than 80% of DMRs in tomato were not significantly associated with single-nucleotide polymorphisms (SNPs), and DMRs had strong linkages with adjacent SNPs. We additionally profiled 339 metabolites from 364 diverse accessions and further performed a metabolic association study based on SNPs and DMRs. We detected 971 and 711 large-effect loci via SNP and DMR markers, respectively. Combined with multi-omics, we identified 13 candidate genes and updated the polyphenol biosynthetic pathway. Our results showed that DNA methylation variants could complement SNP profiling of metabolite diversity. Our study thus provides a DNA methylome map across diverse accessions and suggests that DNA methylation variation can be the genetic basis of metabolic diversity in plants.

Rice3K56 is a high-quality SNP array for genome-based genetic studies and breeding in rice (Oryza sativa L.)

Single nucleotide polymorphism (SNP) genotyping arrays provide an optimal high-throughput platform for genetic research and molecular breeding programs in both animals and plants. In this study, a high-quality and custom-designed Rice3K56 SNP array was developed with the resequencing data of 3024 rice accessions worldwide, which was then tested extensively in 192 representative rice samples. Printed on the GeneTitan chips of Affymetrix Axiom each containing 56,606 SNP markers, the Rice3K56 array has a high genotyping reliability (99.6%), high and uniform genome coverage (an average of 6.7-kb between adjacent SNPs), abundant polymorphic information and easy automation, compared with previously developed rice SNP arrays. When applied in rice varietal differentiation, population diversity analysis, gene mapping of 13 complex traits by a genome-wide association study analysis (GWAS), and genome selection experiments in a recombinant inbred line and a multi-parent advanced generation inter-cross populations, these properties of the Rice3K56 array were well demonstrated for its power and great potential to be a highly efficient tool for rice genetic research and genomic breeding.

Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice.

Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700K high-density rice array, Affymetrix 44K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580K Axiom Rice Genotyping Chip (580K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log10 p-value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.

Feature Compression Applications of Genetic Algorithm.

With the rapid development of molecular breeding technology and many new varieties breeding, a method is urgently needed to identify different varieties accurately and quickly. Using this method can not only help farmers feel convenient and efficient in the normal cultivation and breeding process but also protect the interests of breeders, producers and users. In this study, single nucleotide polymorphism (SNP) data of 533 Oryza sativa, 284 Solanum tuberosum and 247 Sus scrofa and 544 Manihot esculenta Crantz were used. The original SNPs were filtered and screened to remove the SNPs with deletion number more than 1% or the homozygous genotype 0/0 and 1/1 number less than 2. The correlation between SNPs were calculated, and the two adjacent SNPs with correlation R2 > 0.95 were retained. The genetic algorithm program was developed to convert the genotype format and randomly combine SNPs to calculate a set of a small number of SNPs which could distinguish all varieties in different species as fingerprint data, using Matlab platform. The successful construction of three sets of fingerprints showed that the method developed in this study was effective in animals and plants. The population structure analysis showed that the genetic algorithm could effectively obtain the core SNPs for constructing fingerprints, and the fingerprint was practical and effective. At present, the two-dimensional code of Manihot esculenta Crantz fingerprint obtained by this method has been applied to field planting. This study provides a novel idea for the Oryza sativa, Solanum tuberosum, Sus scrofa and Manihot esculenta Crantz identification of various species, lays foundation for the cultivation and identification of new varieties, and provides theoretical significance for many other species fingerprints construction.

Genome-wide association study for milk production traits in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic regions associated with milk production traits during the first three parities in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 135,646 test-day milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP), and somatic cell count (SCC) collected from 1988 to 2020 on 20,744 lactations obtained from 10,345 cows in 128 herds. Data of 28,466 single nucleotide polymorphisms (SNP), located on 29 Bos taurus (BTA) autosomes, for 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single‐step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 adjacent SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0 % of the total additive genetic variance were used to search for candidate genes. Mean heritability (h2) estimated for milk yield traits (MY, FY, PY, FP, and PP) ranged from 0.32 (FP) to 0.41 (PP), 0.30 (FP) to 0.41 (PP), and 0.29 (FP) to 0.39 (PP), in the first, second, and third parity, respectively. Mean h2 estimated for SCS in the first three parities ranged from 0.17 to 0.27. The highest genetic correlations were found between daily MY and PY, followed by those found between daily FY and PY. Negative genetic correlations were found between yield traits (MY, FY, PY) and SCS. In total, seven genomic regions (BTA1 (n = 2), BTA2, BTA7, BTA10, BTA22, and BTA26) were identified to be associated with the included traits. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that differential sets of candidate genes underlie the phenotypic expression of the considered traits among parities and lactation stages of each parity. The findings of this study may be used for future implementation of genomic evaluation to improve lactation performances in DPBB cows.

Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires.

Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

The pattern of linkage disequilibrium in Dual-Purpose Belgian Blue cattle.

Quantifying the level of linkage disequilibrium (LD), non-random association of alleles at two or more loci, is important to determine the number of markers needed for genomic selection. The aims of this study were to evaluate the extent of LD in Dual-Purpose Belgian Blue (DPBB) and to compare the level of LD in DPBB with that of Walloon Holstein. Data of 28,427 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes(BTA), of 639 DPBB and 398 Holstein bulls were used. The level of LD between pairwise SNPs separated by up to 10Mb was evaluated, separately for each breed, using the squared correlation of the alleles at two loci. The analysis of molecular variance showed that the percentage of variation within populations (85.48%) was higher than between populations (14.52%). However, permutation tests showed a significant genetic differentiation between the two studied populations (p<.01). The average LD found between adjacent SNP pairs in DPBB (0.16 (SD=0.22)) was generally lower than in Holstein (0.23 (SD=0.27)). The proportion of SNPs in useful LD (r2 >0.30) within a genomic distance of ≤0.10Mb between SNPs was 18.58% and 28.23% in DPBB and Holstein bulls, respectively. In both breeds, the effective population size decreased over generations; however, the decline was greater in DPBB than that in Holstein. Based on results, it can be concluded that at least 68,000 SNPs are needed for implementing genomic selection in DPBB cattle with enough accuracy.

A mutation in PHKG1 causes high drip loss and low meat quality in Chinese Ningdu yellow chickens

With increasing societal development and the concurrent improvement in people's quality of life, meat consumption has gradually changed from a focus on “quantity” to “quality”. Broiler production is increasingly used as a means to improve meat quality by altering various characteristics, especially its genetic factors. However, until now, little has been known about the genetic variants related to meat quality traits in Chinese purebred chicken populations. To better understand these genetic underpinnings, a total of 17 traits related to meat quality and carcass were measured in 325 Chinese Ningdu yellow chickens. We performed DNA sequencing to detect nucleotide mutations, after which we conducted association studies between PHKG1 gene polymorphisms and traits related to meat quality and carcass. Results indicated a large phenotypic variation in meat quality traits. More specifically, the single nucleotide polymorphism (SNP) rs15845448 was significantly associated with drip loss at 24 h (P = 8.04 × 10−6) and 48 h (P = 5.47 × 10−6), pH (P = 2.39 × 10−3), and meat color L* (P = 9.88 × 10−3). Moreover, the SNP rs15845448 reduced 24 h and 48 h drip loss by 3.62 and 5.97%, respectively. However, no significant associations were found between rs15845448 and carcass traits (P > 0.05). Furthermore, a haplotype block containing 2 adjacent SNPs (rs15845448 and rs15845450) was identified. This block displayed 4 distinct haplotypes that had significant association with drip loss at 24 h and 48 h, pH, and meat color L*. Collectively, these results provide new insights into the genetic basis of meat quality in Chinese Ningdu yellow chickens. Moreover, the significance of SNP rs15845448 could be incorporated into the selection programs involving this breed.

The Bnapus50K array: a quick and versatile genotyping tool for Brassica napus genomic breeding and research.

Rapeseed is a globally cultivated commercial crop, primarily grown for its oil. High-density single nucleotide polymorphism (SNP) arrays are widely used as a standard genotyping tool for rapeseed research, including for gene mapping, genome-wide association studies, germplasm resource analysis, and cluster analysis. Although considerable rapeseed genome sequencing data have been released, DNA arrays are still an attractive choice for providing additional genetic data in an era of high-throughput whole-genome sequencing. Here, we integrated re-sequencing DNA array data (32,216, 304 SNPs) from 505 inbred rapeseed lines, allowing us to develop a sensitive and efficient genotyping DNA array, Bnapus50K, with a more consistent genetic and physical distribution of probes. A total of 42,090 high-quality probes were filtered and synthesized, with an average distance between adjacent SNPs of 8 kb. To improve the practical application potential of this array in rapeseed breeding, we also added 1,618 functional probes related to important agronomic traits such as oil content, disease resistance, male sterility, and flowering time. The additional probes also included those specifically for detecting genetically modified material. These probes show a good detection efficiency and are therefore useful for gene mapping, along with crop variety improvement and identification. The novel Bnapus50K DNA array developed in this study could prove to be a quick and versatile genotyping tool for B. napus genomic breeding and research.

Identification of novel candidate genes for age at first calving in Nellore cows using a SNP chip specifically developed for Bos taurus indicus cattle

The age at first calving has a great economic impact on the beef cattle system and calving at 24 months is an objective of selection for a more efficient herd. However, an age at first calving around 36 months has been observed for Nellore cattle in Brazil. Thus, a genome-wide association study (GWAS) was carried out with 8376 records of age at first calving and 3239 animals genotyped with the GGP-Indicus 35K, which has been developed specifically for Bos taurus indicus. The weighted single-step genomic best linear unbiased prediction method was used, with adjacent SNPs (single nucleotide polymorphisms) in genomic windows of 1.0 Mb. After quality control, 3239 (2161 males and 1078 females) animals genotyped for 30,519 SNPs were used in GWAS analysis. The average and standard deviation of age at first calving were 1041.7 and 140.6 days, respectively. The heritability estimate was 0.10 ± 0.02. The GWAS analysis found seven genomic regions in BTA1, 2, 5, 12, 18, 21, and 24, which explained a total of 11.24% of the additive genetic variance of age at first calving. In these regions were found 62 protein coding genes, and the genes HSD17B2, SERPINA14, SERPINA1, SERPINA5, STAT1, NFATC1, ATP9B, CTDP1, THPO, ECE2, PSMD2, EIF4G1, EIF2B2, DVL3, POLR2H, TMTC2, and GPC6 are possible candidates for age at first birth due their function. Moreover, two molecular functions (“serine-type endopeptidase inhibitor activity” and “negative regulation of endopeptidase activity”) were significant, which depend on several serpin genes. The use of a SNP chip developed especially for Bos taurus indicus allowed to find genomic regions for age at first calving, which are close to QTLs previously reported for other reproduction-related traits. Future studies can reveal the causal variants and their effects on reproductive precocity of Nellore cows.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20-30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

A genome-wide association study (GWAS) for pH value in the meat of Berkshire pigs.

The purpose of this study is to estimate the single nucleotide polymorphism (SNP) effect for pH values affecting Berkshire meat quality. A total of 39,603 SNPs from 1,978 heads after quality control and 882 pH values were used estimate SNP effect by single step genomic best linear unbiased prediction (ssGBLUP) method. The average physical distance between adjacent SNP pairs was 61.7kbp and the number and proportion of SNPs whose minor allele frequency was below 10% were 9,573 and 24.2%, respectively. The average of observed heterozygosity and polymorphic information content was 0.32 ± 0.16 and 0.26 ± 0.11, respectively and the estimate for average linkage disequilibrium was 0.40. The heritability of pH45m and pH24h were 0.10 and 0.15 respectively. SNPs with an absolute value more than 4 standard deviations from the mean were selected as threshold markers, among the selected SNPs, protein-coding genes of pH45m and pH24h were detected in 6 and 4 SNPs, respectively. The distribution of coding genes <RFX8, CREG2, TBC1D8, EXOC6B> were detected at pH45m and <C12orf49, LOC106506010, BICC1, ANK3> were detected at pH24h.

High-Density Linkage Maps Based on Genotyping-by-Sequencing (GBS) Confirm a Chromosome-Level Genome Assembly and Reveal Variation in Recombination Rate for the Pacific Oyster Crassostrea gigas.

Studies of linkage and linkage mapping have advanced genetic and biological knowledge for over 100 years. In addition to their growing role, today, in mapping phenotypes to genotypes, dense linkage maps can help to validate genome assemblies. Previously, we showed that 40% of scaffolds in the first genome assembly for the Pacific oyster Crassostrea gigas were chimeric, containing single nucleotide polymorphisms (SNPs) mapping to different linkage groups. Here, we merge 14 linkage maps constructed of SNPs generated from genotyping-by-sequencing (GBS) methods with five, previously constructed linkage maps, to create a compendium of nearly 69 thousand SNPs mapped with high confidence. We use this compendium to assess a recently available, chromosome-level assembly of the C. gigas genome, mapping SNPs in 275 of 301 contigs and comparing the ordering of these contigs, by linkage, to their assembly by Hi-C sequencing methods. We find that, while 26% of contigs contain chimeric blocks of SNPs, i.e., adjacent SNPs mapping to different linkage groups than the majority of SNPs in their contig, these apparent misassemblies amount to only 0.08% of the genome sequence. Furthermore, nearly 90% of 275 contigs mapped by linkage and sequencing are assembled identically; inconsistencies between the two assemblies for the remaining 10% of contigs appear to result from insufficient linkage information. Thus, our compilation of linkage maps strongly supports this chromosome-level assembly of the oyster genome. Finally, we use this assembly to estimate, for the first time in a Lophotrochozoan, genome-wide recombination rates and causes of variation in this fundamental process.

BsmI-ApaI-TaqI TAC (BAt) Haplotype of Vitamin D Receptor Gene Is Associated with Increased Risk of Major Depressive Disorder.

Heritability of major depressive disorder (MDD) is between 36 and 44%, suggesting that up tonearly half of the phenotypic variability is attributable to genes. A number of genetic polymorphisms have been shown to predispose certain individuals to depression. Of particular interest are the polymorphisms of the vitamin D receptor (VDR) gene. Although the VDR gene has been well characterized and a vast number of polymorphisms have been identified, the association between BsmI (rs1544410), ApaI (rs7975232) and TaqI (rs731236) single-nucleotide polymorphisms (SNPs), together with their haplotypes, and MDD risk have yet to be established. We conducted a matched case-control study with a total of 600 participants comprising 300 major depressive disorder (MDD) cases and 300 controls matched by age, gender and ethnicity in a 1:1 ratio, in four public hospitals in Kuala Lumpur and Selangor. Three adjacent SNPs of theVDR gene-BsmI (rs1544410), ApaI (rs7975232) and TaqI (rs731236)-were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Odds ratios and 95% confidence intervals (CIs) were obtained from conditional logistic regression using Stata 16. Linkage disequilibrium and haplotype association with MDD were analyzed using the online SNPStats program. None of the genotypes of the three SNPs was significantly associated with risk of developing MDD after adjusting for confounding factors. However, the TAC (BAt) haplotype was associated with increased odds of MDD (adjusted OR = 2.17, 95% CI = 1.30-3.61, p = 0.003) using CCT (baT) as reference haplotype. The findings suggest that the BsmI-ApaI-TaqI TAC (BAt) haplotype of the VDR gene increases susceptibility to MDD.