scholarly journals Sperm Methylome Profiling Can Discern Fertility Levels in the Porcine Biomedical Model

2021 ◽  
Vol 22 (5) ◽  
pp. 2679
Author(s):  
Fabio Pértille ◽  
Manuel Alvarez-Rodriguez ◽  
Arthur Nery da Silva ◽  
Isabel Barranco ◽  
Jordi Roca ◽  
...  
Keyword(s):  
Animal Model ◽  
Semen Quality ◽  
Association Studies ◽  
Genotyping By Sequencing ◽  
Late Summer ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Pig Genome ◽  
Selection For ◽  
Farrowing Rate

A combined Genotyping By Sequencing (GBS) and methylated DNA immunoprecipitation (MeDIP) protocol was used to identify—in parallel—genetic variation (Genomic-Wide Association Studies (GWAS) and epigenetic differences of Differentially Methylated Regions (DMR) in the genome of spermatozoa from the porcine animal model. Breeding boars with good semen quality (n = 11) and specific and well-documented differences in fertility (farrowing rate, FR) and prolificacy (litter size, LS) (n = 7) in artificial insemination programs, using combined FR and LS, were categorized as High Fertile (HF, n = 4) or Low Fertile (LF, n = 3), and boars with Unknown Fertility (UF, n = 4) were tested for eventual epigenetical similarity with those fertility-proven. We identified 165,944 Single Nucleotide Polymorphisms (SNPs) that explained 14–15% of variance among selection lines. Between HF and LF individuals (n = 7, 4 HF and 3 LF), we identified 169 SNPs with p ≤ 0.00015, which explained 58% of the variance. For the epigenetic analyses, we considered fertility and period of ejaculate collection (late-summer and mid-autumn). Approximately three times more DMRs were observed in HF than in LF boars across these periods. Interestingly, UF boars were clearly clustered with one of the other HF or LF groups. The highest differences in DMRs between HF and LF experimental groups across the pig genome were located in the chr 3, 9, 13, and 16, with most DMRs being hypermethylated in LF boars. In both HF and LF boars, DMRs were mostly hypermethylated in late-summer compared to mid-autumn. Three overlaps were detected between SNPs (p ≤ 0.0005, n = 1318) and CpG sites within DMRs. In conclusion, fertility levels in breeding males including FR and LS can be discerned using methylome analyses. The findings in this biomedical animal model ought to be applied besides sire selection for andrological diagnosis of idiopathic sub/infertility.

scholarly journals Development of a 76k Alpaca (Vicugna pacos) Single Nucleotide Polymorphisms (SNPs) Microarray

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 291
Author(s):  
Marcos Calderon ◽  
Manuel J. More ◽  
Gustavo A. Gutierrez ◽  
Federico Abel Ponce de León
Keyword(s):  
Genetic Improvement ◽  
Association Studies ◽  
Fiber Quality ◽  
Genotyping By Sequencing ◽  
Economic Benefits ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Reduced Representation ◽  
Improvement Programs

Small farm producers’ sustenance depends on their alpaca herds and the production of fiber. Genetic improvement of fiber characteristics would increase their economic benefits and quality of life. The incorporation of molecular marker technology could overcome current limitations for the implementation of genetic improvement programs. Hence, the aim of this project was the generation of an alpaca single nucleotide polymorphism (SNP) microarray. A sample of 150 Huacaya alpacas from four farms, two each in Puno and Cerro de Pasco were used for SNP discovery by genotyping by sequencing (GBS). Reduced representation libraries, two per animal, were produced after DNA digestion with ApeK1 and double digestion with Pst1-Msp1. Ten alpaca genomes, sequenced at depths between 12× to 30×, and the VicPac3.1 reference genome were used for read alignments. Bioinformatics analysis discovered 76,508 SNPs included in the microarray. Candidate genes SNPs (302) for fiber quality and color are also included. The microarray SNPs cover 90.5% of the genome length with a density of about 39 ± 2.51 SNPs/Mb of DNA at an average interval of 26.45 ± 18.57 kbp. The performance was evaluated by genotyping 30 family trios and comparing them to their pedigrees, as well as comparing microarray to GBS genotypes. Concordance values of 0.93 and 0.94 for ApeK1 and Pst1-Msp1 generated SNPs were observed. Similarly, 290 fiber quality and color candidate gene SNPs were validated. Availability of this microarray will facilitate genome-wide association studies, marker-assisted selection and, in time, genomic selection.

scholarly journals Single Nucleotide Polymorphism Discovery and Genetic Differentiation Analysis of Geese Bred in Poland, Using Genotyping-by-Sequencing (GBS)

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1074
Author(s):  
Joanna Grzegorczyk ◽  
Artur Gurgul ◽  
Maria Oczkowicz ◽  
Tomasz Szmatoła ◽  
Agnieszka Fornal ◽  
...  
Keyword(s):  
Genotyping By Sequencing ◽  
Read Depth ◽  
Model Organisms ◽  
Single Nucleotide Polymorphism Discovery ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Polymorphism Discovery ◽  
Genome Wide ◽  
Plumage Development ◽  
Edar Gene

Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda® breed, constituting 95% of the country’s population, whereas geese of regional varieties are bred in smaller, conservation flocks. However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.

scholarly journals Genotyping-by-Sequencing of Gossypium hirsutum Races and Cultivars Uncovers Novel Patterns of Genetic Relationships and Domestication Footprints

2019 ◽  
Vol 15 ◽  
pp. 117693431988994
Author(s):  
Shulin Zhang ◽  
Yaling Cai ◽  
Jinggong Guo ◽  
Kun Li ◽  
Renhai Peng ◽  
...  
Keyword(s):  
Gossypium Hirsutum ◽  
Genetic Relationships ◽  
Phylogenetic Analyses ◽  
Genotyping By Sequencing ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Breeding Programs ◽  
Association Analyses ◽  
Genome Wide ◽  
Candidate Gene Locus

Determining the genetic rearrangement and domestication footprints in Gossypium hirsutum cultivars and primitive race genotypes are essential for effective gene conservation efforts and the development of advanced breeding molecular markers for marker-assisted breeding. In this study, 94 accessions representing the 7 primitive races of G hirsutum, along with 9 G hirsutum and 12 Gossypium barbadense cultivated accessions were evaluated. The genotyping-by-sequencing (GBS) approach was employed and 146 558 single nucleotide polymorphisms (SNP) were generated. Distinct SNP signatures were identified through the combination of selection scans and association analyses. Phylogenetic analyses were also conducted, and we concluded that the Latifolium, Richmondi, and Marie-Galante race accessions were more genetically related to the G hirsutum cultivars and tend to cluster together. Fifty-four outlier SNP loci were identified by selection-scan analysis, and 3 SNPs were located in genes related to the processes of plant responding to stress conditions and confirmed through further genome-wide signals of marker-phenotype association analysis, which indicate a clear selection signature for such trait. These results identified useful candidate gene locus for cotton breeding programs.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals Upregulation of c-MYC in cis through a Large Chromatin Loop Linked to a Cancer Risk-Associated Single-Nucleotide Polymorphism in Colorectal Cancer Cells

2010 ◽  
Vol 30 (6) ◽  
pp. 1411-1420 ◽  
Author(s):  
Jason B. Wright ◽  
Seth J. Brown ◽  
Michael D. Cole
Keyword(s):  
Cancer Risk ◽  
Cancer Cells ◽  
Association Studies ◽  
Beta Catenin ◽  
Genome Wide Association Studies ◽  
Chromatin Loop ◽  
Nucleotide Polymorphisms ◽  
Distal Enhancer ◽  
Enhancer Activity ◽  
Single Nucleotide

ABSTRACT Genome-wide association studies have mapped many single-nucleotide polymorphisms (SNPs) that are linked to cancer risk, but the mechanism by which most SNPs promote cancer remains undefined. The rs6983267 SNP at 8q24 has been associated with many cancers, yet the SNP falls 335 kb from the nearest gene, c-MYC. We show that the beta-catenin-TCF4 transcription factor complex binds preferentially to the cancer risk-associated rs6983267(G) allele in colon cancer cells. We also show that the rs6983267 SNP has enhancer-related histone marks and can form a 335-kb chromatin loop to interact with the c-MYC promoter. Finally, we show that the SNP has no effect on the efficiency of chromatin looping to the c-MYC promoter but that the cancer risk-associated SNP enhances the expression of the linked c-MYC allele. Thus, cancer risk is a direct consequence of elevated c-MYC expression from increased distal enhancer activity and not from reorganization/creation of the large chromatin loop. The findings of these studies support a mechanism for intergenic SNPs that can promote cancer through the regulation of distal genes by utilizing preexisting large chromatin loops.

Contribution of boars to reproductive performance and paternity after homospermic and heterospermic artificial insemination

2015 ◽  
Vol 27 (7) ◽  
pp. 1012 ◽  
Author(s):  
C. E. R. Ferreira ◽  
D. B. Sávio ◽  
A. C. Guarise ◽  
M. J. Flach ◽  
G. D. A. Gastal ◽  
...  
Keyword(s):  
Reproductive Performance ◽  
Artificial Insemination ◽  
Penetration Rate ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Blood Samples ◽  
Precise Evaluation ◽  
Umbilical Cords ◽  
Farrowing Rate

Heterospermic AI is commonly used in swine despite preventing precise evaluation of individual boar fertility. The present study compared the contribution of four boars (A, B, C and D) for reproductive performance and for paternity using homospermic and heterospermic (AB, AC, AD, BC, BD and CD) AI (n = 204 for homospermic AI; n = 307 for heterospermic AI). Blood samples from the four boars, from all sows inseminated with heterospermic doses and from the umbilical cords of their piglets, as well as tissue smears from mummified fetuses, were genotyped using single nucleotide polymorphisms (SNPs). Differences among boars were detected for the in vitro oocyte penetration rate and for the number of spermatozoa per oocyte (P < 0.05), but not for sperm motility, mitochondrial functionality and integrity of the membrane, acrosome and DNA (P > 0.05). Homospermic and heterospermic AI resulted in similar (P > 0.05) farrowing rates (90.5% and 89.9%, respectively) and total litter size (12.4 ± 0.4 and 12.7 ± 0.7, respectively). Farrowing rate was lower for Boar B than for Boar C (P < 0.05), but no other differences in reproductive performance among boars were observed with homospermic AI. The SNPs determined the paternity of 94.2% of the piglets sired by heterospermic AI. In the AC pool, paternity contribution per boar was similar (P > 0.05), but differences between boars occurred in all other pools (P < 0.05). Boar D achieved the greatest paternity contribution in all pools and parity categories (nearly 60%), whereas Boar B sired the fewest piglets (at most 40%). Reproductive performance was similar with homospermic and heterospermic AI, but differences in performance among boars undetected with homospermic AI were only evident after genotyping the piglets sired through heterospermic AI.

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