P4043 Construction of the SNP panel for hucul horse parentage control based on the openarray platform

2016 ◽  
Vol 94 (suppl_4) ◽  
pp. 99-100
Author(s):  
A. Fornal ◽  
A. Piestrzynska-Kajtoch ◽  
A. Radko
Keyword(s):  
Snp Panel ◽  
Parentage Control

scholarly journals 99 Breed authentication of five lines comparing Structure, Admixture, and an allele frequency method

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 25-25
Author(s):  
Austin M Putz ◽  
Patrick Charagu ◽  
Abe Huisman
Keyword(s):  
Population Structure ◽  
Allele Frequency ◽  
Regression Coefficients ◽  
Frequency Method ◽  
Large White ◽  
Reference Allele ◽  
Software Packages ◽  
The Matrix ◽  
Snp Panel ◽  
Compare And Contrast

Abstract Two commonly used population structure software packages are freely available for breed authentication, Structure and Admixture. Structure uses a Bayesian approach to model population structure, while Admixture uses a frequentist approach. More recently, an allele frequency method has been updated to use quadratic programming to constrain the multiple linear regression coefficients of the regression of genotype count (divided by two) on the matrix of allele frequencies for each known breed or line. This constraint forced coefficients to sum to one and be greater than or equal to 0 and less than or equal to 1. The goal of this research was to compare and contrast these three methods to determine the breed/line authenticity for each of the five genetic lines. These five lines included Large White, Landrace, a lean Duroc, a meat quality Duroc, and a Pietrain line. Only animals with a 50K SNP panel were used in this analysis. Analyses were run five times for Structure and Admixture to check repeatability. The allele frequency method did not need to be repeated because it remains the same as long as the reference allele frequency matrix stays constant. For Structure, results of breed composition were inconsistent across replicates. Structure separated at least one of the maternal lines in three out of the five replicates with only 500 animals and kept the Duroc lines together as one population. Only 500 animals could be utilized in each run of Structure due to computational restraints. Admixture was very consistent across runs for each animal, but also failed to separate the two Duroc lines, instead splitting one of the two maternal lines. Finally, the allele frequency method split all five lines correctly and was 100% reproducible as long as the reference allele frequency matrix stays the same across runs.

scholarly journals Discovery and refinement of muscle weight QTLs in B6 × D2 advanced intercross mice

2014 ◽  
Vol 46 (16) ◽  
pp. 571-582 ◽  
Author(s):  
P. Carbonetto ◽  
R. Cheng ◽  
J. P. Gyekis ◽  
C. C. Parker ◽  
D. A. Blizard ◽  
...  
Keyword(s):  
Inbred Strains ◽  
Mouse Strains ◽  
Missense Mutations ◽  
Muscle Weight ◽  
Hindlimb Muscles ◽  
Hindlimb Muscle ◽  
Causal Genes ◽  
Snp Panel ◽  
Genomic Regions ◽  
Genetic Contributions

The genes underlying variation in skeletal muscle mass are poorly understood. Although many quantitative trait loci (QTLs) have been mapped in crosses of mouse strains, the limited resolution inherent in these conventional studies has made it difficult to reliably pinpoint the causal genetic variants. The accumulated recombination events in an advanced intercross line (AIL), in which mice from two inbred strains are mated at random for several generations, can improve mapping resolution. We demonstrate these advancements in mapping QTLs for hindlimb muscle weights in an AIL ( n = 832) of the C57BL/6J (B6) and DBA/2J (D2) strains, generations F8–F13. We mapped muscle weight QTLs using the high-density MegaMUGA SNP panel. The QTLs highlight the shared genetic architecture of four hindlimb muscles and suggest that the genetic contributions to muscle variation are substantially different in males and females, at least in the B6D2 lineage. Out of the 15 muscle weight QTLs identified in the AIL, nine overlapped the genomic regions discovered in an earlier B6D2 F2 intercross. Mapping resolution, however, was substantially improved in our study to a median QTL interval of 12.5 Mb. Subsequent sequence analysis of the QTL regions revealed 20 genes with nonsense or potentially damaging missense mutations. Further refinement of the muscle weight QTLs using additional functional information, such as gene expression differences between alleles, will be important for discerning the causal genes.

An SNP panel for the analysis of paternally inherited alleles in maternal plasma using ion Torrent PGM

2017 ◽  
Vol 132 (2) ◽  
pp. 343-352 ◽  
Author(s):  
Donggui Yang ◽  
Hao Liang ◽  
Shaobin Lin ◽  
Qing Li ◽  
Xiaoyan Ma ◽  
...  
Keyword(s):  
Maternal Plasma ◽  
Ion Torrent ◽  
Ion Torrent Pgm ◽  
Snp Panel

scholarly journals Functional SNP panel for parentage assessment and assignment in worldwide goat breeds

2018 ◽  
Vol 50 (1) ◽  
Author(s):  
Andrea Talenti ◽  
◽  
Isabelle Palhière ◽  
Flavie Tortereau ◽  
Giulio Pagnacco ◽  
...  
Keyword(s):  
Snp Panel ◽  
Functional Snp

scholarly journals Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle

2021 ◽  
Vol 11 ◽  
Author(s):  
Bryan Irvine M. Lopez ◽  
Narae An ◽  
Krishnamoorthy Srikanth ◽  
Seunghwan Lee ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Prediction Accuracy ◽  
Sequence Data ◽  
Predictive Performance ◽  
Genomic Region ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Marbling Score ◽  
Muscle Area ◽  
Snp Panel

Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density (1000, 3000, 5000, or 10,000) were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.

Paternity investigation experience with a 40 autosomal SNP panel

2009 ◽  
Vol 2 (1) ◽  
pp. 149-150 ◽  
Author(s):  
M.R. Whittle ◽  
E.C. Favaro ◽  
D.R. Sumita
Keyword(s):  
Snp Panel

scholarly journals Chinese Fir Breeding in the High-Throughput Sequencing Era: Insights from SNPs

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 681 ◽  
Author(s):  
Huiquan Zheng ◽  
Dehuo Hu ◽  
Ruping Wei ◽  
Shu Yan ◽  
Runhui Wang
Keyword(s):  
Population Structure ◽  
Genetic Structure ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Breeding Population ◽  
Population Diversity ◽  
Chinese Fir ◽  
High Density ◽  
Population Structure Analysis ◽  
Snp Panel

Knowledge on population diversity and structure is of fundamental importance for conifer breeding programs. In this study, we concentrated on the development and application of high-density single nucleotide polymorphism (SNP) markers through a high-throughput sequencing technique termed as specific-locus amplified fragment sequencing (SLAF-seq) for the economically important conifer tree species, Chinese fir (Cunninghamia lanceolata). Based on the SLAF-seq, we successfully established a high-density SNP panel consisting of 108,753 genomic SNPs from Chinese fir. This SNP panel facilitated us in gaining insight into the genetic base of the Chinese fir advance breeding population with 221 genotypes for its genetic variation, relationship and diversity, and population structure status. Overall, the present population appears to have considerable genetic variability. Most (94.15%) of the variability was attributed to the genetic differentiation of genotypes, very limited (5.85%) variation occurred on the population (sub-origin set) level. Correspondingly, low FST (0.0285–0.0990) values were seen for the sub-origin sets. When viewing the genetic structure of the population regardless of its sub-origin set feature, the present SNP data opened a new population picture where the advanced Chinese fir breeding population could be divided into four genetic sets, as evidenced by phylogenetic tree and population structure analysis results, albeit some difference in membership of the corresponding set (cluster vs. group). It also suggested that all the genetic sets were admixed clades revealing a complex relationship of the genotypes of this population. With a step wise pruning procedure, we captured a core collection (core 0.650) harboring 143 genotypes that maintains all the allele, diversity, and specific genetic structure of the whole population. This generalist core is valuable for the Chinese fir advanced breeding program and further genetic/genomic studies.

scholarly journals Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel

BMC Genomics ◽  
2008 ◽  
Vol 9 (1) ◽  
pp. 187 ◽  
Author(s):  
Mehar S Khatkar ◽  
Frank W Nicholas ◽  
Andrew R Collins ◽  
Kyall R Zenger ◽  
Julie AL Cavanagh ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
High Density ◽  
Genome Wide ◽  
Holstein Friesian ◽  
Snp Panel ◽  
Friesian Cattle ◽  
Genome Wide Linkage Disequilibrium

Microsatellite-based parentage control in cattle

2009 ◽  
Vol 26 (1) ◽  
pp. 7-12 ◽  
Author(s):  
M-L Glowatzki-Mullis ◽  
C Gaillard ◽  
G Wigger ◽  
R Fries
Keyword(s):  
Parentage Control
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