scholarly journals Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle)

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0241848
Author(s):  
Hyo Jun Lee ◽  
Yoon Ji Chung ◽  
Sungbong Jang ◽  
Dong Won Seo ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Text Mining ◽  
Genomic Prediction ◽  
Causal Variant ◽  
Longissimus Dorsi ◽  
Nucleotide Polymorphisms ◽  
Muscle Area ◽  
Eye Muscle ◽  
Korean Cattle ◽  
Genome Wide ◽  
Snp Panels

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner–Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2019 ◽  
Vol 20 (2) ◽  
pp. 359 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Rashid ◽  
Gaofeng Jia ◽  
Pingchuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

Prediction of eye-muscle area from introscope measurements on carcasses of pigs of bacon weight

1968 ◽  
Vol 10 (3) ◽  
pp. 283-288
Author(s):  
Janet E. Marshall ◽  
J. H. Watson ◽  
A. Slattery
Keyword(s):  
Cross Section ◽  
Live Weight ◽  
Maximum Depth ◽  
Longissimus Dorsi ◽  
Muscle Area ◽  
Eye Muscle ◽  
Minimum Depth ◽  
The Difference ◽  
Predicted Values

Carcasses of 156 pigs (78 gilts, 78 barrows) slaughtered at 200 lb live weight, were probed by introscope at six positions in line with the last rib. Backfat was measured directly and muscle depth was obtained as the difference between total probed depth from skin to rib and backfat depth. The various measurements were used to predict eye-muscle area measured on the exposed cross-section of the longissimus dorsi cut at the level of the last rib.Depth of muscle at 7·5 cm and depth of fat at 10 cm were the best predictive measurements for both barrows and gilts, although muscle at 4·5 cm in gilts and at 10 cm in barrows were of equivalent value to muscle at 7·5 cm. The correlation between observed and predicted values of eye-muscle area was 0·8. The accuracy of these predictions compared favourably with those of carcass lean percentage from maximum depth of backfat at shoulder, minimum depth of fat at loin and fat depth at ‘C’.

scholarly journals Genetic characterization of the Mirgorod pig breed, obtained by analysis of single nucleotide polymorphisms of genes

2019 ◽  
Vol 6 (2) ◽  
pp. 47-57
Author(s):  
P. Vashchenko ◽  
V. Balatsky ◽  
K. Pocherniaev ◽  
V. Voloshchuk ◽  
V. Tsybenko ◽  
...  
Keyword(s):  
Allelic Diversity ◽  
Low Frequency ◽  
Stress Sensitivity ◽  
Nucleotide Polymorphisms ◽  
Muscle Area ◽  
Eye Muscle ◽  
Genetic Characteristics ◽  
Productive Traits ◽  
High Level ◽  
Genetic Anomaly

Aim. To determine genetic characteristics of the Mirgorod pig breed by analysis of 25 SNPs of 22 genes and to conduct the associative analysis of genes MC4R (SNP c.1426 G > A), LEP (SNP g.2845 А ˃ Т), GH (BsuRI- polymorphism), CTSF (SNP g. 22 G > C) with productive traits of animals. Methods. Blood samples of pedigree Mirgorod pigs, bred at SI «Experimental farm named after Decemberists», Poltava region, were used for the studies. DNA genotyping was performed by PCR-RFLP and TaqMan. Results. Specifi c features of the breed were determined in terms of gene allele frequencies, high level of genetic variability (He – 0.326) and allelic diversity (mean number of alleles per locus – 1.96). The KPL2/m allele that causes genetic anomaly of ISTS is absent among investigated Mirgorod pigs, and the recessive RYR1 g.1843T allele, responsible for stress sensitivity of pigs, occurs at a low frequency (0.04). Unlike other breeds, a relatively high frequency of the minor allele g.15A (0.16) of CTSK and polymorphism of the LEP gene (SNP g.3996 T > C) (He – 0.455) was observed. Statistically signifi cant associations of polymorphisms have been established: MC4R (SNP c.1426 G > A) with age of gaining 100 kg, the thickness of backfat and the Eye Muscle Area, GH/BsuRI with the age of gaining 100 kg, and CTSF (SNP g. 22G > C) with Eye Muscle Area. There was a trend of statistically signifi cant differences between groups of pigs with different genotypes of LEP (SNP g.2845 А ˃ Т) and the thickness of the backfat (p = 0.09). Conclusions. It is reasonable to carry out the restoration of the gene pool of the Mirgorod pig breed, taking into account the SNPs of the studied genes and their associations with the productive traits. It is expedient to give preference to pigs with SNP genotypes с.1426 MC4R GA, MC4R AA, g. 22 CTSF CC, g.2845 LEP TT for breed reproduction.

scholarly journals Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1061
Author(s):  
Swati Srivastava ◽  
Bryan Irvine Lopez ◽  
Sara de las Heras-Saldana ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Carcass Traits ◽  
Meat Production ◽  
Nucleotide Polymorphisms ◽  
Genomic Information ◽  
Marbling Score ◽  
Muscle Area ◽  
Eye Muscle ◽  
Hanwoo Cattle ◽  
Genomic Model

Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components.

scholarly journals Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross

2005 ◽  
Vol 85 (1) ◽  
pp. 57-67 ◽  
Author(s):  
C. ÓVILO ◽  
A. FERNÁNDEZ ◽  
J. L. NOGUERA ◽  
C. BARRAGÁN ◽  
R. LETÓN ◽  
...  
Keyword(s):  
Body Composition ◽  
Leptin Receptor ◽  
Receptor Gene ◽  
Chromosome 6 ◽  
Nucleotide Polymorphisms ◽  
Fat Percentage ◽  
Muscle Area ◽  
Eye Muscle ◽  
Coding Regions ◽  
Narrow Region

The leptin receptor gene (LEPR) is a candidate for traits related to growth and body composition, and is located on SSC6 in a region where fatness and meat composition quantitative trait loci (QTL) have previously been detected in several F2 experimental designs. The aims of this work were: (i) to fine map these QTL on a larger sample of animals and generations (F3 and backcross) of an Iberian×Landrace intercross and (ii) to examine the effects of LEPR alleles on body composition traits. Eleven single nucleotide polymorphisms (SNPs) were detected by sequencing LEPR coding regions in Iberian and Landrace pig samples. Three missense polymorphisms were genotyped by pyrosequencing in 33 F0, 70 F1, 418 F2, 86 F3 and 128 individuals coming from the backcross of four F2 males with 24 Landrace females. Thirteen microsatellites and one SNP were also genotyped. Traits analysed were: backfat thickness at different locations (BFT), intramuscular fat percentage (IMFP), eye muscle area (EMA), loin depth (LOD), weight of shoulder (SHW), weight of ribs (RIBW) and weight of belly bacon (BBW). Different statistical models were applied in order to evaluate the number and effects of QTL on chromosome 6 and the possible causality of the LEPR gene variants with respect to the QTL. The results support the presence of two QTL on SSC6. One, at position 60–100 cM, affects BFT and RIBW. The other and more significant maps in a narrow region (130–132 cM) and affects BFT, IMFP, EMA, LOD, SHW, RIBW and BBW. Results also support the association between LEPR alleles and BFT traits. The possible functional implications of the analysed polymorphisms are considered.

scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2018 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Y. Rashid ◽  
Gaofeng Jia ◽  
PingChuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality, and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134, and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

scholarly journals GpNet: Genomic Prediction Network Using Locally Connected Layers in Korean Native Cattle

2021 ◽  
Author(s):  
Hyo-Jun Lee ◽  
Dong Won Seo ◽  
Yoonji Chung ◽  
Doo Ho Lee ◽  
Yeung Kuk Kim ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Genomic Prediction ◽  
Carcass Weight ◽  
Backfat Thickness ◽  
Superior Performance ◽  
Future Research ◽  
Muscle Area ◽  
Eye Muscle ◽  
Previous State ◽  
Beef Carcass

Abstract Background: The use of DNA marker information for the prediction of genetic merit in animal and plant breeding, and susceptibility to disease in human medicine has become widespread. Therefore, an increasing number of methods have been proposed for more accurate and efficient genomic prediction. However, most of the commonly used models for genomic prediction only account for additive effects since most of them are designed based on the linear model. Results: Here, we proposed a GpNet, a deep learning network for genomic prediction in Korean beef cattle. With a locally connected layer, GpNet can estimate LD-block effects of single nucleotide polymorphisms (SNP) with adjacent two or more SNPs closer to 3’-end. This operation is quite similar to how the DNA sequence is used in the translation process in which the RNA polymerase interprets DNA sequence by units of codons to downstream (3’ to 5’). GpNet archived a superior performance than previous state-of-arts methods for beef carcass weight with a predictive ability of 0.721%. GpNet also found two significant quantitative trait locus (QTL) on the regions (bta 6:38464203–39816133, bta 14:25307116–29987025) for carcass weight. However, GpNet showed less performance than linear methods in backfat thickness and eye-muscle area.Conclusions: GpNet outperformed the previous state-of-arts methods for beef carcass weight. However, GpNet cannot achieve superior performance in backfat thickness and eye-muscle area. We noticed that the lack of ability to estimate distant epistasis and dominance was the weakness of GpNet. Therefore, it remains a future research issue to expand GpNet to resolve these flaws and this further study will accelerate the new phase of the genomic prediction.

Data Mining in Genome Wide Association Studies

2011 ◽  
pp. 465-471
Author(s):  
Tom Burr
Keyword(s):  
Data Mining ◽  
Genetic Basis ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Common Diseases ◽  
Genome Wide ◽  
Genomic Regions

The genetic basis for some human diseases, in which one or a few genome regions increase the probability of acquiring the disease, is fairly well understood. For example, the risk for cystic fibrosis is linked to particular genomic regions. Identifying the genetic basis of more common diseases such as diabetes has proven to be more difficult, because many genome regions apparently are involved, and genetic effects are thought to depend in unknown ways on other factors, called covariates, such as diet and other environmental factors (Goldstein and Cavalleri, 2005). Genome-wide association studies (GWAS) aim to discover the genetic basis for a given disease. The main goal in a GWAS is to identify genetic variants, single nucleotide polymorphisms (SNPs) in particular, that show association with the phenotype, such as “disease present” or “disease absent” either because they are causal, or more likely, because they are statistically correlated with an unobserved causal variant (Goldstein and Cavalleri, 2005). A GWAS can analyze “by DNA site” or “by multiple DNA sites. ” In either case, data mining tools (Tachmazidou, Verzilli, and De Lorio, 2007) are proving to be quite useful for understanding the genetic causes for common diseases.

scholarly journals Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1019 ◽  
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Birth Date ◽  
Muscle Area ◽  
Eye Muscle ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using Illumina Bovine 50k SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.

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