Accuracy of 54K to HD gebotype imputation in Brown Swiss cattle

Imputation of genotypes can be used to reduce the implementation costs of genomic selection. In this study, we evaluated the accuracy of genotype imputation from Illumina 54k to Illumina High Density (HD) in Brown Swiss cattle. Genotype data comprised 6,106 54k and 880 HD genotyped bulls and cows of Brown Swiss and Original Braunvieh cattle. Genotype data was checked for parentage conflicts and SNP were excluded if MAF was below 0.5% and SNP call rate was lower than 90%. The final data set included 39,004 SNP for the 54k and 627,306 SNP for the HD chip. HD genotypes of animals born between 2004 and 2008 (n=365) were masked to mimic animals genotyped with the 54k chip. Methods used for imputation were FImpute and Findhap V2. Both programs use pedigree information for imputation. The accuracy of imputation was assessed by the correlation (r) between true and imputed genotypes, the percentage of correctly and incorrectly imputed genotypes. Both programs gave high imputation accuracy with FImpute outperforming Findhap. Accuracy of imputation increased with increasing relationship between the HD genotyped reference population and 54k genotyped imputation candidates. Average r for FImpute and Findhap were 0.992 and 0.988 when both parents of the 54k genotyped candidate were HD genotyped, respectively. Correlations were lower when no direct relatives were HD genotyped (0.971 and 0.918 for FImpute and Findhap, respectively). Accuracy of imputation highly depended on MAF of the imputed SNP. For FImpute, average r ranged between 0.89 (MAF <0.025) and 0.99 (MAF between 0.4 and 0.5)

Genome-wide association study for 13 udder traits from linear type classification in cattle

Udder conformation traits are known to correlate with the incidence of clinical mastitis and the length of productive life. The results of a genome-wide association study based on imputed high-density genotypes of 1,637 -Brown Swiss sires and de-regressed breeding values for 13 udder traits are presented here. For seven traits significant signals could be observed in five regions on BTA3, BTA5, BTA6, BTA17, and BTA25. For fore udder length and teats diameter significant SNPs were found in a known region around 90 Mb on BTA6. For the trait rear udder height significant SNPs are positioned in the coding region of the SNX29gene. Several significant SNPs around 62 Mb on BTA17 are associated with the traits rear udder width, frontteat placement and rear teat placement. The function of potential candidate genes and the influence of substructure will be addressed as next steps

Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population

Estimated breeding values for the selection of more profitable sheep for the sheep meat and wool industries are currently based on pedigree and phenotypic records. With the advent of a medium-density DNA marker array, which genotypes ∼50000 ovine single nucleotide polymorphisms, a third source of information has become available. The aim of this paper was to determine whether this genomic information can be used to predict estimated breeding values for wool and meat traits. The effects of all single nucleotide polymorphism markers in a multi-breed sheep reference population of 7180 individuals with phenotypic records were estimated to derive prediction equations for genomic estimated breeding values (GEBV) for greasy fleece weight, fibre diameter, staple strength, breech wrinkle score, weight at ultrasound scanning, scanned eye muscle depth and scanned fat depth. Five hundred and forty industry sires with very accurate Australian sheep breeding values were used as a validation population and the accuracies of GEBV were assessed according to correlations between GEBV and Australian sheep breeding values . The accuracies of GEBV ranged from 0.15 to 0.79 for wool traits in Merino sheep and from 0.07 to 0.57 for meat traits in all breeds studied. Merino industry sires tended to have more accurate GEBV than terminal and maternal breeds because the reference population consisted mainly of Merino haplotypes. The lower accuracy for terminal and maternal breeds suggests that the density of genetic markers used was not high enough for accurate across-breed prediction of marker effects. Our results indicate that an increase in the size of the reference population will increase the accuracy of GEBV

Small molecule inhibitors of Late SV40 Factor (LSF) abrogate hepatocellular carcinoma (HCC): evaluation using an endogenous HCC model

Hepatocellular carcinoma (HCC) is a lethal malignancy with high mortality and poor prognosis. Oncogenic transcription factor Late SV40 Factor (LSF) plays an important role in promoting HCC. A small molecule inhibitor of LSF, Factor Quinolinone Inhibitor 1 (FQI1), significantly inhibited human HCC xenografts in nude mice without harming normal cells. Here we evaluated the efficacy of FQI1 and another inhibitor, FQI2, in inhibiting endogenous hepatocarcinogenesis. HCC was induced in a transgenic mouse with hepatocyte-specific overexpression of c-myc (Alb/c-myc) by injecting N-nitrosodiethylamine (DEN) followed by FQI1 or FQI2 treatment after tumor development. LSF inhibitors markedly decreased tumor burden in Alb/c-myc mice with a corresponding decrease in proliferation and angiogenesis. Interestingly, in vitro treatment of human HCC cells with LSF inhibitors resulted in mitotic arrest with an accompanying increase in CyclinB1. Inhibition of CyclinB1 induction by Cycloheximide or CDK1 activity by Roscovitine significantly prevented FQI-induced mitotic arrest. A significant induction of apoptosis was also observed upon treatment with FQI. These effects of LSF inhibition, mitotic arrest and induction of apoptosis by FQI1s provide multiple avenues by which these inhibitors eliminate HCC cells. LSF inhibitors might be highly potent and effective therapeutics for HCC either alone or in combination with currently existing therapies.The present study was supported in part by grants from The James S. McDonnell Foundation, National Cancer Institute Grant R01 CA138540-01A1 (DS), National Institutes of Health Grant R01 CA134721 (PBF), the Samuel Waxman Cancer Research Foundation (SWCRF) (DS and PBF), National Institutes of Health Grants R01 GM078240 and P50 GM67041 (SES), the Johnson and Johnson Clinical Innovation Award (UH), and the Boston University Ignition Award (UH). JLSW was supported by Alnylam Pharmaceuticals, Inc. DS is the Harrison Endowed Scholar in Cancer Research and Blick scholar. PBF holds the Thelma Newmeyer Corman Chair in Cancer Research. The authors acknowledge Dr. Lauren E. Brown (Dept. Chemistry, Boston University) for the synthesis of FQI1 and FQI2, and Lucy Flynn (Dept. Biology, Boston University) for initially identifying G2/M effects caused by FQI1. (James S. McDonnell Foundation; R01 CA138540-01A1 - National Cancer Institute; R01 CA134721 - National Institutes of Health; R01 GM078240 - National Institutes of Health; P50 GM67041 - National Institutes of Health; Samuel Waxman Cancer Research Foundation (SWCRF); Johnson and Johnson Clinical Innovation Award; Boston University Ignition Award; Alnylam Pharmaceuticals, Inc.)Published versio

Multifunction Protein Staphylococcal Nuclease Domain Containing 1 (SND1) Promotes Tumor Angiogenesis in Human Hepatocellular Carcinoma through Novel Pathway That Involves Nuclear Factor κB and miR-221

Staphylococcal nuclease domain-containing 1 (SND1) is a multifunctional protein that is overexpressed in multiple cancers, including hepatocellular carcinoma (HCC). Stable overexpression of SND1 in Hep3B cells expressing a low level of SND1 augments, whereas stable knockdown of SND1 in QGY-7703 cells expressing a high level of SND1 inhibits establishment of xenografts in nude mice, indicating that SND1 promotes an aggressive tumorigenic phenotype. In this study we analyzed the role of SND1 in regulating tumor angiogenesis, a hallmark of cancer. Conditioned medium from Hep3B-SND1 cells stably overexpressing SND1 augmented, whereas that from QGY-SND1si cells stably overexpressing SND1 siRNA significantly inhibited angiogenesis, as analyzed by a chicken chorioallantoic membrane assay and a human umbilical vein endothelial cell differentiation assay. We unraveled a linear pathway in which SND1-induced activation of NF-κB resulted in induction of miR-221 and subsequent induction of angiogenic factors Angiogenin and CXCL16. Inhibition of either of these components resulted in significant inhibition of SND1-induced angiogenesis, thus highlighting the importance of this molecular cascade in regulating SND1 function. Because SND1 regulates NF-κB and miR-221, two important determinants of HCC controlling the aggressive phenotype, SND1 inhibition might be an effective strategy to counteract this fatal malady

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project GenTORE and Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Background: At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI). Methods. Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length. Results: RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls. Conclusions: Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ∼ 3,000 to 5,000 evenly spaced SNP

Genomic Selection for Fruit Quality Traits in Apple (Malus×domestica Borkh.)

The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r2 = 0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits