Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Genome-wide association studies for heat stress response in Bos taurus × Bos indicus crossbred cattle
Otto, Pamela I. ; Guimarães, Simone E.F. ; Verardo, Lucas L. ; Azevedo, Ana Luísa S. ; Vandenplas, Jeremie ; Sevillano, Claudia A. ; Marques, Daniele B.D. ; Fatima A. Pires, Maria de; Freitas, Célio de; Verneque, R.S. ; Martins, Marta Fonseca ; Panetto, João Cláudio C. ; Carvalho, Wanessa A. ; Gobo, Diego O.R. ; Silva, Marcos Vinícius G.B. da; Machado, Marco A. - \ 2019
Journal of Dairy Science 102 (2019)9. - ISSN 0022-0302 - p. 8148 - 8158.
crossbred cattle - gene network - heat stress - post-GWAS analyses

Heat stress is an important issue in the global dairy industry. In tropical areas, an alternative to overcome heat stress is the use of crossbred animals or synthetic breeds, such as the Girolando. In this study, we performed a genome-wide association study (GWAS) and post-GWAS analyses for heat stress in an experimental Gir × Holstein F2 population. Rectal temperature (RT) was measured in heat-stressed F2 animals, and the variation between 2 consecutive RT measurements (ΔRT) was used as the dependent variable. Illumina BovineSNP50v1 BeadChip (Illumina Inc., San Diego, CA) and single-SNP approach were used for GWAS. Post-GWAS analyses were performed by gene ontology terms enrichment and gene-transcription factor (TF) networks, generated from enriched TF. The breed origin of marker alleles in the F2 population was assigned using the breed of origin of alleles (BOA) approach. Heritability and repeatability estimates (± standard error) for ΔRT were 0.13 ± 0.08 and 0.29 ± 0.06, respectively. Association analysis revealed 6 SNP significantly associated with ΔRT. Genes involved with biological processes in response to heat stress effects (LIF, OSM, TXNRD2, and DGCR8) were identified as putative candidate genes. After performing the BOA approach, the 10% of F2 animals with the lowest breeding values for ΔRT were classified as low-ΔRT, and the 10% with the highest breeding values for ΔRT were classified as high-ΔRT. On average, 49.4% of low-ΔRT animals had 2 alleles from the Holstein breed (HH), and 39% had both alleles from the Gir breed (GG). In high-ΔRT animals, the average proportion of animals for HH and GG were 1.4 and 50.2%, respectively. This study allowed the identification of candidate genes for ΔRT in Gir × Holstein crossbred animals. According to the BOA approach, Holstein breed alleles could be associated with better response to heat stress effects, which could be explained by the fact that Holstein animals are more affected by heat stress than Gir animals and thus require a genetic architecture to defend the body from the deleterious effects of heat stress. Future studies can provide further knowledge to uncover the genetic architecture underlying heat stress in crossbred cattle.

A second-level diagonal preconditioner for single-step SNPBLUP
Vandenplas, Jeremie ; Calus, Mario P.L. ; Eding, Herwin ; Vuik, Cornelis - \ 2019
Genetics, Selection, Evolution 51 (2019). - ISSN 0999-193X

Background: The preconditioned conjugate gradient (PCG) method is an iterative solver of linear equations systems commonly used in animal breeding. However, the PCG method has been shown to encounter convergence issues when applied to single-step single nucleotide polymorphism BLUP (ssSNPBLUP) models. Recently, we proposed a deflated PCG (DPCG) method for solving ssSNPBLUP efficiently. The DPCG method introduces a second-level preconditioner that annihilates the effect of the largest unfavourable eigenvalues of the ssSNPBLUP preconditioned coefficient matrix on the convergence of the iterative solver. While it solves the convergence issues of ssSNPBLUP, the DPCG method requires substantial additional computations, in comparison to the PCG method. Accordingly, the aim of this study was to develop a second-level preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix at a lower cost than the DPCG method, in addition to comparing its performance to the (D)PCG methods applied to two different ssSNPBLUP models. Results: Based on the properties of the ssSNPBLUP preconditioned coefficient matrix, we proposed a second-level diagonal preconditioner that decreases the largest eigenvalues of the ssSNPBLUP preconditioned coefficient matrix under some conditions. This proposed second-level preconditioner is easy to implement in current software and does not result in additional computing costs as it can be combined with the commonly used (block-)diagonal preconditioner. Tested on two different datasets and with two different ssSNPBLUP models, the second-level diagonal preconditioner led to a decrease of the largest eigenvalues and the condition number of the preconditioned coefficient matrices. It resulted in an improvement of the convergence pattern of the iterative solver. For the largest dataset, the convergence of the PCG method with the proposed second-level diagonal preconditioner was slower than the DPCG method, but it performed better than the DPCG method in terms of total computing time. Conclusions: The proposed second-level diagonal preconditioner can improve the convergence of the (D)PCG methods applied to two ssSNPBLUP models. Based on our results, the PCG method combined with the proposed second-level diagonal preconditioner seems to be more efficient than the DPCG method in solving ssSNPBLUP. However, the optimal combination of ssSNPBLUP and solver will most likely be situation-dependent.

A specific synbiotic-containing amino acid-based formula restores gut microbiota in non-IgE mediated cow's milk allergic infants: A randomized controlled trial
Wopereis, Harm ; Ampting, Marleen T.J. Van; Cetinyurek-Yavuz, Aysun ; Slump, Rob ; Candy, David C.A. ; Butt, Assad M. ; Peroni, Diego G. ; Vandenplas, Yvan ; Fox, Adam T. ; Shah, Neil ; Roeselers, Guus ; Harthoorn, Lucien F. ; Michaelis, Louise J. ; Knol, Jan ; West, Christina E. - \ 2019
Clinical and Translational Allergy 9 (2019)1. - ISSN 2045-7022
Cow's milk allergy - Gut microbiota - Pediatrics - Prebiotics - Probiotics

Background: Altered gut microbiota is implicated in cow's milk allergy (CMA) and differs markedly from healthy, breastfed infants. Infants who suffer from severe CMA often rely on cow's milk protein avoidance and, when breastfeeding is not possible, on specialised infant formulas such as amino-acid based formulas (AAF). Herein, we report the effects of an AAF including specific synbiotics on oral and gastrointestinal microbiota of infants with non-IgE mediated CMA with reference to healthy, breastfed infants. Methods: In this prospective, randomized, double-blind controlled study, infants with suspected non-IgE mediated CMA received test or control formula. Test formula was AAF with synbiotics (prebiotic fructo-oligosaccharides and probiotic Bifidobacterium breve M-16V). Control formula was AAF without synbiotics. Healthy, breastfed infants were used as a separate reference group (HBR). Bacterial compositions of faecal and salivary samples were analysed by 16S rRNA-gene sequencing. Faecal analysis was complemented with the analysis of pH, short-chain fatty acids (SCFAs) and lactic acids. Results: The trial included 35 test subjects, 36 controls, and 51 HBR. The 16S rRNA-gene sequencing revealed moderate effects of test formula on oral microbiota. In contrast, the gut microbiota was substantially affected across time comparing test with control. In both groups bacterial diversity increased over time but was characterised by a more gradual increment in test compared to control. Compositionally this reflected an enhancement of Bifidobacterium spp. and Veillonella sp. in the test group. In contrast, the control-fed infants showed increased abundance of adult-like species, mainly within the Lachnospiraceae family, as well as within the Ruminococcus and Alistipes genus. The effects on Bifidobacterium spp. and Lachnospiraceae spp. were previously confirmed through enumeration by fluorescent in situ hybridization and were shown for test to approximate the proportions observed in the HBR. Additionally, microbial activity was affected as evidenced by an increase of l-lactate, a decrease of valerate, and reduced concentrations of branched-chain SCFAs in test versus control. Conclusions: The AAF including specific synbiotics effectively modulates the gut microbiota and its metabolic activity in non-IgE mediated CMA infants bringing it close to a healthy breastfed profile. Trial registration Registered on 1 May 2013 with Netherlands Trial Register Number NTR3979.

Genomic prediction for crossbred performance using metafounders
Grevenhof, Elizabeth M. van; Vandenplas, Jérémie ; Calus, Mario P.L. - \ 2019
Journal of Animal Science 97 (2019)2. - ISSN 0021-8812 - p. 548 - 558.

Future genomic evaluation models to be used routinely in breeding programs for pigs and poultry need to be able to optimally use information of crossbred (CB) animals to predict breeding values for CB performance of purebred (PB) selection candidates. Important challenges in the commonly used single-step genomic best linear unbiased prediction (ssGBLUP) model are the definition of relationships between the different line compositions and the definition of the base generation per line. The use of metafounders (MFs) in ssGBLUP has been proposed to overcome these issues. When relationships between lines are known to be different from 0, the use of MFs generalizes the concept of genetic groups relying on the genotype data. Our objective was to investigate the effect of using MFs in genomic prediction for CB performance on estimated variance components, and accuracy and bias of GEBV. This was studied using stochastic simulation to generate data representing a three-way crossbreeding scheme in pigs, with the parental lines being either closely related or unrelated. Results show that using MFs, the variance components should be scaled appropriately, especially when basing them on estimates obtained with, for example a pedigree-based model. The accuracies of GEBV that were obtained using MFs were similar to accuracies without using MFs, regardless whether the lines involved in the CB were closely related or unrelated. The use of MFs resulted in a model that had similar or somewhat better convergence properties compared to other models. We recommend the use of MFs in ssGBLUP for genomic evaluations in crossbreeding schemes.

Imputation to whole-genome sequence using multiple pig populations and its use in genome-wide association studies
Berg, Sanne van den; Vandenplas, Jérémie ; Eeuwijk, Fred A. van; Bouwman, Aniek C. ; Lopes, Marcos S. ; Veerkamp, Roel F. - \ 2019
Genetics, Selection, Evolution 51 (2019). - ISSN 0999-193X

BACKGROUND: Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS. METHODS: Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660 K and 80 K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80 K and 660 K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R2) higher than 0.6, and the dosage scores of all iWGS SNPs. RESULTS: For the DL-line (LW-line), imputation of 80 K genotypes to iWGS resulted in an average Beagle R2 of 0.39 (0.49). After quality control, 2.5 × 106 (3.5 × 106) SNPs had a Beagle R2 higher than 0.6, resulting in an average Beagle R2 of 0.83 (0.93). Compared to the 80 K and 660 K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis. CONCLUSIONS: Imputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80 K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping.

A specific synbiotic-containing amino acid-based formula in dietary management of cow's milk allergy : A randomized controlled trial
Fox, Adam T. ; Wopereis, Harm ; Ampting, Marleen T.J. van; Oude Nijhuis, Manon M. ; Butt, Assad M. ; Peroni, Diego G. ; Vandenplas, Yvan ; Candy, David C.A. ; Shah, Neil ; West, Christina E. ; Garssen, Johan ; Harthoorn, Lucien F. ; Knol, Jan ; Michaelis, Louise J. - \ 2019
Clinical and Translational Allergy 9 (2019)1. - ISSN 2045-7022
Bifidobacterium breve M-16V - Cow's milk allergy - Gut microbiota - Prebiotic - Probiotic - Symptoms

Background: Here we report follow-up data from a double-blind, randomized, controlled multicenter trial, which investigated fecal microbiota changes with a new amino acid-based formula (AAF) including synbiotics in infants with non-immunoglobulin E (IgE)-mediated cow's milk allergy (CMA). Methods: Subjects were randomized to receive test product (AAF including fructo-oligosaccharides and Bifidobacterium breve M-16V) or control product (AAF) for 8 weeks, after which infants could continue study product until 26 weeks. Fecal percentages of bifidobacteria and Eubacterium rectale/Clostridium coccoides group (ER/CC) were assessed at 0, 8, 12, and 26 weeks. Additional endpoints included stool markers of gut immune status, clinical symptoms, and safety assessments including adverse events and medication use. Results: The trial included 35 test subjects, 36 controls, and 51 in the healthy reference group. Study product was continued by 86% and 92% of test and control subjects between week 8-12, and by 71% and 80%, respectively until week 26. At week 26 median percentages of bifidobacteria were significantly higher in test than control [47.0% vs. 11.8% (p < 0.001)], whereas percentages of ER/CC were significantly lower [(13.7% vs. 23.6% (p = 0.003)]. Safety parameters were similar between groups. Interestingly use of dermatological medication and reported ear infections were lower in test versus control, p = 0.019 and 0.011, respectively. Baseline clinical symptoms and stool markers were mild (but persistent) and low, respectively. Symptoms reduced towards lowest score in both groups. Conclusion: Beneficial effects of this AAF including specific synbiotics on microbiota composition were observed over 26 weeks, and shown suitable for dietary management of infants with non-IgE-mediated CMA.

Efficient and accurate computation of base generation allele frequencies
Aldridge, M.N. ; Vandenplas, J. ; Calus, M.P.L. - \ 2019
Journal of Dairy Science 102 (2019)2. - ISSN 0022-0302 - p. 1364 - 1373.
best linear unbiased prediction - dairy cattle - general least squares

Allele frequencies are used for several aspects of genomic prediction, with the assumption that these are equal to the allele frequency in the base generation of the pedigree. The current standard method, however, calculates allele frequencies from the current genotyped population. We compared the current standard method with BLUP and general least squares (GLS) methods explicitly targeting the base population to determine whether there is a more accurate and still efficient method of calculating allele frequencies that better represents the base generation. A data set based on a typical dairy population was simulated for 325,266 animals; the last 100,078 animals in generations 9 to 12 of the population were genotyped, with 1,670 SNP markers. For the BLUP method, several SNP genotypes were analyzed with a multitrait model by assuming a heritability of 0.99 and no genetic correlation among them. This method was limited by the time required for each BLUP to converge (approximately 6 min per BLUP run of 15 SNP). The GLS method had 2 implementations. The first implementation, using imputation on the fly and multiplication of sparse matrices, was very efficient and required just 49 s and 1.3 GB of random access memory. The second implementation, using a dense fullA22 −1 matrix, was very inefficient and required more than 1 d of wall clock time and more than 118.2 GB of random access memory. When no selection was considered in the simulations, all methods predicted equally well. When selection was introduced, higher correlations between the estimated allele frequency and known base generation allele frequency were observed for BLUP (0.96 ± 0.01) and GLS (0.97 ± 0.01) compared with the current standard method (0.87 ± 0.01). The GLS method decreased in accuracy when introducing incomplete pedigree, with 25% of sires in the first 5 generations randomly replaced as unknown to erroneously identify founder animals (0.93 ± 0.01) and a further decrease for 8 generations (0.91 ± 0.01). There was no change in accuracy when introducing 5% genotyping errors (0.97 ± 0.01), 5% missing genotypes (0.97 ± 0.01), or both 5% genotyping errors and missing genotypes (0.97 ± 0.01). The GLS method provided the most accurate estimates of base generation allele frequency and was only slightly slower compared with the current method. The efficient implementation of the GLS method, therefore, is very well suited for practical application and is recommended for implementation.

Implementing genonomic prediction models in generic evaluation of large populations
Napel, J. ten; Schopen, Ghyslaine ; Vandenplas, J. ; Cromie, A.R. ; Grevenhof, E.M. van; Veerkamp, R.F. - \ 2018
Towards routine estimation of breeding values using one million genotyped animals
Napel, J. ten; Cromie, Andrew ; Schopen, Ghyslaine ; Vandenplas, J. ; Veerkamp, R.F. - \ 2018
In: World Congress on Genetics Applied to Livestock Production IAVS / Massey University
Significance thresholds and genomic control for a GWAS using SNP panel or imputed whole genome sequence data of a pig population
Berg, S.J.P. van den; Vandenplas, J. ; Eeuwijk, F.A. van; Lopez, Marcos Soares ; Veerkamp, R.F. - \ 2018
In: Proceedings of the World Congress on Genetics Applied to Livestock Production. - - p. 994 - 994.
Derivation of parentage and breed-of-origin of alleles in a crossbred broiler dataset
Calus, M.P.L. ; Vandenplas, J. ; Hulsegge, B. ; Borg, R. ; Henshall, John ; Hawken, Rachel - \ 2018
In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - WCGALP - 6 p.
Pig and poultry breeding programs rely on crossbreeding. With genomic selection, widespread use of crossbred performance in breeding programs comes within reach. Commercial crossbreds, however, may have unknown pedigrees and their genomes include DNA from two to four different breeds, depending on the crossbreeding scheme. SNP information allows: 1) derivation of parentage, provided that genotypes of parents are available, and 2) derivation of breed-of-origin of alleles in crossbreds, provided that sufficient genotypes of purebred animals are available to determine frequencies of segregating haplotypes for each of the parental breeds. We derived both parentage and breed-of-origin of alleles in a broiler dataset that comprised 5882 purebred and 10,943 three-way crossbred offspring that were generated by natural mating of 164 purebred sires to 660 purebred and 1031 F1 crossbred hens. Numbers of offspring per sire had a very skewed distribution, ranging from 1 to 275 crossbreds and 1 to 155 purebreds. Breed-of-origin could be derived for 99.74% of the alleles of the 1031 F1 crossbred hens and for 98.10% of the alleles of the 10,943 three-way crossbred offspring. Visual inspection of the assigned breed-of-origin, however, suggested that there are some errors in assignment of the maternal alleles. Further tuning of the algorithm, or adding more purebred animals of the dam lines to the analysis, may help to resolve those errors. The achieved percentage of assignment to the sire line appears sufficient to proceed with subsequent analyses requiring only the breed-of-origin of the paternal alleles to be known.
Efficient computational strategies for multivariate single-step SNPBLUP
Vandenplas, J. ; Eding, H. ; Calus, M.P.L. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 429 - 426.
Variance estimates from the algorithm for proven and young animals are similar to current methods
Aldridge, M.N. ; Vandenplas, J. ; Bergsma, R. ; Calus, M.P.L. - \ 2018
In: Book of abstracts of the 69th Annual meeting of the European Federation of Animal Science Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 593 - 593.
Deflated preconditioned conjugate gradient method for solving single-step BLUP models efficiently
Vandenplas, Jérémie ; Eding, Herwin ; Calus, Mario P.L. ; Vuik, Cornelis - \ 2018
Genetics, Selection, Evolution 50 (2018)1. - ISSN 0999-193X

Background: The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) method, such as single-step genomic BLUP (ssGBLUP), simultaneously analyses phenotypic, pedigree, and genomic information of genotyped and non-genotyped animals. In contrast to ssGBLUP, SNP effects are fitted explicitly as random effects in the ssSNPBLUP model. Similarly, principal components associated with the genomic information can be fitted explicitly as random effects in a single-step principal component BLUP (ssPCBLUP) model to remove noise in genomic information. Single-step genomic BLUP is solved efficiently by using the preconditioned conjugate gradient (PCG) method. Unfortunately, convergence issues have been reported when solving ssSNPBLUP by using PCG. Poor convergence may be linked with poor spectral condition numbers of the preconditioned coefficient matrices of ssSNPBLUP. These condition numbers, and thus convergence, could be improved through the deflated PCG (DPCG) method, which is a two-level PCG method for ill-conditioned linear systems. Therefore, the first aim of this study was to compare the properties of the preconditioned coefficient matrices of ssGBLUP and ssSNPBLUP, and to document convergence patterns that are obtained with the PCG method. The second aim was to implement and test the efficiency of a DPCG method for solving ssSNPBLUP and ssPCBLUP. Results: For two dairy cattle datasets, the smallest eigenvalues obtained for ssSNPBLUP (ssPCBLUP) and ssGBLUP, both solved with the PCG method, were similar. However, the largest eigenvalues obtained for ssSNPBLUP and ssPCBLUP were larger than those for ssGBLUP, which resulted in larger condition numbers and in slow convergence for both systems solved by the PCG method. Different implementations of the DPCG method led to smaller condition numbers, and faster convergence for ssSNPBLUP and for ssPCBLUP, by deflating the largest unfavourable eigenvalues. Conclusions: Poor convergence of ssSNPBLUP and ssPCBLUP when solved by the PCG method are related to larger eigenvalues and larger condition numbers in comparison to ssGBLUP. These convergence issues were solved with a DPCG method that annihilates the effect of the largest unfavourable eigenvalues of the preconditioned coefficient matrix of ssSNPBLUP and of ssPCBLUP on the convergence of the PCG method. It resulted in a convergence pattern, at least, similar to that of ssGBLUP.

Genome-wide association studies for tick resistance in Bos taurus × Bos indicus crossbred cattle : A deeper look into this intricate mechanism
Otto, Pamela I. ; Guimarães, Simone E.F. ; Verardo, Lucas L. ; Azevedo, Ana Luísa S. ; Vandenplas, Jeremie ; Soares, Aline C.C. ; Sevillano, Claudia A. ; Veroneze, Renata ; Fatima A. Pires, Maria de; Freitas, Célio de; Prata, Márcia Cristina A. ; Furlong, John ; Verneque, Rui S. ; Martins, Marta Fonseca ; Panetto, João Cláudio C. ; Carvalho, Wanessa A. ; Gobo, Diego O.R. ; Silva, Marcos Vinícius G.B. da; Machado, Marco A. - \ 2018
Journal of Dairy Science 101 (2018)12. - ISSN 0022-0302 - p. 11020 - 11032.
breed of origin - gene network - genetic variance - Gir × Holstein crossbred
Rhipicephalus (Boophilus) microplus is the main cattle ectoparasite in tropical areas. Gir × Holstein crossbred cows are well adapted to different production systems in Brazil. In this context, we performed genome-wide association study (GWAS) and post-GWAS analyses for R. microplus resistance in an experimental Gir × Holstein F2 population. Single nucleotide polymorphisms (SNP) identified in GWAS were used to build gene networks and to investigate the breed of origin for its alleles. Tick artificial infestations were performed during the dry and rainy seasons. Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) and single-step BLUP procedure was used for GWAS. Post-GWAS analyses were performed by gene ontology terms enrichment and gene transcription factors networks, generated from enriched transcription factors, identified from the promoter sequences of selected gene sets. The genetic origin of marker alleles in the F2 population was assigned using the breed of origin of alleles approach. Heritability estimates for tick counts were 0.40 ± 0.11 in the rainy season and 0.54 ± 0.11 in dry season. The top ten 0.5-Mbp windows with the highest percentage of genetic variance explained by SNP markers were found in chromosomes 10 and 23 for both the dry and rainy seasons. Gene network analyses allowed the identification of genes involved with biological processes relevant to immune system functions (TREM1, TREM2, and CD83). Gene-transcription factors network allowed the identification of genes involved with immune functions (MYO5A, TREML1, and PRSS16). In resistant animals, the average proportion of animals showing significant SNPs with paternal and maternal alleles originated from Gir breed was 44.8% whereas the proportion of animals with both paternal and maternal alleles originated from Holstein breed was 11.3%. Susceptible animals showing both paternal and maternal alleles originated from Holstein breed represented 44.6% on average, whereas both paternal and maternal alleles originated from Gir breed animals represented 9.3%. This study allowed us to identify candidate genes for tick resistance in Gir × Holstein crossbreds in both rainy and dry seasons. According to the origin of alleles analysis, we found that most animals classified as resistant showed 2 alleles from Gir breed, while the susceptible ones showed alleles from Holstein. Based on these results, the identified genes may be thoroughly investigated in additional experiments aiming to validate their effects on tick resistance phenotype in cattle.
Efficient computation of base generation allele frequencies
Aldridge, M.N. ; Vandenplas, J. ; Calus, M.P.L. - \ 2018
Interbull Bulletin 53 (2018). - p. 1 - 8.
Genomic prediction using individual-level data and summary statistics from multiple populations
Vandenplas, Jeremie ; Calus, Mario P.L. ; Gorjanc, Gregor - \ 2018
Genetics 210 (2018)1. - ISSN 0016-6731 - p. 53 - 69.
Genomic prediction - GenPred - Meta-analysis - Quantitative trait - Shared data resources - Statistical method

This study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multi-population data are an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population-specific information. We show that population-specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e., the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enable integration of different sources of information across a wide range of settings, yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions.

Integration of foreign estimates of SNP effects into a domestic SNPBLUP
Vandenplas, J. ; Calus, M.P.L. ; Gorjanc, G. - \ 2018
Interbull Bulletin 53 (2018). - p. 1 - 8.

The aim of this research was to develop and to test different procedures that integrate estimates of single nucleotide polymorphism (SNP) effects and associated measures of precision from a foreign SNP Best Linear Unbiased Prediction (SNPBLUP), into a domestic SNPBLUP when exchange of genotypes or phenotypes is prohibited for whatever reason. In addition to the foreign estimates of SNP effects, procedures were developed assuming the availability of associated: 1) prediction error (co)variance (PEC) matrix; 2) PEC matrix separately for each chromosome; 3) prediction error variances (PEV) only; 4) PEV, allele frequencies, and linkage disequilibrium (LD) of foreign training set; and 5) as 4) but with LD measured on foreign selection candidates. We tested these approaches with a simulation of two historically related populations for a single trait. We confirmed that integrating foreign estimates of SNP effects and the associated PEC matrix led to the same direct genomic values for selection candidates as the joint SNPBLUP using datasets from both populations. Integrating foreign estimates and PEV only led to biased and inaccurate predictions. Procedures based on partial PEC matrices or on LD information gave almost as accurate and unbiased predictions as the joint SNPBLUP. Therefore, accurate integration of foreign estimates of SNP effects into a domestic SNPBLUP seems possible, even if only PEV and some population statistics are available.
Sparse single-step genomic blup in crossbreeding schemes
Vandenplas, Jérémie ; Calus, Mario P.L. ; Napel, Jan ten - \ 2018
Journal of Animal Science 96 (2018)6. - ISSN 0021-8812 - p. 2060 - 2073.
APY - Genomic evaluation - Single-step

The algorithm for proven and young animals (APY) efficiently computes an approximated inverse of the genomic relationship matrix, by dividing genotyped animals in the so-called core and noncore animals. The APY leads to computationally feasible single-step genomic Best Linear Unbiased Prediction (ssGBLUP) with a large number of genotyped animals and was successfully applied to real single-breed or line datasets. This study aimed to assess the quality of genomic estimated breeding values (GEBV) when using the APY (GEBVAPY), in comparison to GEBV when using the directly inverted genomic relationship matrix (GEBVDIRECT), for situations based on crossbreeding schemes, including F1 and F2 crosses, such as the ones for pigs and chickens. Based on simulations of a 3-way crossbreeding program, we compared different approximated inverses of a genomic relationship matrix, by varying the size and the composition of the core group. We showed that GEBVAPY were accurate approximations of GEBVDIRECT for multivariate ssGBLUP involving different breeds and their crosses. GEBVAPY as accurate as GEBVDIRECT were obtained when the core groups included animals from different breed compositions and when the core groups had a size between the numbers of the largest eigenvalues explaining 98% and 99% of the variation in the raw genomic relationship matrix.

SNPrune: an efficient algorithm to prune large SNP array and sequence datasets based on high linkage disequilibrium
Calus, Mario P.L. ; Vandenplas, Jérémie - \ 2018
Genetics, Selection, Evolution 50 (2018). - ISSN 0999-193X

Background: High levels of pairwise linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) array or whole-genome sequence data may affect both performance and efficiency of genomic prediction models. Thus, this warrants pruning of genotyping data for high LD. We developed an algorithm, named SNPrune, which enables the rapid detection of any pair of SNPs in complete or high LD throughout the genome. Methods: LD, measured as the squared correlation between phased alleles (r 2), can only reach a value of 1 when both loci have the same count of the minor allele. Sorting loci based on the minor allele count, followed by comparison of their alleles, enables rapid detection of loci in complete LD. Detection of loci in high LD can be optimized by computing the range of the minor allele count at another locus for each possible value of the minor allele count that can yield LD values higher than a predefined threshold. This efficiently reduces the number of pairs of loci for which LD needs to be computed, instead of considering all pairwise combinations of loci. The implemented algorithm SNPrune considered bi-allelic loci either using phased alleles or allele counts as input. SNPrune was validated against PLINK on two datasets, using an r 2 threshold of 0.99. The first dataset contained 52k SNP genotypes on 3534 pigs and the second dataset contained simulated whole-genome sequence data with 10.8 million SNPs and 2500 animals. Results: SNPrune removed a similar number of SNPs as PLINK from the pig data but SNPrune was almost 12 times faster than PLINK. From the simulated sequence data with 10.8 million SNPs, SNPrune removed 6.4 and 1.4 million SNPs due to complete and high LD. Results were very similar regardless of whether phased alleles or allele counts were used. Using allele counts and multi-threading with 10 threads, SNPrune completed the analysis in 21 min. Using a sliding window of up to 500,000 SNPs, PLINK removed ~ 43,000 less SNPs (0.6%) in the sequence data and SNPrune was 24 to 170 times faster, using one or ten threads, respectively. Conclusions: The SNPrune algorithm developed here is able to remove SNPs in high LD throughout the genome very efficiently in large datasets.

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