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Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits

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Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits Cai et al. Genet Sel Evol (2020) 52:19 https://doi.org/10.1186/s12711-020-00538-6 Genetics Selection Evolution RESEARCH ARTICLE Open Access Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle Zexi Cai1*†, Magdalena Dusza2†, Bernt Guldbrandtsen1, Mogens Sandø Lund1 and Goutam Sahana1 Abstract Background: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that afect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Efect-bearing variants often afect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic efects of variants in candidate genes. However, large sample sizes are required to achieve sufcient power. Multi-trait meta-analysis is one approach to deal with this prob- lem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. Results: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis com- pared with the subjective assessment of overlapping of single-trait QTL confdence intervals. Pleiotropic efects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confrmed by bivariate association analysis. The previously reported pleiotropic efects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic efects of variants in GHR on milk yield and fat yield were con- frmed. Furthermore, our results suggested that variants in KCTD16, KCNK18 and ENSBTAG00000023629 had pleiotropic efects on milk production traits. For milk yield and mastitis resistance, we identifed possible pleiotropic efects of variants in two genes, GC and DGAT1. Conclusions: Multi-trait meta-analysis improves our ability to detect pleiotropic interactions between milk produc- tion traits and identifes variants with pleiotropic efects on milk production traits and mastitis resistance. In particular, this should contribute to better understand the biological mechanisms that underlie the unfavorable genetic correla- tion between milk yield and mastitis. Background Intense selection for increased milk yield has negative Holstein is an important cattle breed in the Danish dairy consequences on the udder health of cows [1]. Unfavora- production and much efort has gone in the genetic ble genetic correlations between milk production and improvement of its milk production and functional traits. clinical mastitis (from 0.21 to 0.55) have been reported [2]. A genetic correlation between two traits could be due to the pleiotropic action of genetic variants or the corre- *Correspondence: [email protected] †Zexi Cai and Magdalena Dusza authors contributed equally to this study lation (i.e., linkage disequilibrium (LD)) between causal 1 Faculty of Technical Sciences, Center for Quantitative Genetics variants. Te identifcation of a quantitative trait locus and Genomics, Aarhus University, 8830 Tjele, Denmark (QTL) that afects simultaneously milk yield and udder Full list of author information is available at the end of the article health can help reveal some of the genetic basis of the © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Cai et al. Genet Sel Evol (2020) 52:19 Page 2 of 15 genetic connection between milk production and masti- the animals were imputed to the high-density (HD) level. tis resistance. In combination with specifc genetic tests, Next, the imputed HD genotypes were imputed to the this information can contribute to reduce the unfavora- WGS level using a multi-breed reference of 1228 animals: ble correlated response on mastitis due to selection that 1148 WGS from Run4 of the 1000 Bull Genomes Pro- focused on improving milk production traits by diferen- ject (288 Holstein, 56 Red, and 61 Jersey, as well as 743 tially weighting variants based on their favorable or unfa- individuals from various breeds) [8] and 80 animals from vorable efects on the two traits. Aarhus University (23 HOL, 30 RDC, and 27 JER). Impu- One application of genome-wide association studies tation to HD genotypes was done with the IMPUTE2 (GWAS) is to detect pleiotropic efects for the QTL iden- v2.3.1 software [9], and imputation to the whole-genome tifed from single-trait analysis. If a genomic region is sig- level with the Minimac2 software [10]. Te average accu- nifcant for two or more traits, it may be due to causal racy (r2-values from Minimac2) was 0.85 for across- variants that are in LD and afect individual traits (link- breed imputation. Imputation accuracy was previously age), or that these traits are afected by the same vari- reported by Wu et al. [11]. ant (pleiotropy). Te number of segregating variants in Before phasing and imputation, we fltered the 54 k and a population is large, but fnite. Te proportion of the HD genotypes based on an SNP call rate higher than 85% segregating variants that are associated with the genetic and an animal call rate higher than 90%. Te imputed variation of complex traits is unknown. However, traits sequence data included 22,751,039 bi-allelic variants. For often appear to be associated with the same or closely- each breed, SNPs with a minor allele frequency (MAF) linked variants in the genome [3, 4], which strongly sug- lower than 1% or with a highly signifcant deviation from gests that, at least some of the underlying causal variants, Hardy–Weinberg proportions (p < 1.0−6) were excluded. afect several traits. Terefore, the primary aim of this After quality fltering, 16,503,508 SNPs remained for analysis was to determine whether QTL associated with analysis. more than one trait were indeed pleiotropic. We used our previous GWAS results (summary statistics) of milk pro- Single‑SNP association analysis for a trait duction traits [3] and mastitis resistance [4] to perform a Te GWAS summary statistics were from two previous multi-trait meta-analysis for scanning lead SNPs associ- association analyses [3, 4] and, here, we provide a brief ated with three milk production traits or with milk yield description of the GWAS method used. Te genetic rela- and mastitis resistance for pleiotropy. In combination tionship matrix (GRM) was estimated using imputed HD with a bivariate analysis, we examined the possible pleio- genotypes by the GCTA software [12]. We followed the tropic nature of the QTL identifed. leave-one-chromosome-out approach [13] to build a kin- ship matrix that was specifc to the analysis of each chro- Methods mosome by leaving out markers on that chromosome to Animals and phenotypes avoid loss of power due to double-counting (ftting the We used de-regressed proofs (DRP) based on estimated SNP as a fxed efect for testing associations and as a ran- breeding values (EBV) [5, 6] for milk, fat, protein yields dom efect as part of the GRM) [14]. and mastitis resistance (udder health index, which is an First, we performed a single-SNP GWAS analysis using index for clinical mastitis from frst to third lactation) for GCTA [12] for each chromosome. A Bonferroni mul- about 5000 Nordic Holstein (HOL) bulls. Nordic Cattle tiple-testing correction was applied to control for false- Genetic Evaluation (https ://www.nordi cebv.info/) pro- positive associations: a SNP was signifcant if its test vided the EBV. probability p-value, pM , was less than 0.05/ M , where M is the number of SNPs. Tis corresponds to a trait-wise Genotype and sequence data nominal type 1 error-rate of 5%. Here, the signifcance pM = 8.5 M ≈ 15.36 We performed an association analysis using imputed threshold value was − log10( ) with whole-genome sequence (WGS) data. All bulls (~ 5000) million SNPs. We identifed the lead SNPs for each inde- were genotyped with the BovineSNP50 BeadChip SNP pendent QTL signal on a chromosome by iteratively ft- array (54 k) versions 1 or 2 (Illumina Inc., San Diego, ting the allele dosages of the lead SNPs identifed in the CA). Imputation to WGS variants was described ear- previous runs as covariate (for details see [3, 4]). lier by Iso-Touru et al. [7]. Briefy, 54k genotypes were imputed to WGS variants by a 2-step approach. First, Genetic variance explained by the identifed QTL using a multi-breed reference of 3383 animals (1222 We used GCTA [12] to estimate the genetic variance Holstein (HOL), 1326 Nordic Red cattle (RDC) and 835 explained by all the identifed QTL together for each trait.
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