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Gualdrón Duarte JL, Yuan C, Gori AS, Moreira GCM, Takeda H, Coppieters W, Charlier C, Georges M, Druet T. Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals. Genet Sel Evol 2023; 55:83. [PMID: 38017417 PMCID: PMC10683324 DOI: 10.1186/s12711-023-00857-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Cohorts of individuals that have been genotyped and phenotyped for genomic selection programs offer the opportunity to better understand genetic variation associated with complex traits. Here, we performed an association study for traits related to body size and muscular development in intensively selected beef cattle. We leveraged multiple trait information to refine and interpret the significant associations. RESULTS After a multiple-step genotype imputation to the sequence-level for 14,762 Belgian Blue beef (BBB) cows, we performed a genome-wide association study (GWAS) for 11 traits related to muscular development and body size. The 37 identified genome-wide significant quantitative trait loci (QTL) could be condensed in 11 unique QTL regions based on their position. Evidence for pleiotropic effects was found in most of these regions (e.g., correlated association signals, overlap between credible sets (CS) of candidate variants). Thus, we applied a multiple-trait approach to combine information from different traits to refine the CS. In several QTL regions, we identified strong candidate genes known to be related to growth and height in other species such as LCORL-NCAPG or CCND2. For some of these genes, relevant candidate variants were identified in the CS, including three new missense variants in EZH2, PAPPA2 and ADAM12, possibly two additional coding variants in LCORL, and candidate regulatory variants linked to CCND2 and ARMC12. Strikingly, four other QTL regions associated with dimension or muscular development traits were related to five (recessive) deleterious coding variants previously identified. CONCLUSIONS Our study further supports that a set of common genes controls body size across mammalian species. In particular, we added new genes to the list of those associated with height in both humans and cattle. We also identified new strong candidate causal variants in some of these genes, strengthening the evidence of their causality. Several breed-specific recessive deleterious variants were identified in our QTL regions, probably as a result of the extreme selection for muscular development in BBB cattle.
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Affiliation(s)
- José Luis Gualdrón Duarte
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium.
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium.
| | - Can Yuan
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Ann-Stephan Gori
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium
| | - Gabriel C M Moreira
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Haruko Takeda
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Wouter Coppieters
- GIGA Genomic Platform, GIGA-R, University of Liège, Avenue de l'Hôpital, 1, 4000, Liège, Belgium
| | - Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
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Talouarn E, Bardou P, Palhière I, Oget C, Clément V, Tosser-Klopp G, Rupp R, Robert-Granié C. Genome wide association analysis on semen volume and milk yield using different strategies of imputation to whole genome sequence in French dairy goats. BMC Genet 2020; 21:19. [PMID: 32085723 PMCID: PMC7035711 DOI: 10.1186/s12863-020-0826-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/13/2020] [Indexed: 01/17/2023] Open
Abstract
Background Goats were domesticated 10,500 years ago to supply humans with useful resources. Since then, specialized breeds that are adapted to their local environment have been developed and display specific genetic profiles. The VarGoats project is a 1000 genomes resequencing program designed to cover the genetic diversity of the Capra genus. In this study, our main objective was to assess the use of sequence data to detect genomic regions associated with traits of interest in French Alpine and Saanen breeds. Results Direct imputation from the GoatSNP50 BeadChip genotypes to sequence level was investigated in these breeds using FImpute and different reference panels: within-breed, all Capra hircus sequenced individuals, European goats and French mainland goats. The best results were obtained with the French goat panel with allele and genotype concordance rates reaching 0.86 and 0.75 in the Alpine and 0.86 and 0.73 in the Saanen breed respectively. Mean correlations tended to be low in both breeds due to the high proportion of variants with low frequencies. For association analysis, imputation was performed using FImpute for 1129 French Alpine and Saanen males using within-breed and French panels on 23,338,436 filtered variants. The association results of both imputation scenarios were then compared. In Saanen goats, a large region on chromosome 19 was significantly linked to semen volume and milk yield in both scenarios. Significant variants for milk yield were annotated for 91 genes on chromosome 19 in Saanen goats. For semen volume, the annotated genes include YBOX2 which is related to azoospermia or oligospermia in other species. New signals for milk yield were detected on chromosome 2 in Alpine goats and on chromosome 5 in Saanen goats when using a multi-breed panel. Conclusion Even with very small reference populations, an acceptable imputation quality can be achieved in French dairy goats. GWAS on imputed sequences confirmed the existence of QTLs and identified new regions of interest in dairy goats. Adding identified candidates to a genotyping array and sequencing more individuals might corroborate the involvement of identified regions while removing potential imputation errors.
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Affiliation(s)
- Estelle Talouarn
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France.
| | - Philippe Bardou
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France.,Sigenae, INRAE, 31326, Castanet-Tolosan, France
| | - Isabelle Palhière
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France
| | - Claire Oget
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France
| | | | | | - Gwenola Tosser-Klopp
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France
| | - Rachel Rupp
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet Tolosan, France
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Zhang F, Huang J, Tang M, Cheng X, Liu Y, Tong C, Yu J, Sadia T, Dong C, Liu L, Tang B, Chen J, Liu S. Syntenic quantitative trait loci and genomic divergence for Sclerotinia resistance and flowering time in Brassica napus. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2019; 61:75-88. [PMID: 30506639 DOI: 10.1111/jipb.12754] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
Oilseed rape (Brassica napus) is an allotetraploid with two subgenomes descended from a common ancestor. Accordingly, its genome contains syntenic regions with many duplicate genes, some of which may have retained their original functions, whereas others may have diverged. Here, we mapped quantitative trait loci (QTL) for stem rot resistance (SRR), a disease caused by the fungus Sclerotinia sclerotiorum, and flowering time (FT) in a recombinant inbred line population. The population was genotyped using B. napus 60K single nucleotide polymorphism arrays and phenotyped in six (FT) and nine (SSR) experimental conditions or environments. In total, we detected 30 SRR QTL and 22 FT QTL and show that some of the major QTL associated with these two traits were co-localized, suggesting a genetic linkage between them. Two SRR QTL on chromosome A2 and two on chromosome C2 were shown to be syntenic, suggesting the functional conservation of these regions. We used the syntenic properties of the genomic regions to exclude genes for selection candidates responsible for QTL-associated traits. For example, 152 of the 185 genes could be excluded from a syntenic A2-C2 region. These findings will help to elucidate polyploid genomics in future studies, in addition to providing useful information for B. napus breeding programs.
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Affiliation(s)
- Fengqi Zhang
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
- Cereal Crops Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Junyan Huang
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
- Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Faculty of Life Science, Hubei University, Wuhan 430062, China
| | - Minqiang Tang
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Xiaohui Cheng
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Yueying Liu
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Chaobo Tong
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
- Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Faculty of Life Science, Hubei University, Wuhan 430062, China
| | - Jingyin Yu
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Tehrim Sadia
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Caihua Dong
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
- Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Faculty of Life Science, Hubei University, Wuhan 430062, China
| | - Lingyan Liu
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
| | - Baojun Tang
- Cereal Crops Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Jianguo Chen
- Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Faculty of Life Science, Hubei University, Wuhan 430062, China
| | - Shengyi Liu
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, the Ministry of Agriculture, Oil Crops Research Institute of CAAS, Wuhan 430062, China
- Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Faculty of Life Science, Hubei University, Wuhan 430062, China
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Martin P, Palhière I, Maroteau C, Clément V, David I, Klopp GT, Rupp R. Genome-wide association mapping for type and mammary health traits in French dairy goats identifies a pleiotropic region on chromosome 19 in the Saanen breed. J Dairy Sci 2018; 101:5214-5226. [PMID: 29573797 DOI: 10.3168/jds.2017-13625] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 01/26/2018] [Indexed: 11/19/2022]
Abstract
Type traits and mammary health traits are important to dairy ruminant breeding because they influence animal health, milking ability, and longevity, as well as the economic sustainability of farms. The availability of the genomic sequence and a single nucleotide polymorphism chip in goats has opened up new fields of investigation to better understand the genes and mechanisms that underlie such complex traits and to be able to select them. Our objective was to perform a genome-wide association study in dairy goats for 11 type traits and somatic cell count (SCC) as proxies for mastitis resistance. A genome-wide association study was implemented using a daughter design composed of 1,941 Alpine and Saanen goats sired by 20 artificial insemination bucks, genotyped with the Illumina GoatSNP50 BeadChip (Illumina Inc., San Diego, CA). This association study was based on both linkage analyses and linkage disequilibrium using QTLmap software (http://dga7.jouy.inra.fr/qtlmap/) interval mapping was performed with the likelihood ratio test using linear regressions. Breeds were analyzed together and separately. The study highlighted 37 chromosome-wide significant quantitative trait loci (QTL) with linkage analyses and 222 genome-wide significant QTL for linkage disequilibrium, for type and SCC traits in dairy goats. Genomic control of those traits was mostly polygenic and breed-specific, suggesting that within-breed selection would be favored for those traits. Of note, Capra hircus autosome (CHI) 19 appeared to be highly enriched in single nucleotide polymorphisms associated with type and SCC, with 2 highly significant regions in the Saanen breed. One region (33-42 Mb) was significantly associated with SCC and includes candidate genes associated with response to intramammary infections (RARA, STAT3, STAT5A, and STAT5B). Another region of the CHI 19 (24.5-27 Mb) exhibited an adverse pleiotropic effect on milk production (milk, fat yield, and protein yield) and udder traits (udder floor position and rear udder attachment) that agreed with the negative genetic correlations that exist between those 2 groups of traits. These QTL were not found in the Alpine breed. In Alpine, the 2 most significant regions were associated with chest depth on CHI 6 (45.8-46.0 Mb) and CHI 8 (80.7-81.1 Mb). These results will be helpful for goat selection in the future and could lead to identification of causal mutations.
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Affiliation(s)
- Pauline Martin
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, F-31326 France
| | - Isabelle Palhière
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, F-31326 France
| | - Cyrielle Maroteau
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, F-31326 France
| | - Virginie Clément
- Institut de l'Elevage, Chemin de Borde Rouge, Castanet Tolosan, F-31326 France
| | - Ingrid David
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, F-31326 France
| | | | - Rachel Rupp
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, F-31326 France.
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Schulthess AW, Reif JC, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Ganal MW, Röder MS, Jiang Y. The roles of pleiotropy and close linkage as revealed by association mapping of yield and correlated traits of wheat (Triticum aestivum L.). JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:4089-4101. [PMID: 28922760 PMCID: PMC5853857 DOI: 10.1093/jxb/erx214] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 06/01/2017] [Indexed: 05/22/2023]
Abstract
Grain yield (GY) of bread wheat (Triticum aestivum L.) is quantitatively inherited. Correlated GY-syndrome traits such as plant height (PH), heading date (HD), thousand grain weight (TGW), test weight (TW), grains per ear (GPE), and ear weight (EW) influence GY. Most quantitative genetics studies assessed the multiple-trait (MT) complex of GY-syndrome using single-trait approaches, and little is known about its underlying pleiotropic architecture. We investigated the pleiotropic architecture of wheat GY-syndrome through MT association mapping (MT-GWAS) using 372 varieties phenotyped in up to eight environments and genotyped with 18 832 single nucleotide polymorphisms plus 24 polymorphic functional markers. MT-GWAS revealed a total of 345 significant markers spread genome wide, representing 8, 40, 11, 40, 34, and 35 effective GY-PH, GY-HD, GY-TGW, GY-TW, GY-GPE, and GY-EW associations, respectively. Among them, pleiotropic roles of Rht-B1 and TaGW2-6B loci were corroborated. Only one marker presented simultaneous associations for three traits (i.e. GY-TGW-TW). Close linkage was difficult to differentiate from pleiotropy; thus, the pleiotropic architecture of GY-syndrome was dissected more as a cause of pleiotropy rather than close linkage. Simulations showed that minor allele frequencies, along with sizes and distances between quantitative trait loci for two traits, influenced the ability to distinguish close linkage from pleiotropy.
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Affiliation(s)
- Albert W Schulthess
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jie Ling
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | | | | | | | | | | | | | | | - Marion S Röder
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
- Correspondence:
| | - Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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Deciphering mechanisms underlying the genetic variation of general production and liver quality traits in the overfed mule duck by pQTL analyses. Genet Sel Evol 2017; 49:38. [PMID: 28424047 PMCID: PMC5396126 DOI: 10.1186/s12711-017-0313-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 03/30/2017] [Indexed: 11/29/2022] Open
Abstract
Background The aim of this study was to analyse the mechanisms that underlie phenotypic quantitative trait loci (QTL) in overfed mule ducks by identifying co-localized proteomic QTL (pQTL). The QTL design consisted of three families of common ducks that were progeny-tested by using 294 male mule ducks. This population of common ducks was genotyped using a genetic map that included 334 genetic markers located across 28 APL chromosomes (APL for Anas platyrhynchos). Mule ducks were phenotyped for 49 traits related to growth, metabolism, overfeeding ability and meat and fatty liver quality, and 326 soluble fatty liver proteins were quantified. Results One hundred and seventy-six pQTL and 80 phenotypic QTL were detected at the 5% chromosome-wide significance threshold. The great majority of the identified pQTL were trans-acting and localized on a chromosome other than that carrying the coding gene. The most significant pQTL (1% genome-wide significance) were found for alpha-enolase on APL18 and fatty acid synthase on APL24. Some proteins were associated with numerous pQTL (for example, 17 and 14 pQTL were detected for alpha-enolase and apolipoprotein A1, respectively) and pQTL hotspots were observed on some chromosomes (APL18, 24, 25 and 29). We detected 66 co-localized phenotypic QTL and pQTL for which the significance of the two-trait QTL (2t-QTL) analysis was higher than that of the strongest QTL using a single-trait approach. Among these, 16 2t-QTL were pleiotropic. For example, on APL15, melting rate and abundance of two alpha-enolase spots appeared to be impacted by a single locus that is involved in the glycolytic process. On APLZ, we identified a pleiotropic QTL that modified both the blood level of glucose at the beginning of the force-feeding period and the concentration of glutamate dehydrogenase, which, in humans, is involved in increased glucose absorption by the liver when the glutamate dehydrogenase 1 gene is mutated. Conclusions We identified pleiotropic loci that affect metabolic pathways linked to glycolysis or lipogenesis, and in the end to fatty liver quality. Further investigation, via transcriptomics and metabolomics approaches, is required to confirm the biomarkers that were found to impact the genetic variability of these phenotypic traits. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0313-6) contains supplementary material, which is available to authorized users.
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Rausher MD, Delph LF. Commentary: When does understanding phenotypic evolution require identification of the underlying genes? Evolution 2015; 69:1655-64. [PMID: 25973520 DOI: 10.1111/evo.12687] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 05/10/2015] [Indexed: 12/15/2022]
Abstract
Adaptive evolution is fundamentally a genetic process. Over the past three decades, characterizing the genes underlying adaptive phenotypic change has revealed many important aspects of evolutionary change. At the same time, natural selection is often fundamentally an ecological process that can often be studied without identifying the genes underlying the variation on which it acts. This duality has given rise to disagreement about whether, and under what circumstances, it is necessary to identify specific genes associated with phenotypic change. This issue is of practical concern, especially for researchers who study nonmodel organisms, because of the often enormous cost and labor required to "go for the genes." We here consider a number of situations and questions commonly addressed by researchers. Our conclusion is that although gene identification can be crucial for answering some questions, there are others for which definitive answers can be obtained without finding underlying genes. It should thus not be assumed that considerations of "empirical completeness" dictate that gene identification is always desirable.
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Affiliation(s)
- Mark D Rausher
- Department of Biology, Duke University, Box 90338, Durham, North Carolina, 27708.
| | - Lynda F Delph
- Department of Biology, 1001 East Third Street, Indiana University, Bloomington, Indiana, 47405
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Bolormaa S, Pryce JE, Reverter A, Zhang Y, Barendse W, Kemper K, Tier B, Savin K, Hayes BJ, Goddard ME. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet 2014; 10:e1004198. [PMID: 24675618 PMCID: PMC3967938 DOI: 10.1371/journal.pgen.1004198] [Citation(s) in RCA: 161] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 01/02/2014] [Indexed: 12/14/2022] Open
Abstract
Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V−1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups. We describe novel methods for finding significant associations between a genome wide panel of SNPs and multiple complex traits, and further for distinguishing between genes with effects on multiple traits and multiple linked genes affecting different traits. The method uses a meta-analysis based on estimates of SNP effects from independent single trait genome wide association studies (GWAS). The method could therefore be widely used to combine already published GWAS results. The method was applied to 32 traits that describe growth, body composition, feed intake and reproduction in 10,191 beef cattle genotyped for approximately 700,000 SNP. The genes found to be associated with these traits can be arranged into 4 groups that differ in their pattern of effects and hence presumably in their physiological mechanism of action. For instance, one group of genes affects weight and fatness in the opposite direction and can be described as a group of genes affecting mature size, while another group affects weight and fatness in the same direction.
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Affiliation(s)
- Sunduimijid Bolormaa
- Victorian Department of Environment and Primary Industries, Bundoora, Victoria, Australia
- * E-mail:
| | - Jennie E. Pryce
- Victorian Department of Environment and Primary Industries, Bundoora, Victoria, Australia
| | - Antonio Reverter
- CSIRO Animal, Food and Health Sciences, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | - Yuandan Zhang
- Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia
| | - William Barendse
- CSIRO Animal, Food and Health Sciences, Queensland Bioscience Precinct, St. Lucia, Queensland, Australia
| | - Kathryn Kemper
- School of Land and Environment, University of Melbourne, Parkville, Victoria, Australia
| | - Bruce Tier
- Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia
| | - Keith Savin
- Victorian Department of Environment and Primary Industries, Bundoora, Victoria, Australia
| | - Ben J. Hayes
- Victorian Department of Environment and Primary Industries, Bundoora, Victoria, Australia
| | - Michael E. Goddard
- Victorian Department of Environment and Primary Industries, Bundoora, Victoria, Australia
- School of Land and Environment, University of Melbourne, Parkville, Victoria, Australia
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