Literature DB >> 27568046

Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds.

Irene van den Berg1, Didier Boichard2, Mogens Sandø Lund3.   

Abstract

The objective of this study was to compare mapping precision and power of within-breed and multibreed genome-wide association studies (GWAS) and to compare the results obtained by the multibreed GWAS with 3 meta-analysis methods. The multibreed GWAS was expected to improve mapping precision compared with a within-breed GWAS because linkage disequilibrium is conserved over shorter distances across breeds than within breeds. The multibreed GWAS was also expected to increase detection power for quantitative trait loci (QTL) segregating across breeds. GWAS were performed for production traits in dairy cattle, using imputed full genome sequences of 16,031 bulls, originating from 6 French and Danish dairy cattle populations. Our results show that a multibreed GWAS can be a valuable tool for the detection and fine mapping of quantitative trait loci. The number of QTL detected with the multibreed GWAS was larger than the number detected by the within-breed GWAS, indicating an increase in power, especially when the 2 Holstein populations were combined. The largest number of QTL was detected when all populations were combined. The analysis combining all breeds was, however, dominated by Holstein, and QTL segregating in other breeds but not in Holstein were sometimes overshadowed by larger QTL segregating in Holstein. Therefore, the GWAS combining all breeds except Holstein was useful to detect such peaks. Combining all breeds except Holstein resulted in smaller QTL intervals on average, but this outcome was not the case when the Holstein populations were included in the analysis. Although no decrease in the average QTL size was observed, mapping precision did improve for several QTL. Out of 3 different multibreed meta-analysis methods, the weighted z-scores model resulted in the most similar results to the full multibreed GWAS and can be useful as an alternative to a full multibreed GWAS. Differences between the multibreed GWAS and the meta-analyses were larger when different breeds were combined than when the 2 Holstein populations were combined.
Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  genome-wide association studies (GWAS); meta-analysis; multibreed; whole genome sequence

Mesh:

Year:  2016        PMID: 27568046     DOI: 10.3168/jds.2016-11073

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  18 in total

1.  RAPID COMMUNICATION: Multi-breed validation study unraveled genomic regions associated with puberty traits segregating across tropically adapted breeds1.

Authors:  Thaise P Melo; Marina R S Fortes; Gerardo A Fernandes Junior; Lucia G Albuquerque; Roberto Carvalheiro
Journal:  J Anim Sci       Date:  2019-07-02       Impact factor: 3.159

2.  Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen.

Authors:  Irene van den Berg; Phuong N Ho; Tuan V Nguyen; Mekonnen Haile-Mariam; Timothy D W Luke; Jennie E Pryce
Journal:  Genet Sel Evol       Date:  2022-04-18       Impact factor: 4.297

3.  Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle.

Authors:  Irene van den Berg; Didier Boichard; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2016-11-04       Impact factor: 4.297

4.  Genome-wide association study and genomic prediction in citrus: Potential of genomics-assisted breeding for fruit quality traits.

Authors:  Mai F Minamikawa; Keisuke Nonaka; Eli Kaminuma; Hiromi Kajiya-Kanegae; Akio Onogi; Shingo Goto; Terutaka Yoshioka; Atsushi Imai; Hiroko Hamada; Takeshi Hayashi; Satomi Matsumoto; Yuichi Katayose; Atsushi Toyoda; Asao Fujiyama; Yasukazu Nakamura; Tokurou Shimizu; Hiroyoshi Iwata
Journal:  Sci Rep       Date:  2017-07-05       Impact factor: 4.379

5.  Selecting sequence variants to improve genomic predictions for dairy cattle.

Authors:  Paul M VanRaden; Melvin E Tooker; Jeffrey R O'Connell; John B Cole; Derek M Bickhart
Journal:  Genet Sel Evol       Date:  2017-03-07       Impact factor: 4.297

6.  Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai).

Authors:  Mai F Minamikawa; Norio Takada; Shingo Terakami; Toshihiro Saito; Akio Onogi; Hiromi Kajiya-Kanegae; Takeshi Hayashi; Toshiya Yamamoto; Hiroyoshi Iwata
Journal:  Sci Rep       Date:  2018-08-10       Impact factor: 4.379

7.  Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution.

Authors:  Hubert Pausch; Reiner Emmerling; Birgit Gredler-Grandl; Ruedi Fries; Hans D Daetwyler; Michael E Goddard
Journal:  BMC Genomics       Date:  2017-11-09       Impact factor: 3.969

8.  Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle.

Authors:  Marie-Pierre Sanchez; Armelle Govignon-Gion; Pascal Croiseau; Sébastien Fritz; Chris Hozé; Guy Miranda; Patrice Martin; Anne Barbat-Leterrier; Rabia Letaïef; Dominique Rocha; Mickaël Brochard; Mekki Boussaha; Didier Boichard
Journal:  Genet Sel Evol       Date:  2017-09-18       Impact factor: 4.297

9.  Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds.

Authors:  Irene van den Berg; Ruidong Xiang; Janez Jenko; Hubert Pausch; Mekki Boussaha; Chris Schrooten; Thierry Tribout; Arne B Gjuvsland; Didier Boichard; Øyvind Nordbø; Marie-Pierre Sanchez; Mike E Goddard
Journal:  Genet Sel Evol       Date:  2020-07-07       Impact factor: 4.297

10.  High confidence copy number variants identified in Holstein dairy cattle from whole genome sequence and genotype array data.

Authors:  Adrien M Butty; Tatiane C S Chud; Filippo Miglior; Flavio S Schenkel; Arun Kommadath; Kirill Krivushin; Jason R Grant; Irene M Häfliger; Cord Drögemüller; Angela Cánovas; Paul Stothard; Christine F Baes
Journal:  Sci Rep       Date:  2020-05-15       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.