Literature DB >> 33673800

Phenomes: the current frontier in animal breeding.

Miguel Pérez-Enciso1,2, Juan P Steibel3,4.   

Abstract

Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to 'phenomics' instead of 'genomics'. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and 'missingness' of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.

Entities:  

Mesh:

Year:  2021        PMID: 33673800      PMCID: PMC7934239          DOI: 10.1186/s12711-021-00618-1

Source DB:  PubMed          Journal:  Genet Sel Evol        ISSN: 0999-193X            Impact factor:   4.297


  32 in total

Review 1.  Phenomics: the next challenge.

Authors:  David Houle; Diddahally R Govindaraju; Stig Omholt
Journal:  Nat Rev Genet       Date:  2010-12       Impact factor: 53.242

2.  What exactly are genomes, genotypes and phenotypes? And what about phenomes?

Authors:  M Mahner; M Kary
Journal:  J Theor Biol       Date:  1997-05-07       Impact factor: 2.691

3.  Can we open the black box of AI?

Authors:  Davide Castelvecchi
Journal:  Nature       Date:  2016-10-06       Impact factor: 49.962

4.  Identification of errors and factors associated with errors in data from electronic swine feeders.

Authors:  D S Casey; H S Stern; J C M Dekkers
Journal:  J Anim Sci       Date:  2005-05       Impact factor: 3.159

5.  Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Authors:  Belcy K Angarita; Rodolfo J C Cantet; Kaitlin E Wurtz; Carly I O O’Malley; Janice M Siegford; Catherine W Ernst; Simon P Turner; Juan P Steibel
Journal:  J Anim Sci       Date:  2019-09-03       Impact factor: 3.159

6.  Bayesian inference on genetic merit under uncertain paternity.

Authors:  Fernando F Cardoso; Robert J Tempelman
Journal:  Genet Sel Evol       Date:  2003 Sep-Oct       Impact factor: 4.297

7.  Opportunities to Improve Resilience in Animal Breeding Programs.

Authors:  Tom V L Berghof; Marieke Poppe; Han A Mulder
Journal:  Front Genet       Date:  2019-01-14       Impact factor: 4.599

8.  The Future of Phenomics.

Authors:  Christine Baes; Flavio Schenkel
Journal:  Anim Front       Date:  2020-04-01

Review 9.  A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock.

Authors:  James E Koltes; John B Cole; Roxanne Clemmens; Ryan N Dilger; Luke M Kramer; Joan K Lunney; Molly E McCue; Stephanie D McKay; Raluca G Mateescu; Brenda M Murdoch; Ryan Reuter; Caird E Rexroad; Guilherme J M Rosa; Nick V L Serão; Stephen N White; M Jennifer Woodward-Greene; Millie Worku; Hongwei Zhang; James M Reecy
Journal:  Front Genet       Date:  2019-12-17       Impact factor: 4.599

View more
  3 in total

1.  Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein.

Authors:  Francesco Tiezzi; Allison Fleming; Francesca Malchiodi
Journal:  Animals (Basel)       Date:  2022-05-06       Impact factor: 3.231

Review 2.  Applications of Omics Technology for Livestock Selection and Improvement.

Authors:  Dibyendu Chakraborty; Neelesh Sharma; Savleen Kour; Simrinder Singh Sodhi; Mukesh Kumar Gupta; Sung Jin Lee; Young Ok Son
Journal:  Front Genet       Date:  2022-06-02       Impact factor: 4.772

3.  Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation.

Authors:  Wim Gorssen; Carmen Winters; Roel Meyermans; Rudi D'Hooge; Steven Janssens; Nadine Buys
Journal:  Sci Rep       Date:  2022-09-13       Impact factor: 4.996

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.