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Genes
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Genomic Prediction Methods for Sequencing Data
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Genomic Prediction Methods for Sequencing Data

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Animal Genetics and Genomics".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 39069

Special Issue Editor

Prof. Dr. Cedric Gondro

E-Mail Website
Guest Editor
Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, USA
Interests: quantitative genetics; animal breeding; computational genomics; evolutionary computation; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Genomic testing has become ubiquitous in some livestock species and breeds. Farmers and breeders can now make faster and better decisions based on the DNA of an animal. Genomics is becoming the linchpin of genetic progress and is a key aspect for feeding an ever-growing human population. Industry largely relies on low- or mid-density panels for its applications, but with the decreasing costs of sequencing, there is a growing research interest in understanding its value for prediction purposes. However, as we move towards full sequence data, it is clear that we are on a diminishing returns curve. Current results suggest that the increase in prediction accuracy is mostly marginal compared to lower-density marker panels. It is an opportune time to envisage the next generation of methods that will allow us to better harness information from these high-throughput datasets. This Special Issue welcomes manuscripts that discuss the pros and/or cons of sequence data for livestock and particularly cattle production; Big Data methods articles for genomic prediction at the sequence level; and novel approaches that will drive the next round of technological development in the field.

Dr. Cedric Gondro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Cattle
  • Animal breeding
  • Genomic prediction
  • Sequence data
  • Imputation
  • Big Data
  • Statistical genetics

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Published Papers (3 papers)

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Research

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16 pages, 1521 KiB  
Article
Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
by Warren M. Snelling, Jesse L. Hoff, Jeremiah H. Li, Larry A. Kuehn, Brittney N. Keel, Amanda K. Lindholm-Perry and Joseph K. Pickrell
Genes 2020, 11(11), 1312; https://doi.org/10.3390/genes11111312 - 5 Nov 2020
Cited by 31 | Viewed by 5925
Abstract
Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced [...] Read more.
Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple Bos taurus and Bos indicus-influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers’ phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers’ MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction. Full article
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data)
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Review

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32 pages, 365 KiB  
Review
Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90
by Daniela Lourenco, Andres Legarra, Shogo Tsuruta, Yutaka Masuda, Ignacio Aguilar and Ignacy Misztal
Genes 2020, 11(7), 790; https://doi.org/10.3390/genes11070790 - 14 Jul 2020
Cited by 79 | Viewed by 11667
Abstract
Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and [...] Read more.
Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data. Full article
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data)
19 pages, 2045 KiB  
Review
A Guide on Deep Learning for Complex Trait Genomic Prediction
by Miguel Pérez-Enciso and Laura M. Zingaretti
Genes 2019, 10(7), 553; https://doi.org/10.3390/genes10070553 - 20 Jul 2019
Cited by 105 | Viewed by 20708
Abstract
Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of [...] Read more.
Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily modified to suit specific needs. DL comprises a wide variety of algorithms which depend on numerous hyperparameters. Careful optimization of hyperparameter values is critical to avoid overfitting. Among the DL architectures currently tested in genomic prediction, convolutional neural networks (CNNs) seem more promising than multilayer perceptrons (MLPs). A limitation of DL is in interpreting the results. This may not be relevant for genomic prediction in plant or animal breeding but can be critical when deciding the genetic risk to a disease. Although DL technologies are not “plug-and-play”, they are easily implemented using Keras and TensorFlow public software. To illustrate the principles described here, we implemented a Keras-based code in GitHub. Full article
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data)
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