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Mara Battagin

Bio: Mara Battagin is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Population & Sire. The author has an hindex of 11, co-authored 28 publications receiving 427 citations. Previous affiliations of Mara Battagin include University of Padua & The Roslin Institute.

Papers
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Journal ArticleDOI
TL;DR: AlphaSim allows breeders and researchers to simulate genomic data with specific user criteria and is flexible, computationally efficient, and easy to use for a wide range of possible scenarios.
Abstract: This paper describes AlphaSim, a software package for simulating plant and animal breeding programs. AlphaSim enables the simulation of multiple aspects of breeding programs with a high degree of flexibility. AlphaSim simulates breeding programs in a series of steps: (i) simulate haplotype sequences and pedigree; (ii) drop haplotypes into the base generation of the pedigree and select single-nucleotide polymorphism (SNP) and quantitative trait nucleotide (QTN); (iii) assign QTN effects, calculate genetic values, and simulate phenotypes; (iv) drop haplotypes into the burn-in generations; and (v) perform selection and simulate new generations. The program is flexible in terms of historical population structure and diversity, recent pedigree structure, trait architecture, and selection strategy. It integrates biotechnologies such as doubled-haploids (DHs) and gene editing and allows the user to simulate multiple traits and multiple environments, specify recombination hot spots and cold spots, specify gene jungles and deserts, perform genomic predictions, and apply optimal contribution selection. AlphaSim also includes restart functionalities, which increase its flexibility by allowing the simulation process to be paused so that the parameters can be changed or to import an externally created pedigree, trial design, or results of an analysis of previously simulated data. By combining the options, a user can simulate simple or complex breeding programs with several generations, variable population structures and variable breeding decisions over time. In conclusion, AlphaSim is a flexible and computationally efficient software package to simulate biotechnology enhanced breeding programs with the aim of performing rapid, low-cost, and objective in silico comparison of breeding technologies.

160 citations

Journal ArticleDOI
TL;DR: Higher recombination rates can enhance the efficiency of breeding programs to turn genetic variation into response to selection, however, to increase response toselection significantly, the recombination rate would need to be increased 10- or 20-fold.
Abstract: In this work, we performed simulations to explore the potential of manipulating recombination rates to increase response to selection in livestock breeding programs. We carried out ten replicates of several scenarios that followed a common overall structure but differed in the average rate of recombination along the genome (expressed as the length of a chromosome in Morgan), the genetic architecture of the trait under selection, and the selection intensity under truncation selection (expressed as the proportion of males selected). Recombination rates were defined by simulating nine different chromosome lengths: 0.10, 0.25, 0.50, 1, 2, 5, 10, 15 and 20 Morgan, respectively. One Morgan was considered to be the typical chromosome length for current livestock species. The genetic architecture was defined by the number of quantitative trait variants (QTV) that affected the trait under selection. Either a large (10,000) or a small (1000 or 500) number of QTV was simulated. Finally, the proportions of males selected under truncation selection as sires for the next generation were equal to 1.2, 2.4, 5, or 10 %. Increasing recombination rate increased the overall response to selection and decreased the loss of genetic variance. The difference in cumulative response between low and high recombination rates increased over generations. At low recombination rates, cumulative response to selection tended to asymptote sooner and the genetic variance was completely eroded. If the trait under selection was affected by few QTV, differences between low and high recombination rates still existed, but the selection limit was reached at all rates of recombination. Higher recombination rates can enhance the efficiency of breeding programs to turn genetic variation into response to selection. However, to increase response to selection significantly, the recombination rate would need to be increased 10- or 20-fold. The biological feasibility and consequences of such large increases in recombination rates are unknown.

41 citations

Journal ArticleDOI
TL;DR: Physical and color characteristics of chicken meat were investigated by directly applying a fiberoptic probe to the breast muscle and using the visible-near-infrared (NIR) spectral range from 350 to 1,800 nm to suggest that NIR is a feasible technique for the assessment of several quality traits of intact breast muscle.

41 citations

Journal ArticleDOI
TL;DR: This study evaluated the potential of accurate within-family imputation for enabling cost-effective genomic selection in a breeding program with inbred parents and their segregating progeny distributed among families.
Abstract: Genomic selection has great potential to increase the efficiency of plant breeding, but its implementation is hindered by the high costs of collecting the necessary data. In this study we evaluated the potential of accurate within-family imputation for enabling cost-effective genomic selection. We have simulated a breeding program with inbred parents and their segregating progeny distributed among families, of which some were used as a training set and some were used as a prediction set. parents were genotyped at high density (20,000 markers), while progeny were genotyped at high or low density (500, 200, 100, or 50 markers) and imputed. Low-density markers were chosen to segregate within each family separately. The assumed low-density genotyping costs accounted for this assumption. Six sets of scenarios were analyzed in which imputation was leveraged to maximize cost effectiveness of genomic selection by (i) decreasing the genotyping costs, (ii) increasing selection intensity by genotyping more individuals at fewer markers, or (iii) increasing prediction accuracy by genotyping more phenotyped individuals at fewer markers. The results show that, with a constant size of the training and prediction sets, the prediction accuracy was unimpaired when at least 200 low-density markers were used. However, the return on investment was maximal (5.67 times that of the baseline scenario) when only 50 low-density markers were used because that enabled maximal reduction in the genotyping costs and only minimal reduction in the prediction accuracy. Increasing either the training set or prediction set further increased the return on investment when imputed genotypes were used, but not when the true high-density genotypes were used. The results show how plant breeding programs can implement genomic selection in a cost-effective way. G. Gorjanc, M. Battagin, J.-F. Dumasy, R. Antolin, R.C. Gaynor, and J.M. Hickey, The Roslin Institute and Royal (Dick) School of Veterinary Studies, Univ. of Edinburgh, Easter Bush Research Centre, Midlothian EH25 9RG, UK. Received 16 June 2016. Accepted 9 Nov. 2016. *Corresponding author (Gregor.Gorjanc@roslin.ed.ac.uk). Assigned to Associate Editor Aaron Lorenz. Published in Crop Sci. 57:216–228 (2017). doi: 10.2135/cropsci2016.06.0526 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY license (https:// creativecommons.org/licenses/by/4.0/). Published December 22, 2016

39 citations

Journal ArticleDOI
TL;DR: A simple pipeline is proposed to correct the preferential bias towards the reference allele that can occur during variant discovery and users of low-coverage sequence data are recommended to be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-co coverage sequence data.
Abstract: Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing. We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points. We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data.

37 citations


Cited by
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Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

DissertationDOI
01 Jan 1983

766 citations

Journal ArticleDOI
TL;DR: Recent advances in high throughput field phenotyping technologies designed to inform the genetics of quantitative traits, including crop yield and disease tolerance are reviewed.

210 citations