WO2020229641A1 - A method and system for estimation of the breeding value of an animal for eating quality and/or commercial yield prediction - Google Patents
A method and system for estimation of the breeding value of an animal for eating quality and/or commercial yield prediction Download PDFInfo
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- WO2020229641A1 WO2020229641A1 PCT/EP2020/063558 EP2020063558W WO2020229641A1 WO 2020229641 A1 WO2020229641 A1 WO 2020229641A1 EP 2020063558 W EP2020063558 W EP 2020063558W WO 2020229641 A1 WO2020229641 A1 WO 2020229641A1
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- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Definitions
- the present disclosure relates to a method and system to predict an estimation of breeding value of an animal. More specifically the present disclosure relates to estimation of breeding value of an animal for meat-eating quality and/or commercial meat yield prediction.
- US Patent Application US2017362655 relate to methods, compositions, and systems are provided for managing bovine subjects in order to maximize their individual potential performance and edible meat value, and to maximize profits obtained in marketing the bovine subjects.
- the methods and systems draw an inference of a trait of a bovine subject by determining the nucleotide occurrence of at least one bovine Single Nucleotide Polymorphism (SNP) that is identified herein as being associated with the trait.
- SNP bovine Single Nucleotide Polymorphism
- Chinese Patent Application CN105200148 relates to method for auxiliary detection of carcass composition traits of cattle with dual purposes of meat and dairy as well as kits and can apply to early selection of breeding beef cattle for genetic marker screening of carcass traits of the cattle with dual purposes of meat and dairy.
- molecular detection kits are used for detection, SNPs genetic markers of carcass and meat quality traits in beef cattle sample population can be identified after sequencing, haplotype analysis is performed, 6 SNPs loci in a PSAP (prosaposin) gene function action zone and haplotype combinations of the SNPs loci can serve as effective molecular genetic markers for auxiliary detection of the carcass traits of the cattle with the dual purposes of meat and dairy.
- PSAP prosaposin
- haplotype combinations of the SNPs loci can serve as effective molecular genetic markers for auxiliary detection of the carcass traits of the cattle with the dual purposes of meat and dairy.
- Primer segments of PSAP genes and the SNPs locus haplotype combinations Hap2 and Hap6 can serve as special kits for detection and prediction of the carcass composition traits of the cattle with the dual purposes of meat and dairy.
- kits can be used for early selection of excellent individuals in germplasm resource development of the cattle with the dual purposes of meat and dairy as well as screening and production of fattening cattle before lairage in industrial production.
- Other patent publications in the art include W02005/078144, CN 108 388 765, CN 103 914 631 and CN 109 524 059.
- none of the above prior art provides a method or system for selecting or predicting livestock having a desirable meat-eating quality or the optimised yield of selected commercial cuts of meat. While inter-breed differentiation on meat-eating quality is currently used, intra-breed variability in meat-eating quality is not currently considered. Furthermore, accurate assessment of meat- eating quality attributes typically requires experienced sensory scientists and a team of trained sensory panellists. Each animal assessment also requires multiple panellist measurements (6-10) for each quality trait. Consequently, generating databases of animal or genetic associated eating quality sensory traits is both costly and time consuming. Furthermore, to maintain accurate estimates of genetic merit, in for instance a National Breeding Index, continued and long term sensory measurements are required. The efficiency and minimum number of measurements from which a breeding index can be maintained is of critical importance.
- Embodiments of the present invention are directed to a method and system for predicting an estimation of breeding value or genetic merit of an animal, as set out in the appended claims, based on meat trait parameters such as eating quality, commercial yield and corresponding animal characteristics.
- the invention provides a computer implemented method to predict an estimation of a breeding value or genetic merit of an animal, comprising:
- trait parameters are selected from at least one of meat-eating quality parameter and/or a commercial yield parameter;
- the generated model By configuring the generated model to take account of the ancestry information of each animal allows for a more computationally accurate model to be generated to enable an accurate prediction of the breeding value of an animal.
- no system has been developed to accurately predict the estimated breeding value of an animal.
- the invention achieves this by compensating the values to take account of the ancestry data to provide a more accurate prediction.
- an additive genetic variance and A as a numerator relationship matrix allows for the efficient identifying of particular traits associated with a particular animal in a simple and effective way.
- the matrix generation enables a much simpler solution to output an accurate predictor of the breeding value or genetic merit of an animal compared to other systems and methodologies.
- the generated model normalizes all the non-genetic effects leaving the genetic component of the trait as the only variance.
- the method begins with generating a database comprising meat-eating quality parameters and corresponding animal characteristics, where meat-eating quality parameters comprise tenderness, chewiness, juiciness and flavour. These quality parameters are chosen to be non- antagonistic to carcass traits such as confirmation and fat class within the Irish Bovine herd.
- the step of inputting the informative database to apply breeding values on eating quality traits for an entire national breeding index, for example Irish National Breeding Index.
- Another database containing yield cut data for specific commercial cuts can be used to provide the necessary models allowing for the generation of real-time yield data.
- a statistical model is generated based on trait data in said generated database.
- the trait can be at least one of meat-eating quality parameter and/or a commercial yield parameter.
- the statistical model is compensated at least for genetic variance of said animals and/or nuisance effects (e.g. environmental).
- a predicted estimate of the breeding value or genetic merit of the animal is outputted based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance.
- the system for estimation of breeding value of an animal comprises a database, where the data base comprises one or more of carcass traits, animal trait parameters and corresponding animal characteristics.
- the system further comprises a processor operatively coupled to the database.
- the processor is configured to generate a statistical model based on the traits in said generated database and compensate the generated statistical model at least for genetic variance of animals wherein the genetic variance of the animal comprises ancestry information of one or more animals.
- the processor is configured to estimate, the Estimated Breeding Value (EBV) of the animal based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance.
- EBV Estimated Breeding Value
- the system provides ranking of animals based on an index value which is in turn a weighted function of the animal traits or characteristics. Also, using the system a consumer or breeder may choose an animal with an appropriate index value having the balance of desired traits or characteristics.
- the trait of an animal may be predicted based on the method and system above and a breeder or a consumer may choose to breed or consume the desired animal with the desired meat-eating quality or optimal commercial yield.
- the degree of confidence of estimation of the breeding value is generated relative to a base population of informative ancestors of the animal informed genomic sequences verifying the ancestry of the animal.
- a computer program product stored in a non-transitory storage medium, said storage medium operatively coupled to a processor and said computer program product causing the processor to carry out the method of any of claims described herein.
- FIG. 1 exemplarily illustrates a method for estimation of breeding value of an animal
- FIG. 2 is a functional block diagram illustrating the primary components of an apparatus for estimation of breeding value of an animal
- FIG. 3 is a flow chart illustrating an exemplary embodiment according to one aspect of the invention.
- FIG. 1 exemplarily illustrates a method for estimation of breeding value of an animal.
- the method begins with generating 101 a database comprising one or more traits such as meat-eating quality parameters and commercial yield parameters. For example, each entry in the database of animals is rated for their meat tenderness, chewiness, juiciness and flavour.
- a statistical model is individually generated 102 based on meat traits in said generated database.
- a statistical model for determining the trait accounts for the influence of gender of the animal, age of the animal, heterosis, farm date and panel data.
- the statistical model is compensated 103 for genetic variance of the animals.
- the genetic variance of the animal comprises ancestry information of each animal.
- A an additive genetic variance and A as a numerator relationship matrix allows for the efficient identifying of particular traits associated with a particular animal.
- the matrix generation enables a much simpler solution to output an accurate predictor of the breeding value or genetic merit of an animal.
- the statistical model may be compensated for other genetic or environmental aspects, which affect the trait of the animal.
- trait-i, trait2, ... trait n are various animal characteristics
- the first trait may be feed conversion and the second trait may be eating quality the third trait may be yield and the above traits may be weighted for a ranking based on the desired eating quality outcome.
- eating quality is the desired eating quality more weight is attached to the eating quality trait in comparison to other traits such that the animals may be ranked in accordance with the eating quality as desired.
- FIG. 2 is a functional block diagram illustrating the primary components of an apparatus for estimation of breeding value of an animal.
- the system for estimation of breeding value of an animal comprises an informative database 201 comprising meat traits and corresponding animal characteristics where meat traits comprise traits such as eating quality and commercial yield.
- the informative database can be built using information relating to a herd, for example a min 10 animals from the same farm, same date, same gender criteria etc.
- the system further comprises a processor 202 operatively coupled to the database 201.
- the processor 202 is configured to generate a statistical model based on a combination of trait data in said generated database 201 and compensate the generated statistical model at least for genetic variance of animals.
- a statistical model for determining eating quality is based on gender of the animal, age of the animal, heterosis, farm date and panel date. Further, the statistical model may be compensated for genetic or environmental aspects, which affect the meat-eating quality of the animal.
- the processor 202 is configured to estimate, breeding value of the animal based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance. Further, the degree of confidence of estimation of the breeding value is generated, by the processor 202, relative to a base population of informative ancestors of the animal.
- the processor 202 is configured for providing ranking of animals based on an index value which is in turn a weighted function of the animal traits or characteristics.
- index value wrtraiti + W2-trait2 + W3-trait3 + W4-trait4 + ... + w n trait n where, wi, W2, ... w n are predetermined weights;
- trait-i, trait2, ... trait n are various animal characteristics.
- the first trait may be feed conversion and the second trait may be eating quality a third trait may be commercial yield and the above traits may be weighted for a ranking based on the desired trait.
- the desired trait more weight is attached to the eating quality trait in comparison to other traits such that the animals may be ranked in accordance with the eating quality as desired.
- the meat-eating quality of an animal may be predicted based on the method and system above and a breeder or a consumer may choose to breed or consume the desired animal with the desired meat-eating quality.
- a memory 203 may be operably coupled to the processor 202.
- the memory 203 stores a computer program product, which causes the processor to carry out the above recited method steps or the functions of the system disclosed above.
- FIG. 3 is a flow chart illustrating an exemplary embodiment according to one aspect of the invention.
- a predictive model employed for Bovine Animal Ranking based on desired traits such as eating quality or commercial yield. It will be appreciated that other animals such as sheep, chickens, pigs and the like can be estimated in accordance with the invention.
- the starting point as shown in FIG. 3 for eating quality is inputting an existing representative dataset from a trained sensory panel, comprising a number of people (preferably six or more) and eating quality assessments from animals identified and sourced to reflect the modern-day germplasm used on a typical farm.
- the animal’s pedigree is confirmed from a captured genomic sequence, as well as any nuisance effects.
- the traits are defined as follows: Eating Quality; Tenderness; Juiciness, Chewiness and/or Beef Flavour.
- the starting point as shown in FIG. 3 for eating quality is inputting an existing representative dataset of a trained sensory panel consisting of one panellist and eating quality assessments from animals identified and sourced to reflect the modern-day germplasm used on a typical farm.
- the animal’s pedigree is confirmed from a captured genomic sequence as well as any nuisance effects.
- the traits are defined as follows: Eating Quality; Tenderness; Juiciness, chewiness and/or Meat flavour.
- the starting point as shown in FIG. 3 for commercial yield prediction is inputting existing commercial yield data from processed animals in either untrimmed greenweight form or trimmed to a commercial specification, identified and sourced to reflect the modern-day germplasm used on a typical farm.
- An updated and expanded database, where the Trait(s) reflecting meat-eating quality, can be defined and stored.
- the traits can be defined as follows: a range from 1 to 100 commercial cuts to a defined commercial specification.
- Unique coefficients representing a herd of cattle for example an Irish herd, can be generated and are used in the statistical model.
- the contribution of genetic differences to the observed variability (i.e. , heritability) is quantified in the sample population.
- the data, trait definition, statistical model and estimated variance components are used to estimate measures of genetic merit for individuals and the relatives using of the animal.
- the estimated genetic merit (estimated breeding value) as well as the estimated degree of confidence in these predictions (i.e. reliability) are generated and re-based to be relative to a base population of informative ancestors.
- the ancestor information relating to an animal can be used as a genetic variance model configured to compensate the generated statistical model.
- the genetic variance can be gathered information relating to the animals parents, grandparents, siblings or relations and used to compensate the statistical model to provide a more accurate predicted value of the estimate breeding value of an animal, as set out below in more detail.
- a weight i.e. monetary or otherwise
- An important aspect to the invention is the ability to build the most pertinent and parsimonious statistical model to allow for generation of both the estimates of the necessary contribution of the random effects to the underlying variability but also the coefficients on each of the fixed and random effects. This requires not only the large unique dataset globally on sensory information coupled with information on the likely contributing features, but also in-depth knowledge and experience on the effects (and, where relevant their interactions) that associate with the different sensory traits. A similar approach can be adopted for yield where a large commercial yield dataset is required to develop the yield statistical model
- genomic evaluations for each sensory trait using a highly informative genotype panel bespoke to the cattle herd in Ireland which includes DNA variants of particular informativeness to Ireland including all known DNA variants in genes for the purpose of verifying animal parentage or ancestry thereby improving the accuracy of the prediction.
- the genomic evaluations for one or more sensory traits can be used when calculating the additive genetic variance below.
- HSD is the fixed effect of herd-by-date of slaughter
- the DL is the fixed effect of date-by-location of sensory analysis,
- the DL can be the herd number and the date of slaughter joined together so the model knows that all animals slaughtered from that farm on that day can be treated the same,
- GENDER is the gender of the animal (i.e. , bull, steer or heifer),
- a is the additive genetic effect (N(0,Aaa2)
- aa2 is the additive genetic variance and A is the numerator relationship matrix
- N(0,lae2) with ae2 representing the residual variance and I an identity matrix.
- a key differentiator is that the inclusion the additive genetic variance (aa2) having ancestry information and A as the numerator relationship matrix allows for a more accurate prediction of an estimation of a breeding value or genetic merit of an animal.
- the predicted breeding value or genetic merit of the animal enables a consumer or breeder is thereby to choose an animal with an appropriate index value having the desired traits or characteristics.
- e to factor in the residual variance can be optionally included to further refine the accuracy when outputting the predicted estimate of breeding value.
- a repeatability model can also be used where a permanent environmental effect is included as a random effect where N(0,l sRE2) with sRE2 being the permanent environmental variance and I the identity matrix.
- ear tag numbers of animals are entered into a system, the animals may exist in a feedlot or lairage or may even have just been slaughtered.
- the system confirms, through a database, that animal identification is correct and fetches the pre-generated measures of genetic merit, as well as ancillary information (e.g. gender, number of movements), for that animal. Animals are then ranked in accordance with the above methodology.
- the benefit of the invention is to facilitate differentiation of animals genetically divergent for a range of traits combined or individually.
- the apparatus described in the present disclosure may be implemented in hardware, firmware, software, or any combination thereof.
- the processing units, or processors(s) or controller(s) may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
- ASICs application specific integrated circuits
- DSPs digital signal processors
- DSPDs digital signal processing devices
- PLDs programmable logic devices
- FPGAs field programmable gate arrays
- processors controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
- software codes may be stored in a memory and executed by a processor.
- Memory may be implemented within the processor unit or external to the processor unit.
- memory refers to any type of volatile memory or nonvolatile memory.
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GB2117625.0A GB2599289A (en) | 2019-05-14 | 2020-05-14 | A method and system for estimation of the breeding value of an animal for eating quality and/or commercial yield prediction |
AU2020275244A AU2020275244A1 (en) | 2019-05-14 | 2020-05-14 | A method and system for estimation of the breeding value of an animal for eating quality and/or commercial yield prediction |
EP20729957.9A EP3970092A1 (en) | 2019-05-14 | 2020-05-14 | A method and system for estimation of the breeding value of an animal for eating quality and/or commercial yield prediction |
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Cited By (1)
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CN112862076A (en) * | 2021-02-25 | 2021-05-28 | 江苏兴牧农业科技有限公司 | Breeding method, model and breeding system for increasing qualified egg number of yellow-feather broiler breeder |
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- 2020-05-14 AU AU2020275244A patent/AU2020275244A1/en active Pending
- 2020-05-14 EP EP20729957.9A patent/EP3970092A1/en active Pending
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- 2020-05-14 GB GB2117625.0A patent/GB2599289A/en not_active Withdrawn
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US20170362655A1 (en) | 2002-12-31 | 2017-12-21 | Cargill, Incorporated | Methods and systems for inferring bovine traits |
WO2005078133A2 (en) * | 2004-02-09 | 2005-08-25 | Monsanto Technology Llc | Marker assisted best linear unbiased predicted (ma-blup): software adaptions for practical applications for large breeding populations in farm animal species |
WO2005078144A1 (en) | 2004-02-13 | 2005-08-25 | Audi Ag | Method for producing a component by reshaping a plate, and device for carrying out said method |
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CN112862076B (en) * | 2021-02-25 | 2024-04-09 | 江苏兴牧农业科技有限公司 | Breeding method, model and breeding system for improving qualified egg number of yellow feather broiler breeder |
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AU2020275244A1 (en) | 2022-01-20 |
IE20200299A2 (en) | 2022-08-03 |
GB2599289A (en) | 2022-03-30 |
GB202117625D0 (en) | 2022-01-19 |
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