Naam
Naam I Jibrila MSc
RoepnaamIbrahim
Emailibrahim.jibrila@wur.nl

Werk
OmschrijvingPromovendus
OrganisatieDepartement Dierwetenschappen
OrganisatieeenheidFokkerij en Genomica
Telefoon+31 317 483 198
Mobiel+31 6 41287790
Telefoon secretariaat+31 317 482 335
Telefoon 2
Fax
Notitie voor telefonist
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BezoekadresDroevendaalsesteeg 1
6708PB, WAGENINGEN
Gebouw/Kamer107/E0.197
PostadresPostbus 338
6700AH, WAGENINGEN
Bodenummer27
OmschrijvingPromovendus
OrganisatieWageningen Livestock Research
OrganisatieeenheidFokkerij & Genomica
Telefoon+31 317 483 198
Mobiel+31 6 41287790
Telefoon secretariaat+31 317 483 953
Telefoon 2
Fax
Notitie voor telefonist
Notitie door telefonist
BezoekadresDroevendaalsesteeg 1
6708PB, WAGENINGEN
Gebouw/Kamer107/E0.197
PostadresPostbus 338
6700AH, WAGENINGEN
Bodenummer27
Nevenwerkzaamheden
  • No ancillary activities - No ancillary activities
    jan 2021 - Nu


Biografie

Expertiseprofiel
Expertise
Sociale media
  Ibrahim Jibrila op Linkedin
  Ibrahim Jibrila op ResearchGate
  Ibrahim Jibrila op Twitter

Publicaties
Onderzoeker ID's

Projecten

Investigating the impact of pre-selection on accuracy and bias of genomic prediction models

Because it is expensive to genetically evaluate all potential selection candidates, breeding companies pre-select a proportion of the young animals born and raise them to reach the final genetic evaluation age. Pre-selection is usually not accounted for in the final genetic evaluation because information on the pre-culled animals is usually not kept, as the pre-culled animals do not contribute offspring to the subsequent generations.

In dairy cattle breeding programmes, not accounting for pre-selection in the final genetic evaluation has been shown to affect accuracy and bias of breeding value prediction models. Just like in dairy cattle, pre-selection also takes place in pig and poultry breeding programmes. Because of differences in reproduction and structure of breeding programmes in dairy cattle on one hand and pigs or poultry on the other hand, impact of not accounting for pre-selection in the final genetic evaluation may be different among these species. However, this has not been investigated.

The current method of alleviating the impact of pre-selection on performance of breeding value prediction models is including information from all selection candidates, including the pre-culled ones, in the final genetic evaluation. Single Step Genomic Best Linear Unbiased Prediction (ssGBLUP) has been shown to remove the impact of pre-selection to some extent. The way it is able to do this is however poorly understood.

Different sources of information are used in pre-selection. In Parent Average Pre-Selection, Phenotypic Pre-Selection and Genomic Pre-Selection, parental average EBV, phenotypes measurable early in life, and GEBVs are respectively used in pre-selecting young animals. Available literature on the impact of pre-selection on performance of breeding value prediction models is focusing mainly on GPS. The greater interest in GPS may be because it is more popular, and its popularity may be due to i) the fact that genotyping is becoming cheaper by the day, ii) the reasonable reliabilities of GEBVs even at young ages, which also suggest that GPS has a higher impact compared to other forms of pre-selection and iii) the fact that GEBVs can differentiate among full-sibs even at birth, unlike PA.

The aim of this research is therefore to investigate the impact of different forms of pre-selection on accuracy and bias of ssGBLUP in pigs and poultry breeding programmes. How different components of ssGBLUP are impacted by pre-selection, and how the impact can be removed without including information from pre-culled animals will also be investigated. To achieve the aim, data on selection decisions will be simulated for a breeding programme that typifies pig and poultry breeding programmes. GEBVs of some of the animals in the dataset will be estimated using ssGBLUP, with and without pre-selection and compared with TBV, to investigate the impact of pre-selection on accuracy and bias of ssGBLUP. In the following step, theoretical work will be done to understand how pre-selection impacts different components of ssGBLUP, and find way(s) of removing the impacts without including information from pre-culled animals. Finally, empirical data from breeding companies will be used to validate the newly developed model.


Onderwijs

Bachelor of Agriculture (Animal Science); Usmanu Danfodiyo University, Sokoto, Nigeria; 2008

MSc Animal Sciences (Animal Breeding and Genetics), Wageningen University and Research, 2017

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