Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

Tyler, Anna L.; Kassaby, Baha El; Kolishovski, Georgi; Emerson, J. J.; Wells, Ann; Mahoney, James; Carter, Gregory W.

doi:10.1093/g3journal/jkab131

“…Quantile-quantile plots for longitudinal cocaine IVSA showed deviations from normality ( Figure S1E ), reflecting longer linkage disequilibrium blocks in the HMDP compared to human, as well as the longitudinal nature of the phenotypes. 29 …”

Section: Resultsmentioning

confidence: 99%

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice

Ah

¹

,

Bagley

²

,

LaPierre

³

et al. 2023

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“…More elaborate descriptions of population structure, husbandry and phenotyping methods can be found in Svenson et al . (2012) and Tyler et al . (2021).…”

Section: Methodsmentioning

confidence: 97%

“…On average, the animals were related to each other at a level equivalent to first cousins, which is by design (Svenson et al 2012). More elaborate descriptions of population structure, husbandry and phenotyping methods can be found in Svenson et al (2012) and Tyler et al (2021).…”

Section: Phenotypesmentioning

confidence: 99%

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Perez

¹

,

Bink

²

,

Svenson

³

et al. 2022

Preprint

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Recent developments allowed generating multiple high quality ‘omics’ data that could increase predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here we have assessed the performance of parametric and non-parametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using best linear unbiased prediction (BLUP), while non-parametric models were implemented using the gradient boosting machine algorithm (GBM). We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While GBM models captured more phenotypic variation, their predictive performance did not exceed the BLUP models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the GBM model was able to outperform BLUP for most traits except body weight, but the same pattern was not observed when using both SNP genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

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“…On average, the animals were related to each other at a level equivalent to first cousins, which is by design ( Svenson et al 2012 ). More elaborate description of population structure, husbandry and phenotyping methods can be found in Svenson et al (2012) and Tyler et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Perez

¹

,

Bink

²

,

Svenson

³

et al. 2022

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

Recent developments allowed generating multiple high quality ‘omics’ data that could increase predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here we have assessed the performance of parametric and non-parametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using best linear unbiased prediction (BLUP), while non-parametric models were implemented using the gradient boosting machine algorithm (GBM). We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While GBM models captured more phenotypic variation, their predictive performance did not exceed the BLUP models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the GBM model was able to outperform BLUP for most traits except body weight, but the same pattern was not observed when using both SNP genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

show abstract

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

Cited by 4 publications

References 40 publications

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

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