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Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations.

ABSTRACT:

Key message

We propose a criterion to predict genomic selection efficiency for structured populations. This criterion is useful to define optimal calibration set and to estimate prediction reliability for multiparental populations. Genomic selection refers to the use of genotypic information for predicting the performance of selection candidates. It has been shown that prediction accuracy depends on various parameters including the composition of the calibration set (CS). Assessing the level of accuracy of a given prediction scenario is of highest importance because it can be used to optimize CS sampling before collecting phenotypes, and once the breeding values are predicted it informs the breeders about the reliability of these predictions. Different criteria were proposed to optimize CS sampling in highly diverse panels, which can be useful to screen collections of genotypes. But plant breeders often work on structured material such as biparental or multiparental populations, for which these criteria are less adapted. We derived from the generalized coefficient of determination (CD) theory different criteria to optimize CS sampling and to assess the reliability associated to predictions in structured populations. These criteria were evaluated on two nested association mapping (NAM) populations and two highly diverse panels of maize. They were efficient to sample optimized CS in most situations. They could also estimate at least partly the reliability associated to predictions between NAM families, but they could not estimate differences in the reliability associated to the predictions of NAM families using the highly diverse panels as calibration sets. We illustrated that the CD criteria could be adapted to various prediction scenarios including inter and intra-family predictions, resulting in higher prediction accuracies.

SUBMITTER: Rincent R

PROVIDER: S-EPMC5641287 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations.

Rincent R R Charcosset A A Moreau L L

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20170809 11

<h4>Key message</h4>We propose a criterion to predict genomic selection efficiency for structured populations. This criterion is useful to define optimal calibration set and to estimate prediction reliability for multiparental populations. Genomic selection refers to the use of genotypic information for predicting the performance of selection candidates. It has been shown that prediction accuracy depends on various parameters including the composition of the calibration set (CS). Assessing the l ...[more]

PMID: 28795202

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Project description:PurposeIn some patients with Hodgkin lymphoma (HL), proton beam therapy (PBT) may reduce the risk of radiation-related cardiovascular disease (CVD) and second cancers (SC) compared with photon radiation therapy (RT). Our aim was to identify patients who benefit the most from PBT in terms of predicted 30-year absolute mortality risks (AMR30) from CVD and SC, taking into account individual background, chemotherapy, radiation, and smoking-related risks.Methods and materialsEighty patients with supradiaphragmatic HL treated with PBT between 2015 and 2019 were replanned using optimal photon RT. To identify patients predicted to derive the greatest benefit from PBT compared with photon RT, doses and AMR30 from CVD and SC of the lung, breast, and esophagus were compared for all patients and across patient subgroups.ResultsFor patients with mediastinal disease below the origin of the left main coronary artery (n = 66; 82%), PBT reduced the mean dose to the heart, left ventricle, and heart valves by 1.0, 2.7, and 3.6 Gy, respectively. Based on U.S. mortality rates, PBT reduced CVD AMR30 by 0.2%, from 5.9% to 5.7%. The benefit was larger if the mediastinal disease overlapped longitudinally with the heart by ≥40% (n = 23; 29%). PBT reduced the mean dose to the heart, left ventricle, and heart valves by 3.2, 5.6, and 5.1 Gy, respectively, and reduced CVD AMR30 by 0.8%, from 7.0% to 6.2%. For patients with axillary disease (n = 25; 31%), PBT reduced the mean lung dose by 2.8 Gy and lung cancer AMR30 by 0.6%, from 2.7% to 2.1%. Breast and esophageal doses were also lower with PBT, but the effects on AMR30 were negligible. The effect of smoking on CVD and lung cancer AMR30 was much larger than radiation and chemotherapy and the differences between radiation modalities.ConclusionsThe predicted benefit of PBT is not universal and limited to certain categories of patients with lymphoma and lower mediastinal or axillary disease. Smoking cessation should be strongly encouraged in smokers who require thoracic RT.

Dataset Information

Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations.

Key message

Publications

Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations.

Similar Datasets

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