PubMed · 1489621
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B N Løkken. 1992-11-16. [Many challenges as chief delegate. Interview by Siv Barstad].. https://pubmed.ncbi.nlm.nih.gov/1489621/
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This study presents a comprehensive evaluation of genomic selection (GS) in timothy (Phleum pratense L.), comparing nine prediction models across yield and quality traits at two Norwegian locations. Forward validation with independent full-sib (FS2) families revealed a substantial generalization gap, highlighting the need for realistic accuracy assessment in polyploid forage breeding. Timothy (Phleum pratense L.) is the most important forage grass in Northern Europe, yet genomic selection has not been systematically evaluated in this hexaploid species. We assessed 889 FS2-families originating from biparental crosses among 49 cultivars/populations. The FS2-families were genotyped with 30,698 SNP markers derived from genotyping-by-sequencing (GBS) and field tested for three harvest years at a highland and a lowland continental location in Southern Norway. Nine genomic prediction models were compared for six yield traits (dry matter yield per cut and total) and six quality traits (protein, digestibility, and fiber fractions) across three cuts/year. Within-training cross-validation accuracies were moderate to high (mean r = 0.62), with Random Forest and SVR consistently outperforming GBLUP. However, forward validation using 213 independent FS2-families revealed dramatically lower accuracies (mean r = 0.16), with only 16 of 30 trait-dataset combinations reaching statistical significance (p < 0.05). Genomic heritabilities (GREML), estimated across environments, ranged from near zero for the quality traits to 0.55 for the yield traits. Multi-trait models improved accuracy by 3-5% over single-trait approaches, while FS2 families-by-environment interaction models with Random Forest achieved the highest within-training accuracy (mean r = 0.71). Marker density analysis showed accuracy plateauing at approximately 15000 SNPs. Genetic correlations among the yield component traits were estimated by multi-trait REML; correlations among the quality traits could not be estimated reliably because their genomic heritabilities were low. A multi-trait selection index identified top-performing FS2-families for further crossing recommendations. These results provide a benchmark for GS implementation in hexaploid timothy and emphasize that cross-validation substantially overestimates prediction accuracy for truly independent material.
The biennial national statistical surveys of research and development expenditure, based on OECD guidelines, contribute to the knowledge base for Norwegian research policy. This article outlines the resources for research and development in the medical sciences, with emphasis on the higher education sector. This sector, which includes university hospitals, performs about 75% of medical research in Norway (medical research in industry not included) while the remaining 25% is performed in research institutes in the institute sector. In 1995, current expenditure for medical research amounted to 1,240 million Norwegian kroner, 930 million in the higher education sector and 310 million in the institute sector. From 1993 to 1995, there was a small increase in real terms in resources for medical research in universities and colleges paid for over institutional budgets, while there was no growth in externally funded research. Over the 25-year period 1970-1995, the growth in expenditures for medical research was higher than for the natural sciences, but lower than for the humanities and the social sciences. Comparisons between the Nordic countries show that investments in medical research in Norway is much lower than in Denmark or Sweden.
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