William D. Beavis is professor and GF Sprague Chair for Population Genetics in the Department of Agronomy at Iowa State University. His research focuses on statistical genetics and ways to optimize plant breeding. He is known for discovering what has since become known as the Beavis effect: namely, that the estimates of phenotypic variance associated with one of multiple quantitative trait loci, each of which has a small effect on the trait being studied, are typically significantly inflated if the sample size of organisms in the study is too low (e.g. about 100), but that these estimates are fairly accurate if the number of individuals is much greater (about 1,000).
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| - William D. Beavis is professor and GF Sprague Chair for Population Genetics in the Department of Agronomy at Iowa State University. His research focuses on statistical genetics and ways to optimize plant breeding. He is known for discovering what has since become known as the Beavis effect: namely, that the estimates of phenotypic variance associated with one of multiple quantitative trait loci, each of which has a small effect on the trait being studied, are typically significantly inflated if the sample size of organisms in the study is too low (e.g. about 100), but that these estimates are fairly accurate if the number of individuals is much greater (about 1,000). (en)
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| - Expression of Nuclear-Cytoplasmic Interactions on Quantitatively Inherited Traits from Interspecific Matings of Oat Species (en)
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| - William D. Beavis is professor and GF Sprague Chair for Population Genetics in the Department of Agronomy at Iowa State University. His research focuses on statistical genetics and ways to optimize plant breeding. He is known for discovering what has since become known as the Beavis effect: namely, that the estimates of phenotypic variance associated with one of multiple quantitative trait loci, each of which has a small effect on the trait being studied, are typically significantly inflated if the sample size of organisms in the study is too low (e.g. about 100), but that these estimates are fairly accurate if the number of individuals is much greater (about 1,000). (en)
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