VISUALIZATION OF INHERITANCE PATTERNS FROM GRAPHIC REPRESENTATION OF ADDITIVE AND DOMINANCE RELATIONSHIPS BETWEEN ANIMALS

Citation
Yc. Huang et Rd. Shanks, VISUALIZATION OF INHERITANCE PATTERNS FROM GRAPHIC REPRESENTATION OF ADDITIVE AND DOMINANCE RELATIONSHIPS BETWEEN ANIMALS, Journal of dairy science, 78(12), 1995, pp. 2877-2883
Citations number
18
Categorie Soggetti
Agriculture Dairy & AnumalScience","Food Science & Tenology
Journal title
ISSN journal
00220302
Volume
78
Issue
12
Year of publication
1995
Pages
2877 - 2883
Database
ISI
SICI code
0022-0302(1995)78:12<2877:VOIPFG>2.0.ZU;2-V
Abstract
Evaluation of pedigrees of normal and affected individuals help to sug gest possible patterns of inheritance. Because large numbers of indivi duals are involved in studies of genetic disease, classic two-dimensio nal family tree charts are difficult to draw. Instead, DFA plots of gr aduated circles, weighted by dominance relationships, inbreeding, and additive relationships between individuals can be plotted above, on, a nd below the diagonal. Base animals without phenotypic measurements co ntribute information for computation of approximate dominance relation ships, inbreeding, and additive relationships, but are not explicitly in the graph. Plotting only a set of randomly selected animals for eac h combination of phenotypes improves the visualization effect, especia lly when pedigrees are large or when computer resources are limited. F or deficiency of uridine monophosphate synthase and interdigital hyper plasia, DFA plots showed higher densities of additive and dominance re lationships among affected animals. However, DFA plots did not show cl ear patterns of inheritance for heel erosion, laminitis, or sole ulcer s because environmental effects were important for those traits. Group ed graduated circles also improved visualization. The function of DFA plots is comparable with statistical scatter plots that display data t o help examine statistical approaches. The DFA plots serve as a first approach to evaluate genetic hypotheses before a more complex model is fit.