Abstract
OBJECTIVE As the mode of inheritance is often unknown for complex diseases, a MOD-score analysis, in which the parametric LOD score is maximized with respect to the trait-model parameters, can be a powerful approach in genetic linkage analysis. Because the calculation of the disease-locus likelihood is the most time-consuming step in a MOD-score analysis, we aimed to optimize this part of the calculation to speed up linkage analysis using the GENEHUNTER-MODSCORE software package. METHODS Our new algorithm is based on minimizing the effective number of inheritance vectors by collapsing them into classes. To this end, the disease-locus-likelihood contribution of each inheritance vector is represented and stored in its algebraic form as a symbolic sum of products of penetrances and disease-allele frequencies. Simulations were used to assess the speedup of our new algorithm. RESULTS We were able to achieve speedups ranging from 1.94 to 11.52 compared to the original GENEHUNTER-MODSCORE version, with higher speedups for larger pedigrees. When calculating p values, the speedup ranged from 1.69 to 10.36. CONCLUSION Computation times for MOD-score analysis, involving the evaluation of many tested sets of trait-model parameters and p value calculation, have been prohibitively high so far. With our new algebraic algorithm, such an analysis is now feasible within a reasonable amount of time.
| Dokumententyp: | Zeitschriftenartikel | 
|---|---|
| Publikationsform: | Publisher's Version | 
| Fakultät: | Medizin | 
| Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit | 
| URN: | urn:nbn:de:bvb:19-epub-57283-2 | 
| ISSN: | 0001-5652 | 
| Allianz-/Nationallizenz: | Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich. | 
| Sprache: | Englisch | 
| Dokumenten ID: | 57283 | 
| Datum der Veröffentlichung auf Open Access LMU: | 27. Aug. 2018 08:47 | 
| Letzte Änderungen: | 04. Nov. 2020 13:37 | 
 
		 
	 
    



