Using molecular sizes of simple sequence repeats vs. discrete binned data in assessing probability of ancestry: application to maize hybrids.

Most inferential methods for profiling genotypes based upon the use of DNA fragments use molecular-size data transcribed into discrete bins, which are intervals of DNA fragment sizes. Categorizing into bins is labor intensive with inevitable arbitrariness that may vary between laboratories. We describe and evaluate an algorithm for determining probabilities of parentage based on raw molecular-size data without establishing bins. We determine the standard deviation of DNA fragment size and assess the association of standard deviation with fragment size. We consider a pool of potential ancestors for an index line that is a hybrid with unknown pedigree. We evaluate the identification of inbred parents of maize hybrids with simple sequence repeat data in the form of actual molecular sizes received from two laboratories. We find the standard deviation to be essentially constant over the molecular weight. We compare these results with those of parallel analyses based on these same data that had been transcribed into discrete bins by the respective laboratories. The conclusions were quite similar in the two cases, with excellent performance using either binned or molecular-size data. We demonstrate the algorithm's utility and robustness through simulations of levels of missing and misscored molecular-size data.

Assessing probability of ancestry using simple sequence repeat profiles: applications to maize inbred lines and soybean varieties.

Determining parentage is a fundamental problem in biology and in applications such as identifying pedigrees. Difficulties inferring parentage derive from extensive inbreeding within the population, whether natural or planned; using an insufficient number of hypervariable loci; and from allele mismatches caused by mutation or by laboratory errors that generate false exclusions. Many studies of parentage have been limited to comparisons of small numbers of specific parent-progeny triplets. There have been few large-scale surveys of candidates in which there is no prior knowledge of parentage. We present an algorithm that determines the probability of parentage in circumstances where there is no prior knowledge of pedigree and that is robust in the face of missing data and mistyped data. The focus is parentage of an inbred line having uncertain ancestry. The algorithm is a variation of a previously published hybrid-focused algorithm. We describe the algorithm and demonstrate its performance in determining parentage of 43 inbred varieties of soybean that have been profiled using 236 SSR loci and from seven inbred varieties of maize that were profiled using 70 SSR loci. We include simulations of additional levels of missing and mistyped data to show the algorithm's utility and flexibility.

Assessing probability of ancestry using simple sequence repeat profiles: applications to maize hybrids and inbreds.

Determination of parentage is fundamental to the study of biology and to applications such as the identification of pedigrees. Limitations to studies of parentage have stemmed from the use of an insufficient number of hypervariable loci and mismatches of alleles that can be caused by mutation or by laboratory error and that can generate false exclusions. Furthermore, most studies of parentage have been limited to comparisons of small numbers of specific parent-progeny triplets thereby precluding large-scale surveys of candidates where there may be no prior knowledge of parentage. We present an algorithm that can determine probability of parentage in circumstances where there is no prior knowledge of pedigree and that is robust in the face of missing data or mistyped data. We present data from 54 maize hybrids and 586 maize inbreds that were profiled using 195 SSR loci including simulations of additional levels of missing and mistyped data to demonstrate the utility and flexibility of this algorithm.