Reconstructing sibling relationships in wild populations.
Reconstruction of sibling relationships from genetic data is an important component of many biological applications. In particular, the growing application of molecular markers (microsatellites) to study wild populations of plant and animals has created the need for new computational methods of establishing pedigree relationships, such as sibgroups, among individuals in these populations. Most current methods for sibship reconstruction from microsatellite data use statistical and heuristic techniques that rely on a priori knowledge about various parameter distributions. Moreover, these methods are designed for data with large number of sampled loci and small family groups, both of which typically do not hold for wild populations. We present a deterministic technique that parsimoniously reconstructs sibling groups using only Mendelian laws of inheritance. We validate our approach using both simulated and real biological data and compare it to other methods. Our method is highly accurate on real data and compares favorably with other methods on simulated data with few loci and large family groups. It is the only method that does not rely on a priori knowledge about the population under study. Thus, our method is particularly appropriate for reconstructing sibling groups in wild populations.
Berger-Wolf
Tanya Y
TY
Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu
Sheikh
Saad I
SI
DasGupta
Bhaskar
B
Ashley
Mary V
MV
Caballero
Isabel C
IC
Chaovalitwongse
Wanpracha
W
Putrevu
S Lahari
SL
D000818:Animals; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D005819:Genetic Markers / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods; D005828:Genetics, Population; D040941:Heredity / Q000235:genetics*; D006801:Humans; D018895:Microsatellite Repeats / Q000235:genetics*; D008957:Models, Genetic; D010375:Pedigree; D019655:Quantitative Trait, Heritable; D035781:Siblings
D005819:Genetic Markers