title: Relationship inference from the genetic data on parents or offspring: A comparative study.
abstract: Relationship inference in a population is of interest for many areas of research from anthropology to genetics. It is possible to directly infer the relationship between the two individuals in a couple from their genetic data or to indirectly infer it from the genetic data of one of their offspring. For this reason, one can wonder if it is more advantageous to sample couples or single individuals to study relationships of couples in a population. Indeed, sampling two individuals is more informative than sampling one as we are looking at four haplotypes instead of two, but it also doubles the cost of the study and is a more complex sampling scheme. To answer this question, we performed simulations of 1000 trios from 10 different relationships using real human haplotypes to have realistic genome-wide genetic data. Then, we compared the genome sharing coefficients and the relationship inference obtained from either a pair of individuals or one of their offspring using both single-point and multi-point approaches. We observed that for relationships closer than 1st cousin, pairs of individuals were more informative than one of their offspring for relationship inference, and kinship coefficients obtained from single-point methods gave more accurate or equivalent genome sharing estimations. For more remote relationships, offspring were more informative for relationship inference, and inbreeding coefficients obtained from multi-point methods gave more accurate genome sharing estimations. In conclusion, relationship inference on a parental pair or on one of their offspring provides complementary information. When possible, sampling trios should be encouraged as it could allow spanning a wider range of potential relationships.
authors.affiliation: Inserm, UMR 1137, IAME, Paris, France; Université Paris Diderot, Sorbonne Paris Cité, UMR 1137, Paris, France; Plateforme de Génétique constitutionnelle-Nord (PfGC-Nord), Paris, France.
authors.forename: Steven
authors.initials: S
authors.lastname: Gazal
authors.affiliation: Inserm, UMR 1078, Brest, France; Université Bretagne Occidentale, Brest, France; Centre Hospitalier Régional Universitaire, Brest, France.
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Génin
authors.affiliation: Inserm, U946, Genetic Variation and Human Diseases Lab, Paris, France; Université Paris Diderot, Sorbonne Paris Cité, Institut Universitaire d'Hématologie, UMR 946, Paris, France. Electronic address: anne-louise.leutenegger@inserm.fr.
authors.forename: Anne-Louise
authors.initials: AL
authors.lastname: Leutenegger
mesh_terms: D003198:Computer Simulation; D003241:Consanguinity; D005787:Gene Frequency; D005819:Genetic Markers; D015894:Genome, Human; D006239:Haplotypes; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D008967:Molecular Biology / Q000379:methods*; D010375:Pedigree
chemical_list: D005819:Genetic Markers
keywords: Genome sharing; Identity-by-descent; Inbreeding coefficient; Kinship coefficient; Mating habit; Relationship inference