To be or not to be associated: power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies

Elise Vaumourin (1, 2) , Gwenaël Vourc’h (1) , Sandra Telfer (3) , Xavier Lambin (3) , Diaeldin Salih (4) , Ulrike Seitzer (5) , Serge Morand (6, 7) , Nathalie Charbonnel (8) , Muriel Taussat (2) , Patrick Gasqui (1)

1 UR EpiA - Unité de Recherche d'Épidémiologie Animale
2 BIPAR - Biologie moléculaire et immunologie parasitaires et fongiques
3 APERU - School of Biological Sciences
4 Department of Ticks and Tick-borne Diseases
5 Division of Veterinary-Infection Biology and Immunology
6 UMR ISEM - Institut des Sciences de l'Evolution de Montpellier
7 UPR AGIRs - Animal et gestion intégrée des risques
8 UMR CBGP - Centre de Biologie pour la Gestion des Populations

A growing number of studies are reporting simultaneous infections by parasites in many different hosts. The detection of whether these parasites are significantly associated is important in medicine and epidemiology. Numerous approaches to detect associations are available, but only a few provide statistical tests. Furthermore, they generally test for an overall detection of association and do not identify which parasite is associated with which other one. Here, we developed a new approach, the association screening approach, to detect the overall and the detail of multi-parasite associations. We studied the power of this new approach and of three other known ones (i.e., the generalized chi-square, the network and the multinomial GLM approaches) to identify parasite associations either due to parasite interactions or to confounding factors. We applied these four approaches to detect associations within two populations of multi-infected hosts: (1) rodents infected with Bartonella sp., Babesia microti and Anaplasma phagocytophilum and (2) bovine population infected with Theileria sp. and Babesia sp. We found that the best power is obtained with the screening model and the generalized chi-square test. The differentiation between associations, which are due to confounding factors and parasite interactions was not possible. The screening approach significantly identified associations between Bartonella doshiae and B. microti, and between T. parva, T. mutans, and T. velifera. Thus, the screening approach was relevant to test the overall presence of parasite associations and identify the parasite combinations that are significantly over- or under-represented. Unraveling whether the associations are due to real biological interactions or confounding factors should be further investigated. Nevertheless, in the age of genomics and the advent of new technologies, it is a considerable asset to speed up researches focusing on the mechanisms driving interactions between parasites.

Mots clés: chi-square test; network model; GLM approach; screening; parasite community; modeling; interactions; associations

Domaines: Sciences agricoles; Biologie animale; Microbiologie et Parasitologie; Médecine humaine et pathologie

Elise Vaumourin, Gwenaël Vourc’h, Sandra Telfer, Xavier Lambin, Diaeldin Salih, et al.. To be or not to be associated: power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies. Frontiers in Cellular and Infection Microbiology, 2014, 4, pp.1-11. ⟨10.3389/fcimb.2014.00062⟩. ⟨hal-01138870⟩