Maximum difference analysis: a new empirical method for genome-wide association studies

Paolo Ajmone Marsan, Ezequiel Luis Nicolazzi, Massimo Cellesi, Corrado Dimauro, Silvia Sorbolini, Giustino Gaspa, Nicolò Pietro Paolo Macciottaa

Risultato della ricerca: Contributo in rivistaArticolo in rivista

1 Citazioni (Scopus)


The availability of high-density single nucleotide polymorphism (SNPs) panels for humans and, recently, for several livestock species has given a great impulse to genome-wide association studies towards the identification of genes associated with complex traits and diseases. The fre- quentist and the Bayesian approach are commonly used to investigate marker associations with traits of interest. Briefly, the former is the most widely used method, being intuitive and easily to apply, whereas the latter requires deeper statistical knowledge, but has the advantage to include prior information to obtain a posterior probability of association. Both methods, how- ever, require parameters or distributions to be set a priori by the researcher. In this work, we suggest a new empirical method for genome-wide studies (GWAS), which verifies marker-trait associations using the bootstrap resampling and Chebyshev ’ s inequality. This method, called Maximum Difference Analysis (MDA), was tested on a real dataset of 2093 Italian Holstein bulls with the objective of finding associations between SNPs and milk, fat and protein yield and fat and protein percentage. Results of the MDA method were compared with those obtained to a genome-wide association analysis performed using the R package GenABEL. In addition, we assessed the bovine annotated genes related to the traits under study. The MDA method was able to locate known important loci for milk productive traits, such as the DGAT1 , PRLR , GHR and SCD . Moreover, some new putative candidate genes were detected. The python script of MDA procedure is available at
Lingua originaleEnglish
pagine (da-a)396-406
Numero di pagine11
Stato di pubblicazionePubblicato - 2016


  • GWAS, Bayesian analysis, genetics


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