Beyond conventional statistics: Genomic Informational Field Theory (GIFT) identifies sex-specific herpes virus associations in multiple sclerosis

Background: Detecting higher order relationships in datasets of complex traits, such as multiple sclerosis (MS), has been challenging. Conventional statistics largely rely on comparing averages across groups and thereby discard important information on the underlying distribution of datapoints. The Genomic Information Field Theory (GIFT) overcomes this limitation by ranking individuals based on linear measures, for example immunoglobulin titres. The exact role of humoral immune responses against several human herpes viruses in a sex-dependent manner in MS is currently unknown. Materials and methods: We compared the performance of GIFT with conventional statistical frameworks to detect differences in the humoral immune response against 4 highly prevalent herpes viruses linked to an individuals susceptibility to develop MS in 200 MS patients and 137 healthy controls. Results: GIFT validated the well-known association that the Epstein Barr Virus (EBV) protein EBNA1 is strongly linked to MS susceptibility in both sexes. In contrast to conventional statistics, GIFT also identified association between herpes simplex virus, varicella zoster virus and the EBV VCA protein and female susceptibility to develop MS, whereas male MS susceptibility was only linked to CMV immunoglobulin levels. None of these associations was observed using conventional statistical tools. Conclusion and discussion: We here show for the first time that GIFT is able to detect novel associations in human immunoglobulin data linked to MS susceptibility, which remained undetected by conventional statistical frameworks. This shows the power of GIFT to detect complex phenotype-trait associations and underlying subgroups within populations.