Homozygosity Analysis in Autoimmunity Affected Individuals and Multiplex Autoimmune Disease Families

Abstract

Autoimmune diseases (AD) are responsible for a substantial amount of disability and morbidity worldwide. Research generally focuses on a single disease, although autoimmune phenotypes could represent pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms. This report examined the effect and importance of the homozygosity status, using genome-wide interspersed markers, in individuals and multiplex families affected with AD. This study presented two approaches: (I) a case-control comparison and evaluation on the effect of homozygosity at the genome-wide level and per marker, including 453 unrelated individuals (121 late-, 79 early-onset AD, 40 polyautoimmunity (PolyA), 30 multiple autoimmune syndrome (MAS) and 183 healthy control individuals); and (II) a model-free affected pair linkage approach which included 35 MAS, 49 polyA, 104 late-, and 83 early-onset multiplex families. A total of 372 genome-wide markers were used in the analysis. The standardized observed homozygosity (S OH ) was calculated and the association of the homozygosity status and the autoimmune trait was evaluated. The multipoint model-free linkage analysis was applied by using RELPAL from S.A.G.E v6.3. Results for the S OH showed significant differences between controls and early-onset individuals, where early-onset affected individuals showed lower homozygosity relative to controls. No differences were observed relative to controls for MAS, polyA and late-onset disease at the genome-wide level. The local marker homozygosity effect showed share and specific risk and/or protective effects for 24 markers. The model-free affected pair linkage approach lacked any suggestive linkage signals, but marginal signals displayed excess allele sharing for extreme phenotypes in autoimmunity. This study presumed autoimmunity as a trait rather than a clinical phenotype and tried to approach AD as a continuous trait presenting extreme phenotypes. Future approaches would be expected to dwell on the data presented here to corroborate and expand on sample size, marker coverage and their effects.