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PILRA (rs1859788 A > G) has been suggested to be a protective variant for Alzheimer’s disease (AD) and is an entry co-receptor for herpes simplex virus-1. We conducted a nested case–control study of 360 1:1-matched AD subjects. Interactions between the PILRA-A allele, APOE risk variants (ε3/ε4 or ε4/ε4) and GM17 for AD risk were modelled. The associations were cross-validated using two independent whole-genome sequencing datasets. We found negative interactions between PILRA-A and GM17 (OR 0.72, 95% CI 0.52–1.00) and between PILRA-A and APOE risk variants (OR 0.56, 95% CI 0.32–0.98) in the discovery dataset. In the replication cohort, a joint effect of PILRA and PILRA × GM 17/17 was observed for the risk of developing AD (p .02). Here, we report a negative effect modification by PILRA on APOE and GM17 high-risk variants for future AD risk in two independent datasets. This highlights the complex genetics of AD.

Additional file 1. Sweden population statistics.

Additional file 2. Flow chart of the Betula cohort and anti-HSV IgM seropositivity in the different samples

Additional file 4: Table S1. List of serum metabolites that were analyzed and used in further analysis. 1Only sum of THM and mTHM were included in the analysis as ‘total folate’. 2The sums of the Asparagine and Aspartic acid and the f Glutamine and Glutamic acid levels were used in statistical analysis because of conversion between this forms in the stored samples. In the whole 45 biomarkers could be identified and included in the analysis.

Additional file 5: Table S2. Differences of biomarkers levels between mothers of children with infantile autism and Asperger’s syndrome.

Additional file 5: Table S2. Differences of biomarkers levels between mothers of children with infantile autism and Asperger’s syndrome.

Additional file 4: Table S1. List of serum metabolites that were analyzed and used in further analysis. 1Only sum of THM and mTHM were included in the analysis as ‘total folate’. 2The sums of the Asparagine and Aspartic acid and the f Glutamine and Glutamic acid levels were used in statistical analysis because of conversion between this forms in the stored samples. In the whole 45 biomarkers could be identified and included in the analysis.

Additional file 3: Figure S3. Biomarker levels in each study participant, with a hierarchical cluster analysis based on Euclidian distances with complete linkage. Each column represents a participant and each row a biomarker. Heat map colors represent standardized log-transformed biomarker levels in all participants (red) and below mean biamarker levels (blue).

Additional file 3: Figure S3. Biomarker levels in each study participant, with a hierarchical cluster analysis based on Euclidian distances with complete linkage. Each column represents a participant and each row a biomarker. Heat map colors represent standardized log-transformed biomarker levels in all participants (red) and below mean biamarker levels (blue).

Sectors and affiliations of the interviewees in the five participating countries. (PDF 436 kb)