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Background Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD. Methods We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. Results We identified 2 candidate genetic interactions with PFDR <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. Conclusions In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

The sex-specific prevalence of adrenal diseases has been known for a long time. However, the reason for the high prevalence of these diseases in females is not completely understood. Mouse studies have shown that the adult adrenal gland is sexually dimorphic at different levels like transcriptome, histology, and cell renewal. Here we used RNA-seq to show that in prepubertal mice, male and female adrenal glands were not only sexually dimorphic but also responded differently to the same external stimulus. We previously reported that thyroid hormone receptor β1 (TRβ1) in the adrenal gland is mainly expressed in the inner cortex and the fate of this TRβ1-expressing cell population can be changed by thyroid hormone (T3) treatment. In the present study, we found that adrenal glands in prepubertal mice were sexually dimorphic at the level of the transcriptome. Under T3 treatment, prepubertal females had 1,162 genes differentially expressed between the saline and T3 groups, whereas in males of the same age, only 512 genes were T3-responsive. Immunostaining demonstrated that several top sexually dimorphic T3-responsive genes, including Cyp2f2 and Dhcr24, were specifically expressed in the adrenal inner cortex, precisely in an area partially overlapping with the X-zone. Under T3 treatment, a unique cortical layer that surrounds the adrenal X-zone expanded significantly, forming a distinct layer peculiar to females. Our findings identified novel marker genes for the inner adrenal cortex, indicating there are different sub-zones in the zona fasciculata. The results also highlight the sex-specific response to thyroid hormone in the mouse adrenal gland.

Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer’s disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong’s test, p  <  0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction.