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Published in final edited form as: Neurobiol Aging. 2019 Sep 24;87:138.e7–138.e14. doi: 10.1016/j.neurobiolaging.2019.09.007

Mitonuclear interactions influence Alzheimer’s disease risk

Shea J Andrews a, Brian Fulton-Howard a, Christopher Patterson b,c, G Peggy McFall d,e, Alden Gross f, Elias K Michaelis g, Alison Goate a, Russell H Swerdlow h, Judy Pa b,c,*, Alzheimer’s Disease Neuroimaging Initiative
PMCID: PMC7205324  NIHMSID: NIHMS1543228  PMID: 31784277

Abstract

We examined the associations between mitochondrial DNA haplogroups (MT-hgs; mitochondrial haplotype groups defined by a specific combination of single nucleotide polymorphisms labeled as letters running from A to Z) and their interactions with a polygenic risk score composed of nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset (AOO) of dementia. MT-hg K (Odds ratio [OR]: 2.03 [95% CI: 1.04, 3.97]) and a 1 SD larger nMT-PRS (OR: 2.2 [95% CI: 1.68, 2.86]) were associated with elevated odds of dementia. Significant antagonistic interactions between the nMT-PRS and MT-hg K (OR: 0.45 [95% CI: 0.22, 0.9]) and MT-hg T (OR: 0.22 [95% CI: 0.1, 0.49]) were observed. Individual MT-hgs were not associated with AOO; however, a significant antagonistic interactions was observed between the nMT-PRS and MT-hg T (Hazard ratio: 0.62 [95% CI: 0.42, 0.91]) and a synergistic interaction between the nMT-PRS and MT-hg V (Hazard ratio: 2.28 [95% CI: 1.19, 4.35]). These results suggest that MT-hgs influence dementia risk and that variants in the nuclear and mitochondrial genome interact to influence the AOO of dementia.

Keywords: Mitochondria, Polygenic risk score, Alzheimer’s disease

1. Introduction

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive and functional deterioration resulting in a loss of independent living and ultimately death (Masters et al., 2015). The neuropathological hallmarks of AD are the abnormal aggregation and accumulation of amyloid-β peptides into extracellular amyloid plaques and hyperphosphorylated tau intracellular neurofibrillary tangles, accompanied by neuroinflammation, gliosis, and neurodegeneration (Masters et al., 2015; Mhatre et al., 2015). As such, studies on AD pathogenesis and therapeutics have largely focused on the role of Aβ and tau. However, with several negative trials of drugs targeting Aβ pathways, there has been increasing interest in evaluating the role of other pathological features in AD, such as mitochondrial dysfunction (Panza et al., 2019; Perez Ortiz and Swerdlow, 2019).

Mitochondria are vital to cellular function, first as the major source of cellular energy through the generation of adenosine triphosphate via oxidative phosphorylation and also through regulation of calcium uptake, apoptosis, and production and sequestration of reactive oxygen species (Gorman et al., 2016). Each mitochondrion possesses its own 16,569 base pair circular genome (mtDNA) that encodes 37 genes: 13 protein-coding genes, 22 tRNAs, and 2 ribosomal RNAs (Taanman, 1999). Genetic variation in the mitochondria is often described by established haplotype groups defined by a specific combination of single nucleotide polymorphisms (SNPs) that represent major branch points in the mitochondrial phylogenetic tree (van Oven and Kayser, 2009). Mitochondrial haplogroups are named using capital letters running from A to Z, with further subclades defined using lower case letters and numbers. Mitochondrial haplogroups (MT-hgs) H, I, J, K, T, V, W, X, and U are predominantly found in Europe. The nuclear genome also plays a key role in mitochondrial function as it contains 1145 genes that encode proteins that influence mitochondrial function (mitonuclear genes) (Calvo et al., 2016). These mitonuclear genes encode most of the proteins involved in the oxidative phosphorylation system and are also essential for maintaining mtDNA replication and organelle network proliferation and destruction (Chinnery and Hudson, 2013). A recent systematic review of 43 studies examining the effects of mitonuclear incompatibility across vertebrates and invertebrates found significant effects on health, including gene expression, metabolic traits, anatomical or morphological traits, life span, and fecundity (Dobler et al., 2018), indicating that incompatibility between nuclear and mitochondrial genes can influence biological function. Furthermore, in 6 admixed human populations, increasing discordance between nuclear and mtDNA ancestry was associated with reduced mtDNA copy number—a proxy measure for mitochondrial function (Zaidi and Makova, 2019). Thus, mitochondrial function relies on fine-tuned mitonuclear interactions that require the nuclear and mitochondrial genomes to be compatible with each other.

The central nervous system is particularly vulnerable to impaired mitochondrial metabolism because of its high-energy demands. Increasing evidence links mitochondrial dysfunction to neurodegenerative diseases such as AD. Support for the role of mitochondria in AD comes from studies observing changes in the rate of metabolism, disruption of fusion and fission, altered concentration of mitochondria in cerebrospinal fluid, morphological changes, and aggregation of Aβ in the mitochondria (Perez Ortiz and Swerdlow, 2019; Swerdlow, 2018). In addition, maternal history of AD confers an increased risk of AD, cognitive aging, and elevated biomarkers for AD, which is consistent with the maternal inheritance of mtDNA (Honea et al., 2012; Swerdlow, 2018). Despite this evidence, the role of the mitochondrial genome in AD remains inconclusive, as a recent systematic literature review of 17 studies reported few definitive findings on the association of mitochondrial genetic variation with AD (Ridge and Kauwe, 2018). In addition, although candidate gene studies have implicated several mitonuclear genes in AD risk, genome-wide association studies (GWAS) have not supported the association of specific mitonuclear genes with AD, with the exception of TOMM40, which is in high linkage disequilibrium with apolipoprotein E (APOE) (Chiba-Falek et al., 2018; Kunkle et al., 2019). To date, no study has investigated whether the genetic variation in mitonuclear genes interacts with the mitochondrial genome to influence AD risk.

In this study, we investigate the association of mitonuclear interactions in AD by evaluating the interactions between an AD polygenic risk score that included only variants from mitonuclear genes (nMT-PRS) and MT-hgs on AD risk and survival.

2. Methods

2.1. Alzheimer’s Disease Neuroimaging Initiative

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD.

Descriptive characteristics of ADNI participants at baseline and last assessment are presented in Table 1.

Table 1.

Descriptive characteristics of Alzheimer’s Disease Neuroimaging Initiative at baseline and last assessment (survival analysis)

Variable Baseline analysis
 Survival analysis
Controls (n = 301) Alzheimer’s disease (n = 187) Controls (n = 273) Mild cognitive impairment (n = 350) Alzheimer’s disease (n = 424)
Age, mean (SD) 75.49 (5.23) 75.67 (7.92) 78.82 (6.78) 77.83 (7.7) 74.77 (8.14)
Female, n (%) 135 (44.85) 82 (43.85) 126 (46.15) 134 (38.29) 172 (40.57)
Education, mean (SD) 16.3 (2.72) 14.96 (2.98) 16.44 (2.72) 15.84 (2.82) 15.42 (2.91)
APOE, n (%)
 ε3/ε3 201 (60.54) 60 (29.41) 172 (63) 183 (52.29) 140 (33.02)
 ε4+ 92 (27.71) 139 (68.14) 69 (25.27) 139 (39.71) 278 (65.57)
 ε2+ 39 (11.75) 5 (2.45) 32 (11.72) 28 (8) 6 (1.42)
 nMT-PRS, mean (SD) −0.37 (0.85) 0.47 (0.93) −0.38 (0.86) −0.08 (0.82) 0.4 (0.94)
Haplogroups, n (%)
 H 169 (50.9) 92 (45.1) 130 (47.62) 151 (43.14) 200 (47.17)
 I 14 (4.22) 6 (2.94) 10 (3.66) 10 (2.86) 17 (4.01)
 J 29 (8.73) 20 (9.8) 25 (9.16) 36 (10.29) 38 (8.96)
 K 34 (10.24) 31 (15.2) 28 (10.26) 46 (13.14) 43 (10.14)
 T 31 (9.34) 16 (7.84) 23 (8.42) 40 (11.43) 41 (9.67)
 U 35 (10.54) 26 (12.75) 35 (12.82) 48 (13.71) 51 (12.03)
 V 11 (3.31) 7 (3.43) 13 (4.76) 10 (2.86) 14 (3.3)
 W 3 (0.9) 4 (1.96) 3 (1.1) 7 (2) 10 (2.36)
 X 6 (1.81) 2 (0.98) 6 (2.2) 2 (0.57) 10 (2.36)
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2.2. Nuclear DNA

GWAS data for ADNI participants were obtained and processed as previously described (Saykin et al., 2015). Briefly, genomic DNA samples extracted from blood were genotyped on Illumina GWAS arrays (ADNI1: 610-Quad; ADNI GO/2 OmniExpress). Genotype data then underwent stringent quality control checks, with variants excluded if the call rate was <0.95, minor allele frequency was <1%, or were not in Hardy-Weinberg equilibrium (p < 1 × 10−6) and samples excluded if call rate was <0.95, discordant sex was reported, cryptic relatedness, non-European ancestry, or outlying heterozygosity. To empirically determine ancestry, the samples were projected onto principal components from known ancestral populations in the 1000 Genomes Project, with samples determined to be European population outliers if they were ±6 SD away from the EUR population mean on the first 10 principal components (1000 Genomes Project Consortium et al., 2015). Within-ancestry principal components were created using the –PCA function in PLINK (Purcell et al., 2007) to correct for residual population stratification within the European population subset. SNPs that were not directly assayed were imputed using the Haplotype Reference Consortium (McCarthy et al., 2016), with imputed variants excluded due to poor imputation quality (INFO <0.3) or low minor allele frequency (<1%).

2.3. Mitochondrial DNA

138 mtDNA variants were available for 757 samples from ADNI1 who were genotyped on the Illumina 610-Quad array. Additional mitochondrial genetic variants were made available via imputation of the mitochondrial genome, as previously described (McInerney et al., 2019), using a custom reference panel of mitochondrial genome sequences and the chromosome X imputation protocol in IMPUTE2 (Howie et al., 2009). An additional 809 samples with mitochondrial variants were made available via whole genome sequencing (Ridge et al., 2018). MT-hgs were assigned to the genotyped/imputed data set (SNPs with an info score >0.4) using HaploGrep2 (Weissensteiner et al., 2016), whereas in the whole genome sequenced data set, MT-hgs were assigned using Phy-Mer (Navarro-Gomez et al., 2015). We previously validated the imputation of mitochondrial variants in ADNI using 258 participants for whom whole genome sequencing and genotyping data were available (McInerney et al., 2019).

2.4. Polygenic risk scores

The software package PRSice was used to construct an AD PRS for nuclear-encoded mitochondrial polygenic risk scores (nMT-PRSs) (Euesden et al., 2015). To generate a mitonuclear AD PRS, SNPs from stage 1 of the International Genomics of Alzheimer’s Project (IGAP) (Lambert et al., 2013) were annotated to known protein-coding genes (±50kb) using MAGMA (de Leeuw et al., 2015) and those SNPs that were assigned to any of 1158 mitonuclear genes were extracted (Calvo et al., 2016). A p-value threshold of 0.5 was used for inclusion of SNPs into the nMT-PRS as this threshold has been previously shown to have the most significant association with case/control diagnosis (Escott-Price et al., 2015). To obtain independent loci, linkage disequilibrium clumping was performed by excluding SNPs that had an r2 > 0.1 with another variant with smaller p-value association within a 250kb window. SNPs were weighted by their effect sizes in IGAP. A total of 19,630 SNPs were included in the nMT-PRS (Supplementary Table 1).

2.5. Statistical analysis

Cross-sectional analysis:

The effect of the MT-hgs on baseline risk of dementia was assessed using binomial multivariate logistic regression models with MT-hg H used as the reference group and adjusting for age, APOE status, sex, and the first 2 principal components (model 1). To evaluate interactions between the MT-hgs and nMT-PRS, an interaction term was included in the model (model 2). As logistic regression tests for interactions on a multiplicative scale (that the combined effect is larger or smaller than the product of the individual effects), we used the relative excess risk due to interaction (RERI) to test for departures from additivity (that the combined effect is greater or smaller than the sum of the individual effects).The RERI can be interpreted as the risk that is additional to the risk that is expected on the basis of the addition of the odds ratio (OR) of the variables. In the absence of an interaction effect, the RERI is equal to 0. As MCI is an unstable diagnosis with individuals either converting to dementia, remaining stable or reverting back to normal cognition (Canevelli et al., 2016), participants with MCI (n = 634) were excluded from the analysis.

Survival analysis:

In the survival analysis, age was used as the time to event scale. For subjects who were cognitively normal or suffering from MCI at baseline, AD age at onset was used. For participants with AD at baseline, the reported best estimate of onset of AD-dementia symptoms by the subject (or informant) was used (Osorio et al., 2015). A Cox proportional hazards model with adjustment for APOE status, sex, and the first 2 principal components was used to assess the effects of the nMT-PRS and MT-hgs on Alzheimer’s age of onset (AOO) (model 1). To evaluate potential interactions between the MT-hgs and the nMT-PRS, an interaction term was included in the model (model 2).

Sensitivity analysis:

For a sensitivity analysis, an additional polygenic score was constructed (PRS w/o nMT and APOE), composed of all SNPs associated with late-onset AD (LOAD) at p < 0.5 in IGAP, except for those annotated to known mitonuclear genes and the APOE region (±250 kb of APOE). A total of 191,990 SNPs were included in the PRS. The cross-sectional and survival analyses were repeated introducing this additional PRS as a covariate.

All analyses were performed in the R 3.5.2 statistical computing environment. As this is an exploratory study, we have not corrected for multiple testing as this can result in a high risk of type 2 errors (Bender and Lange, 2001). As associations for 8 haplogroups and the nMT-PRS were tested, a significant p-value after Bonferroni correction would be p < 0.0056 (0.05/9) for main effects models (model 1) and p < 0.0029 (0.05/17) for the interaction models (model 2).

3. Results

3.1. Association of mitonuclear interactions with Alzheimer’s risk

Binomial logistic regression was used to evaluate the main effects of the MT-hgs and nMT-PRS and their interaction on the likelihood of participants having AD (Table 2). In the main effects model (Table 2, model 1), MT-hg K was associated with an increased risk of developing AD (OR: 2.00 [95% CI: 1.04, 3.97]), whereas MT-hg U was nominally associated with increased risk (OR: 1.99 [95% CI: 0.99, 3.97]). A 1-SD increase in the nMT-PRS was associated with an increased likelihood of developing AD (OR: 2.2 [95% CI: 1.68, 2.86]).

Table 2.

Association of a mitochondrial PRS and mitochondrial haplogroups (model 1) and their interactions (model 2) with baseline risk of Alzheimer’s disease

Variable Model 1
 Model 2
βa SE p β SE p
Age   0.02 0.02 0.213   0.03 0.02 0.122
Male −0.01 0.22 0.975   −0.09 0.23 0.682
APOE status
 ε4+   1.2 0.24 6.47E-07   1.25 0.25 5.08E-07
 ε2+ −0.7 0.58 0.227 −0.88 0.6 0.142
PC1 −0.14 0.13 0.274 −0.1 0.13 0.423
PC2   0.53 0.55 0.332   0.42 0.55 0.442
nMT-PRS   0.79 0.14 5.58E-09   1.09 0.2 5.68E-08
Haplogroup
 I   0.27 0.62 0.661   0.33 0.63 0.6
 J   0.09 0.4 0.827   0.06 0.44 0.886
 K   0.694 0.35 0.049   0.71 0.34 0.038
 T −0.23 0.4 0.564   0.04 0.38 0.909
 U   0.687 0.35 0.052   0.81 0.38 0.033
 V   0.01 0.63 0.988   0.07 0.76 0.926
 W   0.66 0.9 0.464   0.74 0.86 0.391
 X −1.23 1.13 0.278 −2.04 1.77 0.247
Haplogroup × nMT-PRS
 I - - - −0.38 0.84 0.647
 J - - -   0.28 0.68 0.685
 K - - - −0.8 0.36 0.026
 T - - - −1.51 0.41 2.18E-04
 U - - -   0.07 0.52 0.886
 V - - -   1.01 1.44 0.482
 W - - - −0.68 0.85 0.425
 X - - - −2.31 1.97 0.24
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Key: APOE, apolipoprotein E; nMT-PRS, nuclear-encoded mitochondrial polygenic risk score; PC1, principal component 1; PC2, principal component 2.

a

Results in the main text are presented as the exponentiation of the beta. p-values are unadjusted.

In the interaction model (Table 2, model 2), a significant interaction was observed between the nMT-PRS and MT-hg T (OR: 0.22 [95% CI: 0.1, 0.49]) and MT-hg K (OR: 0.45 [95% CI: 0.22,0.9]). Under an additive model, the RERI for MT-hgs T and K were RERI −2.8 (95% CI: −4.33, −1.26) and −3.56 (95% CI: −5.54, −1.57), respectively, indicating that the nMT-PRS and MT-hgs K and T acted antagonistically in relation to AD risk, such that the relative risk of AD was 2.8 and 3.56 times lower than expected from the addition of the separate effects of the nMT-PRS and MT-hg.

3.2. Association of mitonuclear interactions with Alzheimer’s AOO

A Cox proportional hazard model was used to evaluate the main effects of the MT-hgs and nMT-PRS and their interaction on Alzheimer’s AOO (Table 3). In the main effects model, a 1-SD increase in the nMT-PRS was associated with an earlier AOO (Hazard ratio [HR]: 1.44 [95% CI: 1.28, 1.61]). However, none of the MT-hgs were significantly associated with AOO.

Table 3.

Association of a mitochondrial PRS and mitochondrial haplogroups (model 1) and their interactions (model 2) with Alzheimer’s disease age of onset

Variable Model 1
 Model 2
βa SE p βa SE p
Male −0.21 0.1 0.0034 −0.22 0.1 0.028
APOE status
 ε4+   0.8 0.12 6.50E-12   0.78 0.12 2.70E-11
 ε2+ −0.85 0.39 0.029 −0.86 0.39 0.027
PC1 −0.11 0.06 0.073 −0.1 0.06 0.104
PC2   0.03 0.04 0.525   0.03 0.04 0.509
nMT-PRS   0.36 0.06 3.54E-10   0.37 0.08 2.55E-06
Haplogroup
 I −0.01 0.26 0.958   0.04 0.28 0.879
 J −0.06 0.17 0.707 −0.09 0.19 0.635
 K −0.06 0.18 0.742 −0.05 0.18 0.78
 T −0.31 0.17 0.073 −0.14 0.18 0.422
 U −0.1 0.15 0.528 −0.16 0.17 0.35
 V   0.17 0.28 0.549 −0.26 0.38 0.496
 W   0.38 0.33 0.247   0.38 0.33 0.241
 X   0.08 0.32 0.794   0.16 0.36 0.664
Haplogroup × nMT-PRS
 I - - - −0.14 0.29 0.617
 J - - -   0.08 0.22 0.72
 K - - - −0.06 0.17 0.74
 T - - - −0.48 0.2 0.015
 U - - -   0.14 0.15 0.371
 V - - -   0.82 0.33 0.013
 W - - -   0.13 0.34 0.697
 X - - - −0.13 0.32 0.68
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Key: APOE, apolipoprotein E; nMT-PRS, nuclear-encoded mitochondrial polygenic risk score; PC1, principal component 1; PC2, principal component 2.

a

Results in the main text are presented as the exponentiation of the beta.

In the interactive model, a significant interaction was observed between the nMT-PRS and MT-hg T (HR: 0.62 [95% CI: 0.42, 0.91]) and MT-hg V (HR: 2.28 [95% CI: 1.19, 4.35]). Under an additive model, RERI for MT-hgs T and V were −0.7 (95% CI: −1.24, −0.16) and 1.06 (95% CI: −0.9, 3.02), respectively, indicating that the relative risk of AD was 0.7 times lower for MT-hg T and 1.06 times higher for MT-hg V than expected from the addition of the separate effects of the nMT-PRS and MT-hg.

3.3. Sensitivity analysis

The effect of adjusting the baseline logistic model and the survival model with a PRS composed of non-nuclear mitochondrial SNPs and non-APOE region SNPs are presented in Tables 4 and 5. The effect of the nMT-PRS on AD risk and AOO was attenuated but remained statistically significant. The significant interactions observed between the nMT-PRS and MT-hg T in the baseline model and MT-hg V remained statistically significant after covarying for the PRS without nMT and APOE. However, the interaction with MT-hg K in the baseline model was nominally significant, whereas the interaction with MT-hg T was no longer significant in the survival model.

Table 4.

Association of a mitochondrial PRS and mitochondrial haplogroups (model 1) and their interactions (model 2) with baseline risk of Alzheimer’s disease adjusting for PRS excluding nMT genes and APOE

Variable Model 1
 Model 2
βa SE p βa SE p
Age   0.001 0.02 0.953   0.01 0.02 0.686
Male   0.16 0.28 0.58   0.03 0.29 0.92
APOE status
 ε4+   1.66 0.32 1.78E-07   1.72 0.33 2.20E-07
 ε2+ −0.6 0.72 0.407 −0.74 0.74 0.312
PC1 −0.59 0.17 3.42E-04 −0.55 0.17 9.86E-04
PC2   0.84 0.69 0.224   0.69 0.7 0.327
PRS w/o nMT & APOE   3.26 0.33 1.05E-22   3.26 0.34 3.65E-22
nMT-PRS   0.42 0.17 0.012   0.68 0.24 0.004
Haplogroup
 I   0.55 0.74 0.459   0.63 0.74 0.395
 J   0.53 0.55 0.331   0.55 0.57 0.333
 K   0.48 0.45 0.289   0.44 0.44 0.311
 T   0.43 0.47 0.357   0.56 0.48 0.248
 U   1.01 0.46 0.027   1.11 0.48 0.021
 V   0.5 0.8 0.532   0.81 1.02 0.423
 W   0.19 1.14 0.87   0.55 1.14 0.631
 X −0.29 1.23 0.813 −1.51 2.48 0.543
Haplogroup × nMT-PRS
 I - - - −0.36 1.15 0.752
 J - - -   0.11 0.8 0.893
 K - - - −0.9 0.46 0.052
 T - - - −1.25 0.58 0.032
 U - - -   0.24 0.61 0.702
 V - - -   1.16 1.93 0.546
 W - - - −1.02 0.99 0.305
 X - - - −3.29 2.78 0.237
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Key: APOE, apolipoprotein E; nMT-PRS, nuclear-encoded mitochondrial polygenic risk score; PC1, principal component 1; PC2, principal component 2.

a

Results in the main text are presented as the exponentiation of the beta.

Table 5.

Association of a mitochondrial PRS and mitochondrial haplogroups (model 1) and their interactions (model 2) with Alzheimer’s disease age of onset adjusting for PRS excluding nMT genes and APOE

Variable Model 1
 Model 2
βa SE p βa SE p
Male −0.34 0.1 8.06E-04 −0.35 0.1 6.78E-04
APOE status
 ε4+   0.78 0.11 6.42E-12   0.77 0.12 3.32E-11
 ε2+ −0.71 0.39 0.069 −0.73 0.39 0.064
PC1 −0.3 0.07 7.63E-06 −0.28 0.07 3.26E-05
PC2   0.05 0.04 0.207   0.05 0.04 0.194
PRS w/o nMT & APOE   1.03 0.09 3.56E-29   1.03 0.09 2.63E-28
nMT-PRS   0.15 0.06 0.018   0.17 0.08 0.041
Haplogroup
 I −0.26 0.26 0.302 −0.15 0.28 0.597
 J   0.08 0.18 0.632   0.06 0.19 0.748
 K −0.11 0.18 0.521 −0.09 0.19 0.623
 T −0.14 0.17 0.417 −0.08 0.18 0.631
 U −0.13 0.16 0.396 −0.14 0.17 0.413
 V   0.22 0.28 0.426 −0.29 0.41 0.489
 W   0.47 0.33 0.149   0.5 0.33 0.124
 X −0.02 0.33 0.949   0.21 0.37 0.57
Haplogroup × nMT-PRS
 I - - - −0.26 0.28 0.366
 J - - -   0.09 0.22 0.668
 K - - - −0.11 0.17 0.505
 T - - - −0.22 0.21 0.282
 U - - -   0.01 0.16 0.927
 V - - -   0.88 0.39 0.025
 W - - -   0.26 0.35 0.455
 X - - - −0.37 0.34 0.278
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Key: APOE, apolipoprotein E; nMT-PRS, nuclear-encoded mitochondrial polygenic risk score; PC1, principal component 1; PC2, principal component 2.

a

Results in the main text are presented as the exponentiation of the beta.

4. Discussion

In this study, we investigated whether mitonuclear interactions influence Alzheimer’s risk and survival by evaluating the interactive effects of MT-hgs and a polygenic risk score composed of mitonuclear genes (nMT-PRS) on baseline risk of AD and AOO of dementia. We observed that nMT-PRS was associated with an increased risk of AD and an earlier AOO, even after adjusting for a PRS composed of SNPs from the rest of the genome. In addition, we observed that MT-hg K was associated with an increased baseline risk of AD, and in the interactive model, modified the risk associated with the nMT-PRS in an antagonistic manner, such that the combined effect of the nMT-PGS and MT-hg K was smaller than expected given their additive effects. In effect, they are less harmful together than they are on their own, although the potential underlying mechanisms of this compensatory effect are complex (Lehner, 2011). MT-hg T was also observed to modify the risk associated with the nMT-PRS in an antagonistic manner for both baseline risk of AD and AOO. Finally, we observed that MT-hg V was associated with an increased risk of AD beyond that expected in the additive model with the nMT-PRS.

These results suggest that epistasis between nuclear and mitochondrial genomes, in which one gene’s effect is dependent on the presence of another gene or set of genes, influences the risk of AD. Although, to date, no previous study to our knowledge has investigated the interaction between mitonuclear genes and the mitochondrial genome in the context of AD, several studies have investigated associations between mitochondrial genetic variation and APOE. In APOE ε4 carriers, MT-hgs K and U were observed to have neutralizing effect (Carrieri et al., 2001; Maruszak et al., 2011) on AD risk. Conversely, SNP mt7028C, a defining SNP for MT-hg H, and MT-hg H5a acted synergistically with APOE ε4 to increased risk of AD (Coto et al., 2011; Maruszak et al., 2011). Finally, SNP mt4336C, which defines MT-hg H5a, was associated with an increased risk of AD only in APOE ε4 carriers (Edland et al., 2002). Outside of AD, mitonuclear interactions have also been implicated in altering the penetrance of primary pathological mutations underlying mitochondrial disease or modifying the pathogenic phenotype of other diseases, such as nonsyndromic sensorineural deafness (Kokotas et al., 2007; Morrow and Camus, 2017). The results of this study, in addition to the prevalence of mitonuclear epistasis in other diseases, suggests that the inconclusive results of mitochondrial genetic variation in AD may not only be due to small sample sizes, limited genetic data collection, and inadequate approaches to association analysis (Ridge and Kauwe, 2018) but could also be attributed to the modifying effects of nuclear-encoded mitochondrial genes. As such, future studies investigating the association of mtDNA with AD should consider evaluating the modifying effect of nDNA.

The MT-hg association analysis is in agreement with two previous studies conducted in ADNI. Lakatos et al. (Lakatos et al., 2010) investigated the association of 4 MT-hg clusters (HV, JT, UK, and IWX) with AD in 358 participants and found that the UK MT-hg cluster was associated with an increased risk of AD. Ridge et al. (Ridge et al., 2013) utilized a TreeScanning approach to assess the relationship of mitochondrial genetic variation with structural MRI and cognitive biomarkers and found that SNPs defining either MT-hg K1A1B or K1A1B2A1 and MT-hg U5B1 or U5B1B2 were associated with reduced temporal pole thickness, which is considered evidence of increased risk for AD. However, within the context of the wider literature, MT-hgs U, K, and T have been associated with conflicting reports, with different studies reporting either protective, risk, or nonsignificant effects (Ridge and Kauwe, 2018). In cybrid cell lines, MT-hg T in comparison with MT-hg H has a higher capability to cope with oxidative stress (Mueller et al., 2012), whereas cybrids containing MT-hg K express higher levels of APOE (Thaker et al., 2016).

AD polygenic risk scores have been widely used to evaluate whether genetic liability for AD is associated with AD endophenotypes and in the prediction of disease status (Chasioti et al., 2019; Ibanez et al., 2019). These PRSs, however, have generally been applied to variants across the entire genome. Using biological knowledge to incorporate variants located in genes that are part of specific pathways in the calculation of PRS, instead of considering the entire genome, allows for the construction of pathway specific PRS (Darst et al., 2017; Ibanez et al., 2019). In contrast to univariate analysis which is often underpowered due to the small effect sizes of individual SNPs, the joint analysis of the combined effect of all SNPs within a pathway may have a larger combined effect size and greater statistical power to detect an association. Pathway-based analysis may also be more powerful predictor for understanding how specific biomarkers may contribute to disease pathogenesis. Furthermore, as a large proportion of the heritability of AD is explained by variants that lie below the genome-wide significant threshold, the inclusion of subthreshold variants allows the PRS to encompass more of the causal variants (Escott-Price et al., 2015). To date, there has been a limited application of pathway-specific PRS in AD, with only one study evaluating the association of PRSs for the immune, Aβ clearance, and cholesterol pathways with AD-related biomarkers (Darst et al., 2017; Ibanez et al., 2019). However, these PRSs were poor predictors of cognition, amyloid PET deposition, and cerebrospinal fluid Ab, tau, and P-tau levels, potentially due to only including genome-wide significant loci (Darst et al., 2017). In the present study, we show that a pathway-specific PRS composed of SNPs located within nuclear-encoded mitochondrial genes is associated with both risk of AD and an earlier AAO, suggesting that mitochondrial function moderates AD pathogenesis. Our findings are supported by another recent study that built a molecular network using modules of coexpressed genes and identified 3 modules enriched for gene ontology categories related to mitochondria. These modules were associated with histopathological β-amyloid burden, cognitive decline, and clinical diagnosis of AD (Mostafavi et al., 2018). Interestingly, a pathway analysis conducted by IGAP tested for overrepresentation of genes containing significantly associated SNPs within a series of functional gene sets found no evidence of enrichment in mitochondrial pathways (Jones et al., 2015). This analysis, however, only examined mitochondrial pathways that contained a subset of the mitochondria-related genes relevant to that gene set, whereas our study examined the aggregate effect of all nuclear encoded genes related to mitochondrial function.

The results of this study should be interpreted in conjunction with some study limitations. First, ADNI has a relatively small sample size, which can contribute to unreliable findings as a result of (a) a low probability of finding true effects, (b) a lower probability that an observed effect that is statistically significant reflects a true effect, or (c) an extracted estimate of the magnitude of an effect when a true effect is discovered (Button et al., 2013). In addition, as this is an exploratory study, the findings of this study need to be replicated in a larger cohort. Second, when constructing PRS, sample overlap between the base data set (i.e., IGAP) and the target data sets (i.e., ADNI) can result in inflation of the association between the PRS and trait tested in the target data set (Choi et al., 2018). However, it should be noted that IGAP consists of 54,162 participants, with ADNI only contributing 441 samples to IGAP, or 0.81% of IGAPs total sample size. In addition, the samples included in the IGAP analysis are a subset of those included in this analysis. As such, the sample overlap between the base and target data sets is unlikely to substantially bias the results of this study. Third, the subjects in this study were of European ancestry and European MT-hg, and thus the results presented may not be generalizable to other racial/ethnic populations. In particular, in admixed populations that have a greater discordance between nuclear and mitochondrial ancestry, it could be expected that mitonuclear interactions may contribute to even more phenotypic variation in disease (Zaidi and Makova, 2019). The primary strength of this paper was evaluating the effect of MT-hgs in the context of a participant’s nuclear polygenic risk for LOAD. Second, by imputing mtDNA variants, we were able to more accurately assign MT-hgs to individuals who were included in a previous ADNI study (Lakatos et al., 2010). Finally, we utilized both cross-sectional and longitudinal data to evaluate the baseline risk of LOAD and AOO.

In conclusion, this is the first study to investigate the interactive effects of a LOAD PRS composed of mitonuclear genes and MT-hgs on Alzheimer’s risk and survival. We found that the nMT-PRS was associated with increased risk of AD and an earlier AOO. MT-hg T was observed to attenuate the effect of the nMT-PRS on the risk of AD and AOO, whereas MT-hgs K and V were observed to attenuate the effect of the nMT-PRS on baseline risk and strengthen the effect of the nMT-PRS on AOO, respectively. The results from this study need to be replicated in independent cohorts to validate our findings. These findings suggest that interactions between the nuclear and mitochondrial genomes may influence AD pathogenesis.

Supplementary Material

1

Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative, United States (National Institutes of Health, United States Grant U01 AG024904) and DOD ADNI (Department of Defense, United States award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, United States, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc; Cogstate; Eisai Inc; Elan Pharmaceuticals, Inc; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co, Inc; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

This study was conducted as a part of the 2017 Advanced Psychometric Methods in Cognitive Aging Research conference supported by the National Institute on Aging (R13AG030995; PI: Dan Mungas). JP and CP were supported by the National Institute on Aging (R01AG054617 PI: JP). SJA, BF-H, and AMG are supported by the JPB Foundation, United States (http://www.jpbfoundation.org). EKM and RHS are supported by the National Institute on Aging (P30AG035982).

Footnotes

Disclosure

AMG served on the scientific advisory board for Denali Therapeutics from 2015–to 2018. She has also served as a consultant for Biogen, AbbVie, Pfizer, GSK, Eisai, and Illumina.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.neurobiolaging.2019.09.007.

References

  1. Bender R, Lange S, 2001. Adjusting for multiple testing-when and how? J. Clin. Epidemiol 54, 343–349. [DOI] [PubMed] [Google Scholar]
  2. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, Munafò MR, 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci 14, 365–376. [DOI] [PubMed] [Google Scholar]
  3. Calvo SE, Clauser KR, Mootha VK, 2016. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 44, D1251–D1257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Canevelli M, Grande G, Lacorte E, Quarchioni E, Cesari M, Mariani C, Bruno G, Vanacore N, 2016. Spontaneous reversion of mild cognitive impairment to normal cognition: a systematic review of literature and meta-analysis. J. Am. Med. Dir. Assoc 17, 943–948. [DOI] [PubMed] [Google Scholar]
  5. Carrieri G, Bonafè M, De Luca M, Rose G, Varcasia O, Bruni A, Maletta R, Nacmias B, Sorbi S, Corsonello F, Feraco E, Andreev KF, Yashin AI, Franceschi C, De Benedictis G, 2001. Mitochondrial DNA haplogroups and APOE4 allele are non-independent variables in sporadic Alzheimer’s disease. Hum. Genet 108, 194–198. [DOI] [PubMed] [Google Scholar]
  6. Chasioti D, Yan J, Nho K, Saykin AJ, 2019. Progress in polygenic composite scores in Alzheimer’s and other complex diseases. Trends Genet. 35, 371–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chiba-Falek O, Gottschalk WK, Lutz MW, 2018. The effects of the TOMM40 poly-T alleles on Alzheimer’s disease phenotypes. Alzheimers. Dement 14, 692–698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chinnery PF, Hudson G, 2013. Mitochondrial genetics. Br. Med. Bull 106, 135–159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Choi SW, Mak TSH, O’Reilly P, 2018. A guide to performing Polygenic Risk Score analyses. bioRxiv 1–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Coto E, Gómez J, Alonso B, Corao AI, Díaz M, Menéndez M, Martínez C, Calatayud MT, Morís G, Álvarez V, 2011. Late-onset Alzheimer’s disease is associated with mitochondrial DNA 7028C/haplogroup H and D310 poly-C tract heteroplasmy. Neurogenetics 12, 345–346. [DOI] [PubMed] [Google Scholar]
  11. Darst BF, Koscik RL, Racine AM, Oh JM, Krause RA, Carlsson CM, Zetterberg H, Blennow K, Christian BT, Bendlin BB, Okonkwo OC, Hogan KJ, Hermann BP, Sager MA, Asthana S, Johnson SC, Engelman CD, 2017. Pathway-specific polygenic risk scores as predictors of amyloid-β deposition and cognitive function in a sample at increased risk for Alzheimer’s disease. J. Alzheimers. Dis 55, 473–484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Leeuw CA, Mooij JM, Heskes T, Posthuma D, 2015. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol 11, e1004219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dobler R, Dowling DK, Morrow EH, Reinhardt K, 2018. A systematic review and meta-analysis reveals pervasive effects of germline mitochondrial replacement on components of health. Hum. Reprod. Update 24, 519–534. [DOI] [PubMed] [Google Scholar]
  14. Edland SD, Tobe VO, Rieder MJ, Bowen JD, McCormick W, Teri L, Schellenberg GD, Larson EB, Nickerson DA, Kukull WA, 2002. Mitochondrial genetic variants and Alzheimer disease: a case-control study of the T4336C and G5460A variants. Alzheimer Dis. Assoc. Disord 16, 1–7. [DOI] [PubMed] [Google Scholar]
  15. Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, Badarinarayan N, , GERAD/PERADES, IGAP consortia, Morgan K, Passmore P, Holmes C, Powell J, Brayne C, Gill M, Mead S, Goate A, Cruchaga C, Lambert J-C, van Duijn C, Maier W, Ramirez A, Holmans P, Jones L, Hardy J, Seshadri S, Schellenberg GD, Amouyel P, Williams J, 2015. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain 138, 3673–3684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Euesden J, Lewis CM, O’Reilly PF, 2015. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR, 2015. A global reference for human genetic variation. Nature 526, 68–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gorman GS, Chinnery PF, DiMauro S, Hirano M, Koga Y, McFarland R, Suomalainen A, Thorburn DR, Zeviani M, Turnbull DM, 2016. Mitochondrial diseases. Nat. Rev. Dis. Primers 2, 16080. [DOI] [PubMed] [Google Scholar]
  19. Honea RA, Vidoni ED, Swerdlow RH, Burns JM, Alzheimer’s Disease Neuroimaging Initiative, 2012. Maternal family history is associated with Alzheimer’s disease biomarkers. J. Alzheimers. Dis 31, 659–668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Howie BN, Donnelly P, Marchini J, 2009. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ibanez L, Farias FHG, Dube U, Mihindukulasuriya KA, Harari O, 2019. Polygenic risk scores in neurodegenerative diseases: a review. Curr. Genet. Med. Rep 7, 22–29. [Google Scholar]
  22. Jones L, Lambert J-C, Wang L-S, Choi S-H, Harold D, Vedernikov A, Escott-Price V, Stone T, Richards A, Bellenguez C, Ibrahim-Verbaas CA, Naj AC, Sims R, Gerrish A, Jun G, DeStefano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thornton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin C-F, Schmidt H, Kunkle BW, Dunstan ML, Ruiz A, Bihoreau M-T, Reitz C, Pasquier F, Hollingworth P, Hanon O, Fitzpatrick AL, Buxbaum J, Campion D, Crane PK, Becker T, Gudnason V, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letteneur L, Kornhuber J, Tárraga L, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fiévet N, Huentelman MJ, Gill M, Emilsson V, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuinness B, Larson EB, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Kehoe P, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossù P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez Garcia F, Fox N, Hardy J, Deniz Naranjo MC, Razquin C, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Moebus S, Mecocci P, del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Jessen F, Dichgans M, Lannfelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alavarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O’Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley TH, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RFAG, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JSK, Boerwinkle E, Riemenschneider M, Boada M, Hiltunen M, Martin ER, Pastor P, Schmidt R, Rujescu D, Dartigues J-F, Mayeux R, Tzourio C, Hofman A, Nöthen MM, Graff C, Psaty BM, Haines JL, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Ramirez A, Schellenberg GD, Seshadri S, Amouyel P, Williams J, Holmans PA, 2015. Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimers. Dement 11, 658–671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kokotas H, Petersen MB, Willems PJ, 2007. Mitochondrial deafness. Clin. Genet 71, 379–391. [DOI] [PubMed] [Google Scholar]
  24. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Martin ER, Sleegers K, Badarinarayan N, Jakobsdottir J, Hamilton-Nelson KL, Moreno-Grau S, Olaso R, Raybould R, Chen Y, Kuzma AB, Hiltunen M, Morgan T, Ahmad S, Vardarajan BN, Epelbaum J, Hoffmann P, Boada M, Beecham GW, Garnier J-G, Harold D, Fitzpatrick AL, Valladares O, Moutet M-L, Gerrish A, Smith AV, Qu L, Bacq D, Denning N, Jian X, Zhao Y, Del Zompo M, Fox NC, Choi S-H, Mateo I, Hughes JT, Adams HH, Malamon J, Sanchez-Garcia F, Patel Y, Brody JA, Dombroski BA, Naranjo MCD, Daniilidou M, Eiriksdottir G, Mukherjee S, Wallon D, Uphill J, Aspelund T, Cantwell LB, Garzia F, Galimberti D, Hofer E, Butkiewicz M, Fin B, Scarpini E, Sarnowski C, Bush WS, Meslage S, Kornhuber J, White CC, Song Y, Barber RC, Engelborghs S, Sordon S, Voijnovic D, Adams PM, Vandenberghe R, Mayhaus M, Cupples LA, Albert MS, De Deyn PP, Gu W, Himali JJ, Beekly D, Squassina A, Hartmann AM, Orellana A, Blacker D, Rodriguez-Rodriguez E, Lovestone S, Garcia ME, Doody RS, Munoz-Fernadez C, Sussams R, Lin H, Fairchild TJ, Benito YA, Holmes C, Karamujić-Ćomić H, Frosch MP, Thonberg H, Maier W, Roschupkin G, Ghetti B, Giedraitis V, Kawalia A, Li S, Huebinger RM, Kilander L, Moebus S, Hernández I, Kamboh MI, Brundin R, Turton J, Yang Q, Katz MJ, Concari L, Lord J, Beiser AS, Keene CD, Helisalmi S, Kloszewska I, Kukull WA, Koivisto AM, Lynch A, Tarraga L, Larson EB, Haapasalo A, Lawlor B, Mosley TH, Lipton RB, Solfrizzi V, Gill M, Longstreth WT Jr., Montine TJ, Frisardi V, Diez-Fairen M, Rivadeneira F, Petersen RC, Deramecourt V, Alvarez I, Salani F, Ciaramella A, Boerwinkle E, Reiman EM, Fievet N, Rotter JI, Reisch JS, Hanon O, Cupidi C, Andre Uitterlinden AG, Royall DR, Dufouil C, Maletta RG, de Rojas I, Sano M, Brice A, Cecchetti R, George-Hyslop PS, Ritchie K, Tsolaki M, Tsuang DW, Dubois B, Craig D, Wu C-K, Soininen H, Avramidou D, Albin RL, Fratiglioni L, Germanou A, Apostolova LG, Keller L, Koutroumani M, Arnold SE, Panza F, Gkatzima O, Asthana S, Hannequin D, Whitehead P, Atwood CS, Caffarra P, Hampel H, Quintela I, Carracedo Á, Lannfelt L, Rubinsztein DC, Barnes LL, Pasquier F, Frölich L, Barral S, McGuinness B, Beach TG, Johnston JA, Becker JT, Passmore P, Bigio EH, Schott JM, Bird TD, Warren JD, Boeve BF, Lupton MK, Bowen JD, Proitsi P, Boxer A, Powell JF, Burke JR, Kauwe JSK, Burns JM, Mancuso M, Buxbaum JD, Bonuccelli U, Cairns NJ, McQuillin A, Cao C, Livingston G, Carlson CS, Bass NJ, Carlsson CM, Hardy J, Carney RM, Bras J, Carrasquillo MM, Guerreiro R, Allen M, Chui HC, Fisher E, Masullo C, Crocco EA, DeCarli C, Bisceglio G, Dick M, Ma L, Duara R, Graff-Radford NR, Evans DA, Hodges A, Faber KM, Scherer M, Fallon KB, Riemenschneider M, Fardo DW, Heun R, Farlow MR, Kölsch H, Ferris S, Leber M, Foroud TM, Heuser I, Galasko DR, Giegling I, Gearing M, Hüll M, Geschwind DH, Gilbert JR, Morris J, Green RC, Mayo K, Growdon JH, Feulner T, Hamilton RL, Harrell LE, Drichel D, Honig LS, Cushion TD, Huentelman MJ, Hollingworth P, Hulette CM, Hyman BT, Marshall R, Jarvik GP, Meggy A, Abner E, Menzies GE, Jin L-W, Leonenko G, Real LM, Jun GR, Baldwin CT, Grozeva D, Karydas A, Russo G, Kaye JA, Kim R, Jessen F, Kowall NW, Vellas B, Kramer JH, Vardy E, LaFerla FM, Jöckel KH, Lah JJ, Dichgans M, Leverenz JB, Mann D, Levey AI, Pickering-Brown S, Lieberman AP, Klopp N, Lunetta KL, Wichmann H-E, Lyketsos CG, Morgan K, Marson DC, Brown K, Martiniuk F, Medway C, Mash DC, Nöthen MM, Masliah E, Hooper NM, McCormick WC, Daniele A, McCurry SM, Bayer A, McDavid AN, Gallacher J, McKee AC, van den Bussche H, Mesulam M, Brayne C, Miller BL, Riedel-Heller S, Miller CA, Miller JW, Al-Chalabi A, Morris JC, Shaw CE, Myers AJ, Wiltfang J, O’Bryant S, Olichney JM, Alvarez V, Parisi JE, Singleton AB, Paulson HL, Collinge J, Perry WR, Mead S, Peskind E, Cribbs DH, Rossor M, Pierce A, Ryan NS, Poon WW, Nacmias B, Potter H, Sorbi S, Quinn JF, Sacchinelli E, Raj A, Spalletta G, Raskind M, Caltagirone C, Bossu P, Orfei MD, Reisberg B, Clarke R, Reitz C, Smith AD, Ringman JM, Warden D, Roberson ED, Wilcock G, Rogaeva E, Bruni AC, Rosen HJ, Gallo M, Rosenberg RN, Ben-Shlomo Y, Sager MA, Mecocci P, Saykin AJ, Pastor P, Cuccaro ML, Vance JM, Schneider JA, Schneider LS, Slifer S, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Swerdlow RH, Tang M, Tanzi RE, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Van Eldik LJ, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Wilhelmsen KC, Williamson J, Wingo TS, Woltjer RL, Wright CB, Yu C-E, Yu L, Saba Y, Alzheimer Disease Genetics Consortium (ADGC), European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES), Pilotto A, Bullido MJ, Peters O, Crane PK, Bennett D, Bosco P, Coto E, Boccardi V, De Jager PL, Lleo A, Warner N, Lopez OL, Ingelsson M, Deloukas P, Cruchaga C, Graff C, Gwilliam R, Fornage M, Goate AM, Sanchez-Juan P, Kehoe PG, Amin N, Ertekin-Taner N, Berr C, Debette S, Love S, Launer LJ, Younkin SG, Dartigues J-F, Corcoran C, Ikram MA, Dickson DW, Nicolas G, Campion D, Tschanz J, Schmidt H, Hakonarson H, Clarimon J, Munger R, Schmidt R, Farrer LA, Van Broeckhoven C, C O’Donovan M, DeStefano AL, Jones L, Haines JL, Deleuze J-F, Owen MJ, Gudnason V, Mayeux R, Escott-Price V, Psaty BM, Ramirez A, Wang L-S, Ruiz A, van Duijn CM, Holmans PA, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Lambert J-C, Pericak-Vance MA, 2019. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet 51, 414–430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lakatos A, Derbeneva O, Younes D, Keator D, Bakken T, Lvova M, Brandon M, Guffanti G, Reglodi D, Saykin A, Weiner M, Macciardi F, Schork N, Wallace DC, Potkin SG, Alzheimer’s Disease Neuroimaging Initiative, 2010. Association between mitochondrial DNA variations and Alzheimer’s disease in the ADNI cohort. Neurobiol. Aging 31, 1355–1363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Morón FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fiévet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossù P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, European Alzheimer’s Disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer’s Disease, Alzheimer’s Disease Genetic Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O’Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley TH Jr., Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nöthen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P, 2013. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet 45, 1452–1458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lehner B, 2011. Molecular mechanisms of epistasis within and between genes. Trends Genet. 27, 323–331. [DOI] [PubMed] [Google Scholar]
  28. Maruszak A, Safranow K, Branicki W, Gawęda-Walerych K, Pośpiech E, Gabryelewicz T, Canter JA, Barcikowska M, Zekanowski C, 2011. The impact of mitochondrial and nuclear DNA variants on late-onset Alzheimer’s disease risk. J. Alzheimers. Dis 27, 197–210. [DOI] [PubMed] [Google Scholar]
  29. Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL, 2015. Alzheimer’s disease. Nat. Rev. Dis. Primers 1, 15056. [DOI] [PubMed] [Google Scholar]
  30. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K, Luo Y, Sidore C, Kwong A, Timpson N, Koskinen S, Vrieze S, Scott LJ, Zhang H, Mahajan A, Veldink J, Peters U, Pato C, van Duijn CM, Gillies CE, Gandin I, Mezzavilla M, Gilly A, Cocca M, Traglia M, Angius A, Barrett JC, Boomsma D, Branham K, Breen G, Brummett CM, Busonero F, Campbell H, Chan A, Chen S, Chew E, Collins FS, Corbin LJ, Smith GD, Dedoussis G, Dorr M, Farmaki A-E, Ferrucci L, Forer L, Fraser RM, Gabriel S, Levy S, Groop L, Harrison T, Hattersley A, Holmen OL, Hveem K, Kretzler M, Lee JC, McGue M, Meitinger T, Melzer D, Min JL, Mohlke KL, Vincent JB, Nauck M, Nickerson D, Palotie A, Pato M, Pirastu N, McInnis M, Richards JB, Sala C, Salomaa V, Schlessinger D, Schoenherr S, Slagboom PE, Small K, Spector T, Stambolian D, Tuke M, Tuomilehto J, Van den Berg LH, Van Rheenen W, Volker U, Wijmenga C, Toniolo D, Zeggini E, Gasparini P, Sampson MG, Wilson JF, Frayling T, de Bakker PIW, Swertz MA, McCarroll S, Kooperberg C, Dekker A, Altshuler D, Willer C, Iacono W, Ripatti S, Soranzo N, Walter K, Swaroop A, Cucca F, Anderson CA, Myers RM, Boehnke M, McCarthy MI, Durbin R, Haplotype Reference Consortium, 2016. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet 48, 1279–1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. McInerney TW, Fulton-Howard B, Patterson C, Paliwal D, Jermiin LS, Patel H, Pa J, Swerdlow RH, Goate A, Easteal S, Andrews SJ, for the Alzheimer’s Disease Neuroimaging Initiative, 2019. MitoImpute: a Snakemake pipeline for imputation of mitochondrial genetic variants. bioRxiv 1–22. [Google Scholar]
  32. Mhatre SD, Tsai CA, Rubin AJ, James ML, Andreasson KI, 2015. Microglial malfunction: the third rail in the development of Alzheimer’s disease. Trends Neurosci. 38, 621–636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Morrow EH, Camus MF, 2017. Mitonuclear epistasis and mitochondrial disease. Mitochondrion 35, 119–122. [DOI] [PubMed] [Google Scholar]
  34. Mostafavi S, Gaiteri C, Sullivan SE, White CC, Tasaki S, Xu J, Taga M, Klein HU, Patrick E, Komashko V, McCabe C, Smith R, Bradshaw EM, Root DE, Regev A, Yu L, Chibnik LB, Schneider JA, Young-Pearse TL, Bennett DA, De Jager PL, 2018. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat. Neurosci 21, 811–819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mueller EE, Brunner SM, Mayr JA, Stanger O, Sperl W, Kofler B, 2012. Functional differences between mitochondrial haplogroup T and haplogroup H in HEK293 cybrid cells. PLoS One 7, e52367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Navarro-Gomez D, Leipzig J, Shen L, Lott M, Stassen APM, Wallace DC, Wiggs JL, Falk MJ, van Oven M, Gai X, 2015. Phy-Mer: a novel alignment-free and reference-independent mitochondrial haplogroup classifier. Bioinformatics 31, 1310–1312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Osorio RS, Gumb T, Pirraglia E, Varga AW, Lu S-E, Lim J, Wohlleber ME, Ducca EL, Koushyk V, Glodzik L, Mosconi L, Ayappa I, Rapoport DM, de Leon MJ, Alzheimer’s Disease Neuroimaging Initiative, 2015. Sleep-disordered breathing advances cognitive decline in the elderly. Neurology 84, 1964–1971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Panza F, Lozupone M, Logroscino G, Imbimbo BP, 2019. A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat. Rev. Neurol 15, 73–88. [DOI] [PubMed] [Google Scholar]
  39. Perez Ortiz JM, Swerdlow RH, 2019. Mitochondrial dysfunction in Alzheimer’s disease: role in pathogenesis and novel therapeutic opportunities. Br. J. Pharmacol 176, 3489–3507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC, 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Ridge PG, Kauwe JSK, 2018. Mitochondria and Alzheimer’s disease: the role of mitochondrial genetic variation. Curr. Genet. Med. Rep 6, 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Ridge PG, Koop A, Maxwell TJ, Bailey MH, Swerdlow RH, Kauwe JSK, Honea RA, Alzheimer’s Disease Neuroimaging Initiative, 2013. Mitochondrial haplotypes associated with biomarkers for Alzheimer’s disease. PLoS One 8, e74158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Ridge PG, Wadsworth ME, Miller JB, Saykin AJ, Green RC, , Alzheimer’s Disease Neuroimaging Initiative, Kauwe JSK, 2018. Assembly of 809 whole mitochondrial genomes with clinical, imaging, and fluid biomarker phenotyping. Alzheimers. Dement 14, 514–519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, Ramanan VK, Foroud TM, Faber KM, Sarwar N, Munsie LM, Hu X, Soares HD, Potkin SG, Thompson PM, Kauwe JSK, Kaddurah-Daouk R, Green RC, Toga AW, Weiner MW, Alzheimer’s Disease Neuroimaging Initiative, 2015. Genetic studies of quantitative MCI and AD phenotypes in ADNI: progress, opportunities, and plans. Alzheimers. Dement 11, 792–814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Swerdlow RH, 2018. Mitochondria and mitochondrial cascades in Alzheimer’s disease. J. Alzheimers. Dis 62, 1403–1416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Taanman JW, 1999. The mitochondrial genome: structure, transcription, translation and replication. Biochim. Biophys. Acta 1410, 103–123. [DOI] [PubMed] [Google Scholar]
  47. Thaker K, Chwa M, Atilano SR, Coskun P, Cáceres-Del-Carpio J, Udar N, Boyer DS, Jazwinski SM, Miceli MV, Nesburn AB, Kuppermann BD, Kenney MC, 2016. Increased expression of ApoE and protection from amyloid-beta toxicity in transmitochondrial cybrids with haplogroup K mtDNA. Neurobiol. Dis 93, 64–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. van Oven M, Kayser M, 2009. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat 30, E386–E394. [DOI] [PubMed] [Google Scholar]
  49. Weissensteiner H, Pacher D, Kloss-Brandstätter A, Forer L, Specht G, Bandelt H-J, Kronenberg F, Salas A, Schönherr S, 2016. HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res. 44, W58–W63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Zaidi AA, Makova KD, 2019. Investigating mitonuclear interactions in human admixed populations. Nat. Ecol. Evol 3, 213–222. [DOI] [PMC free article] [PubMed] [Google Scholar]

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