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Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients.

ABSTRACT: BACKGROUND:BMI is a strong indicator of complications from type I diabetes, especially under intensive treatment. METHODS:We have genotyped 435 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays and performed mitoGWAS on BMI. We identified additive interactions between mitochondrial and nuclear variants in genes associated with mitochondrial functioning MitoCarta2.0 and confirmed and refined the results on external cohorts: the Framingham Heart Study (FHS) and GTEx data. Linear mixed model analysis was performed using the GENESIS package in R/Bioconductor. RESULTS:We find a borderline significant association between the mitochondrial variant rs28357980, localized to MT-ND2, and BMI (??=?-?0.69, p?=?0.056). This BMI association was confirmed on 1889 patients from FHS cohort (??=?-?0.312, p?=?0.047). Next, we searched for additive interactions between mitochondrial and nuclear variants. MT-ND2 variants interacted with variants in the genes SIRT3, ATP5B, CYCS, TFB2M and POLRMT. TFB2M is a mitochondrial transcription factor and together with TFAM creates a transcription promoter complex for the mitochondrial polymerase POLRMT. We have found an interaction between rs3021088 in MT-ND2 and rs6701836 in TFB2M leading to BMI decrease (inter_pval?=?0.0241), while interaction of rs3021088 in MT-ND2 and rs41542013 in POLRMT led to BMI increase (inter_pval?=?0.0004). The influence of these interactions on BMI was confirmed in external cohorts. CONCLUSIONS:Here, we have shown that variants in the mitochondrial genome as well as additive interactions between mitochondrial and nuclear SNPs influence BMI in T1DM and general cohorts.

SUBMITTER: Ludwig-Slomczynska AH

PROVIDER: S-EPMC7341625 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients.

Ludwig-Słomczyńska Agnieszka H AH Seweryn Michał T MT Kapusta Przemysław P Pitera Ewelina E Handelman Samuel K SK Mantaj Urszula U Cyganek Katarzyna K Gutaj Paweł P Dobrucka Łucja Ł Wender-Ożegowska Ewa E Małecki Maciej T MT Wołkow Paweł P PP

BMC medical genomics 20200707 1

<h4>Background</h4>BMI is a strong indicator of complications from type I diabetes, especially under intensive treatment.<h4>Methods</h4>We have genotyped 435 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays and performed mitoGWAS on BMI. We identified additive interactions between mitochondrial and nuclear variants in genes associated with mitochondrial functioning MitoCarta2.0 and confirmed and refined the results on external cohorts: the Framingham Heart Study (FHS) a ...[more]

PMID: 32635923

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Project description:The interplay between genes harboring single nucleotide polymorphisms (SNPs) is vital to better understand underlying contributions to the etiology of breast cancer. Much attention has been paid to epistasis between nuclear genes or mutations in the mitochondrial genome. However, there is limited understanding about the epistatic effects of genetic variants in the nuclear and mitochondrial genomes jointly on breast cancer. We tested the interaction of germline SNPs in the mitochondrial (mtSNPs) and nuclear (nuSNPs) genomes of female breast cancer patients in The Cancer Genome Atlas (TCGA) for association with morphological features extracted from hematoxylin and eosin (H&E)-stained pathology images. We identified 115 significant (q-value < 0.05) mito-nuclear interactions that increased nuclei size by as much as 12%. One interaction between nuSNP rs17320521 in an intron of the WSC Domain Containing 2 (WSCD2) gene and mtSNP rs869096886, a synonymous variant mapped to the mitochondrially-encoded NADH dehydrogenase 4 (MT-ND4) gene, was confirmed in an independent breast cancer data set from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). None of the 10 mito-nuclear interactions identified from non-diseased female breast tissues from the Genotype-Expression (GTEx) project resulted in an increase in nuclei size. Comparisons of gene expression data from the TCGA breast cancer patients with the genotype homozygous for the minor alleles of the SNPs in WSCD2 and MT-ND4 versus the other genotypes revealed core transcriptional regulator interactions and an association with insulin. Finally, a Cox proportional hazards ratio = 1.7 (C.I. 0.98-2.9, p-value = 0.042) and Kaplan-Meier plot suggest that the TCGA female breast cancer patients with low gene expression of WSCD2 coupled with large nuclei have an increased risk of mortality. The intergenomic dependency between the two variants may constitute an inherent susceptibility of a more severe form of breast cancer and points to genetic targets for further investigation of additional determinants of the disease.

Project description:BackgroundPart of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.ResultsGBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.ConclusionThis study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.

Dataset Information

Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients.

Publications

Mitochondrial GWAS and association of nuclear - mitochondrial epistasis with BMI in T1DM patients.

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