B3881 - Associations between mitochondrial and nuclear DNA variants in human populations - 28/09/2021

B number: 
Principal applicant name: 
Gavin Hudson | Newcastle Univerisity (United Kingdom)
Dr Mauro Santibanez-Koref
Title of project: 
Associations between mitochondrial and nuclear DNA variants in human populations
Proposal summary: 

Present in nearly all types of human cells, mitochondria generate the majority of our cellular energy and are thus often referred to as the ‘powerhouse of the cell’. In addition, mitochondria play important roles in signalling between cells and cell death (known as apoptosis). Although most of our DNA is within the nucleus (the ‘nuclear genome’ or nDNA), mitochondria contain their own DNA (the ‘mitochondrial genome’ or mtDNA), and human health is dependent upon the coordination of the products of these two genomes.

Genetic variants within mtDNA can cause disease (1) and are also linked to a growing number of age-related complex diseases (2), particularly neurodegenerative diseases including Parkinson’s disease, Alzheimer’s disease, schizophrenia and multiple sclerosis (3). In addition, there are examples where the progression or severity of mtDNA disease is modulated by nDNA variants (e.g. LHON (4, 5)). Conversely, there is evidence in complex diseases where nuclear susceptibility factors are implicated, that common, inherited, mtDNA variation can influence traits such the age of onset (e.g. Alzheimer’s disease (6) and cardiomyopathy (7)).

More recently, the development of mitochondrial replacement therapy, a technique designed to avoid transmission of defective mitochondria from parents to offspring, sparked discussions on mitochondrial nuclear incompatibilities that would require matching donor and recipient mitochondrial and nuclear backgrounds, and more generally on whether the genetic makeup of healthy individuals reflects such incompatibilities (8).

This raises the question whether specific variant combinations of nuclear and mitochondrial alleles are depleted or enriched in the general population. This can be assessed by comparing their frequencies with the frequencies expected from the frequencies of the corresponding nuclear and the mitochondrial alleles assuming independent segregation. Such associations between variants can reflect mixing of different populations or selection against or in favour of particular variant combinations. Associations between nuclear and mitochondrial variants have been repeatedly reported (9, 10). However, it is unclear to what extent the observations reflect population stratification. This is of particular interest because mitochondrial genetic variants have been extensively used to track migration of populations and ancestry.

The aim of this study is to use the ALSPAC cohort genotyping data to: 1) identify combinations of mitochondrial and nuclear variants that are over or underrepresented in a well-characterised population, 2) ascertain whether under or over representation can be explained by heterogeneity within the population and 3) assess the effects of factors such as age or sex. The results will also provide a reference for studying the role of such combinations in human disease.

Impact of research: 
Based on our preliminary studies in smaller diseased and unselected cohorts, we believe we will be able to detect mitonuclear associations in the ALSPAC cohort. This is also supported by recent studies which have detected co-segregation of mtDNA and nDNA in the population (PMID: 34002094 and 26378221). However, these studies did not assess specific variant combinations or their relevance for disease. Identifying and characterising mitonuclear DNA combinations in the population will improve our understanding of mitochondrial/nuclear interactions at the genetic level, and how common mtDNA variants may contribute to complex disease. References 1. Gorman GS, Chinnery PF, DiMauro S, Hirano M, Koga Y, McFarland R, et al. Mitochondrial diseases. Nature reviews Disease primers. 2016;2:16080. 2. Hudson G, Gomez-Duran A, Wilson IJ, Chinnery PF. Recent mitochondrial DNA mutations increase the risk of developing common late-onset human diseases. PLoS Genet. 2014;10(5):e1004369. 3. Chinnery PF, Gomez-Duran A. Oldies but Goldies mtDNA Population Variants and Neurodegenerative Diseases. Front Neurosci. 2018;12:682. 4. Hudson G, Keers S, Yu-Wai-Man P, Griffiths P, Huoponen K, Savontaus ML, et al. Identification of an X-chromosomal locus and haplotype modulating the phenotype of a mitochondrial DNA disorder. Am J Hum Genet. 2005;77(6):1086-91. 5. Pickett SJ, Grady JP, Ng YS, Gorman GS, Schaefer AM, Wilson IJ, et al. Phenotypic heterogeneity in m.3243A>G mitochondrial disease: The role of nuclear factors. Ann Clin Transl Neurol. 2018;5(3):333-45. 6. Andrews SJ, Fulton-Howard B, Patterson C, McFall GP, Gross A, Michaelis EK, et al. Mitonuclear interactions influence Alzheimer's disease risk. Neurobiol Aging. 2020;87:138 e7- e14. 7. McManus MJ, Picard M, Chen HW, De Haas HJ, Potluri P, Leipzig J, et al. Mitochondrial DNA Variation Dictates Expressivity and Progression of Nuclear DNA Mutations Causing Cardiomyopathy. Cell Metab. 2019;29(1):78-90 e5. 8. Yonova-Doing E, Calabrese C, Gomez-Duran A, Schon K, Wei W, Karthikeyan S, et al. An atlas of mitochondrial DNA genotype-phenotype associations in the UK Biobank. Nat Genet. 2021;53(7):982-93. 9. Sloan DB, Fields PD, Havird JC. Mitonuclear linkage disequilibrium in human populations. Proc Biol Sci. 2015;282(1815). 10. Yamamoto K, Sakaue S, Matsuda K, Murakami Y, Kamatani Y, Ozono K, et al. Genetic and phenotypic landscape of the mitochondrial genome in the Japanese population. Commun Biol. 2020;3(1):104. 11. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559-75. 12. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33(2):272-9. 13. Liu Y, Zhang L, Xu S, Hu L, Hurst LD, Kong X. Identification of two maternal transmission ratio distortion loci in pedigrees of the Framingham heart study. Sci Rep. 2013;3:2147.
Date proposal received: 
Tuesday, 21 September, 2021
Date proposal approved: 
Tuesday, 28 September, 2021
Genetics, Bone disorders - arthritis, osteoporosis, GWAS, Statistical methods, Genetics, Genomics, Genome wide association study