Skip to main content

Advertisement

SpringerLink
Log in

New Genetic Approaches to AD: Lessons from APOE-TOMM40 Phylogenetics

  • Dementia (KS Marder, Section Editor)
  • Published:
Current Neurology and Neuroscience Reports Aims and scope Submit manuscript

Abstract

Clinical trials for Alzheimer’s disease are now focusing on the earliest stages of the disease with the goal of delaying dementia onset. There is great utility in using genetic variants to identify individuals at high age-dependent risk when the goal is to begin treatment before the development of any cognitive symptoms. Genetic variants identified through large-scale genome-wide association studies have not substantially improved the accuracy provided by APOE genotype to identify people at high risk of late-onset Alzheimer’s disease (LOAD). We describe novel approaches, focused on molecular phylogenetics, to finding genetic variants that predict age at LOAD onset with sufficient accuracy and precision to be useful. We highlight the discovery of a polymorphism in TOMM40 that, in addition to APOE, may improve risk prediction and review how TOMM40 genetic variants may impact the develop of LOAD independently from APOE. The analysis methods described in this review may be useful for other genetically complex human diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6(4):37.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mayeux R et al. Operationalizing diagnostic criteria for Alzheimer’s disease and other age-related cognitive impairment-Part 1. Alzheimers Dement. 2011;7(1):15–34.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Albert MS et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270–9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Reiman, E.M., et al. CAP-advancing the evaluation of preclinical Alzheimer disease treatments. Nat Rev Neurol. 2016;12(1):56–61.

  5. Pericak-Vance MA et al. Linkage studies in familial Alzheimer disease: evidence for chromosome 19 linkage. Am J Hum Genet. 1991;48(6):1034–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Corder EH et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3.

    Article  CAS  PubMed  Google Scholar 

  7. Liu N et al. Haplotype-Association Analysis. Adv Genet. 2008;60:335–405.

  8. Schmechel DE et al. Increased amyloid beta-peptide deposition in cerebral cortex as a consequence of apolipoprotein E genotype in late-onset Alzheimer disease. Proc Natl Acad Sci. 1993;90(20):9649–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Saunders AM et al. Association of apolipoprotein E allele ε4 with late-onset familial and sporadic Alzheimer’s disease. Neurology. 1993;43(8):1467–72.

    Article  CAS  PubMed  Google Scholar 

  10. Medway C, Morgan K. Review: the genetics of Alzheimer’s disease; putting flesh on the bones. Neuropathol Appl Neurobiol. 2014;40(2):97–105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lambert J-C et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45(12):1452–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chouraki, V. and S. Seshadri, Genetics of Alzheimer’s disease. Adv Genet, 2014. 87: p. 245-94. This is a comprehensive review of the genetics of Alzheimer’s Disease that includes relevant discussion about structural variants, rare variants, untangling the genetics of the APOE-TOMM40 region and gene-environment interactions. Translational genetics are addressed with emphasis on using genetics for risk prediction and for identification of new drug targets.

  13. Hollingworth P et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011;43(5):429-35.

  14. Goldstein DB. Common genetic variation and human traits. N Engl J Med. 2009;360(17):1696–8.

    Article  CAS  PubMed  Google Scholar 

  15. Ridge PG et al. Alzheimer’s disease: analyzing the missing heritability. PLoS One. 2013;8(11):e79771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gatz M et al. Role of Genes and Environments for Explaining Alzheimer Disease. Arch Gen Psychiatry. 2006;63(2):168–74.

    Article  PubMed  Google Scholar 

  17. De Stefano A, S.S, Fitzpatrick A, Ikram M, Gudnason V, Boada M, et al. Genome-wide association studies using prospective cohorts: the CHARGE consortium. Alzheimers Dement. 2011;7(4):S480.

    Google Scholar 

  18. Harold D et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009;41(10):1088–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. MacArthur DG et al. Guidelines for investigating causality of sequence variants in human disease. Nature. 2014;508(7497):469–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhu Q et al. Prioritizing genetic variants for causality on the basis of preferential linkage disequilibrium. Am J Hum Genet. 2012;91(3):422–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bochdanovits Z et al. Accurate prediction of a minimal region around a genetic association signal that contains the causal variant. Eur J Hum Genet. 2014;22(2):238–42.

  22. Teo YY et al. Identifying candidate causal variants via trans-population fine-mapping. Genet Epidemiol. 2010;34(7):653–64.

    Article  PubMed  Google Scholar 

  23. Liu Y et al. A regression-based association test for case-control studies that uses inferred ancestral haplotype similarity. Ann Hum Genet. 2009;73(Pt 5):520–6.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Roses AD et al. A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J. 2010;10(5):375–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Liu Y et al. IL-6 haplotypes, inflammation, and risk for cardiovascular disease in a multiethnic dialysis cohort. J Am Soc Nephrol. 2006;17(3):863–70.

    Article  CAS  PubMed  Google Scholar 

  26. Vardarajan BN et al. Coding mutations in SORL1 and Alzheimer disease. Ann Neurol. 2015;77(2):215–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mahley RW, Weisgraber KH, Huang Y. Apolipoprotein E: structure determines function, from atherosclerosis to Alzheimer’s disease to AIDS. J Lipid Res. 2009;50(Suppl):S183–8.

    PubMed  PubMed Central  Google Scholar 

  28. Holtzman DM, Herz J, Bu G. Apolipoprotein E and apolipoprotein E receptors: normal biology and roles in Alzheimer disease. Cold Spring Harb Perspect Med. 2012;2:3.

    Google Scholar 

  29. Linnertz C, A.L., Gottschalk W, Crenshaw D, Lutz MW, Allen J, Saith S, Mihovilovic M, Burke JR, Welsh-Bohmer KA, Roses AD, Chiba-Falek O, The cis-regulatory effect of an Alzheimer’s disease-associated poly-T locus on expression of TOMM40 and APOE genes. Alzheimers & Dement. 2014;10(5):541–51.

  30. Allen M et al. Novel late-onset Alzheimer disease loci variants associate with brain gene expression. Neurology. 2012;79(3):221–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Roses AD et al. New applications of disease genetics and pharmacogenetics to drug development. Curr Opin Pharmacol. 2014;14:81–9.

    Article  CAS  PubMed  Google Scholar 

  32. Cruchaga C et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS One. 2012;7(2):e31039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sassi C et al. Investigating the role of rare coding variability in Mendelian dementia genes (APP, PSEN1, PSEN2, GRN, MAPT, and PRNP) in late-onset Alzheimer’s disease. Neurobiol Aging. 2014;35(12):2881. e1-6.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jonsson T et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature. 2012;488(7409):96–9.

    Article  CAS  PubMed  Google Scholar 

  35. Medway CW et al. ApoE variant p.V236E is associated with markedly reduced risk of Alzheimer’s disease. Mol Neurodegener. 2014;9:11.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lord J, Lu AJ, Cruchaga C. Identification of rare variants in Alzheimer’s disease. Front Genet. 2014;5:369.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ringman J et al. Genetic Heterogeneity in Alzheimer Disease and Implications for Treatment Strategies. Curr Neurol Neurosci Rep. 2014;14(11):1–9. This study advanced the idea that AD is heterogeneous and that different genetic subtypes of the disease may not uniformly respond to a given intervention. Examples from the recently failed AD therapeutic trials are shared to illustrate the importance of considering biologic pathway differences associated with different genetic origins when designing clinical trials.

    Article  CAS  Google Scholar 

  38. Beckett LA et al. The Alzheimer’s disease neuroimaging initiative phase 2: increasing the length, breadth, and depth of our understanding. Alzheimers Dement. 2015;11(7):823–31.

    Article  PubMed  Google Scholar 

  39. Nettiksimmons J et al. Biological heterogeneity in ADNI amnestic mild cognitive impairment. Alzheimers Dement. 2014;10(5):511–21. e1.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Browning S. Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet. 2008;124(5):439–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schaid DJ. Evaluating associations of haplotypes with traits. Genet Epidemiol. 2004;27(4):348–64.

    Article  PubMed  Google Scholar 

  42. Bader JS. The relative power of SNPs and haplotype as genetic markers for association tests. Pharmacogenomics. 2001;2(1):11–24.

    Article  CAS  PubMed  Google Scholar 

  43. Akey J, Jin L, Xiong M. Haplotypes vs single marker linkage disequilibrium tests: what do we gain? Eur J Hum Genet. 2001;9(4):291–300.

    Article  CAS  PubMed  Google Scholar 

  44. Morris RW, Kaplan NL. On the advantage of haplotype analysis in the presence of multiple disease susceptibility alleles. Genet Epidemiol. 2002;23(3):221–33.

    Article  PubMed  Google Scholar 

  45. Raelson JV et al. Genome-wide association study for Crohn’s disease in the Quebec Founder Population identifies multiple validated disease loci. Proc Natl Acad Sci U S A. 2007;104(37):14747–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee JH et al. A haplotype-based molecular analysis of CFTR mutations associated with respiratory and pancreatic diseases. Hum Mol Genet. 2003;12(18):2321–32.

    Article  CAS  PubMed  Google Scholar 

  47. Bekris LM et al. APOE mRNA and protein expression in postmortem brain are modulated by an extended haplotype structure. Am J Med Genet Part B. 2010;153B(2):409–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Tan EK et al. SORL1 haplotypes modulate risk of Alzheimer’s disease in Chinese. Neurobiol Aging. 2009;30(7):1048–51.

    Article  CAS  PubMed  Google Scholar 

  49. Reitz C et al. Meta-analysis of the association between variants in SORL1 and Alzheimer disease. Arch Neurol. 2011;68(1):99–106.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Templeton AR, Crandall KA, Sing CF. A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping and DNA Sequence Data, III Cladogram Estimation. Genetics. 1992;132(2):619–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Clark TG, De Iorio M, Griffiths RC. Bayesian logistic regression using a perfect phylogeny. Biostatistics. 2007;8(1):32–52.

    Article  PubMed  Google Scholar 

  52. Minichiello MJ, Durbin R. Mapping trait loci by use of inferred ancestral recombination graphs. Am J Hum Genet. 2006;79(5):910–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Morris AP, Whittaker JC, Balding DJ. Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies. AmJ Hum Genet. 2002;70(3):686–707.

    Article  CAS  Google Scholar 

  54. Zöllner S, Pritchard JK. Coalescent-based association mapping and fine mapping of complex trait loci. Genetics. 2005;169(2):1071–92.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bardel C et al. On the use of haplotype phylogeny to detect disease susceptibility loci. BMC Genet. 2005;6(1):24–37.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ding ZH, Mailund T, Song YS. Efficient whole-genome association mapping using local phylogenies for unphased genotype data. Bioinformatics. 2008;24(19):2215–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Durrant C et al. Linkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes. Am J Hum Genet. 2004;75(1):35–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu J, Papasian C, Deng HW. Incorporating single-locus tests into haplotype cladistic analysis in case-control studies. PLoS Genet. 2007;3(3):e46.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Mailund T, Besenbacher S, Schierup MH. Whole genome association mapping by incompatibilities and local perfect phylogenies. BMC Bioinformatics. 2006;7:22.

    Article  Google Scholar 

  60. Seltman H, Roeder K, Devlin B. Evolutionary-based association analysis using haplotype data. Genet Epidemiol. 2003;25(1):48–58.

    Article  PubMed  Google Scholar 

  61. Sevon P, Toivonen H, Ollikainen V. TreeDT: tree pattern mining for gene mapping. IEEE/ACM Trans Comput Biol Bioinform. 2006;3(2):174–85.

    Article  CAS  PubMed  Google Scholar 

  62. Su SY, Balding DJ, Coin LJ. Disease association tests by inferring ancestral haplotypes using a hidden markov model. Bioinformatics. 2008;24(7):972–8.

    Article  CAS  PubMed  Google Scholar 

  63. Tachmazidou I, Verzilli C, Iorio M. Genetic association mapping via evolution-based clustering of haplotypes. PLoS Genet. 2007;3(7):e111.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Thomas DC et al. Bayesian spatial modeling of haplotype associations. Hum Hered. 2003;56(1-3):32–40.

    Article  PubMed  Google Scholar 

  65. Tzeng JY. Evolutionary-based grouping of haplotypes in association analysis. Genet Epidemiol. 2005;28(3):220–31.

    Article  PubMed  Google Scholar 

  66. Waldron ER, Whittaker JC, Balding DJ. Fine mapping of disease genes via haplotype clustering. Genet Epidemiol. 2006;30(2):170–9.

    Article  CAS  PubMed  Google Scholar 

  67. Templeton AR. The diverse applications of cladistic analysis of molecular evolution, with special reference to nested clade analysis. Int J Mol Sci. 2010;11(1):124–39. Templeton describes the application of evolutionary genealogy of DNA lineages, including intraspecific studies, for genotype/phenotype associations at candidate loci and in genome-wide association studies. The author also establishes the theoretical framework of using phylogenetic analysis to untangle a region of high linkage disequilibrium for a genotype/phenotype association study.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Browning BL, Browning SR. Haplotypic analysis of Wellcome Trust Case Control Consortium data. Hum Genet. 2008;123(3):273–80.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Browning, S.R. and B.L. Browning, Haplotype phasing: existing methods and new developments. Nat Rev Genet. 2011;12(10):703–14

  70. Crenshaw DG et al. Using genetics to enable studies on the prevention of Alzheimer’s disease. Clin Pharmacol Ther. 2013;93(2):177–85. This paper presented the use of a composite genetic biomarker to stratify for disease risk in clinical studies aimed at delaying or preventing the onset of AD in individuals at high risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Roses AD et al. TOMM40 and APOE: requirements for replication studies of association with age of disease onset and enrichment of a clinical trial. Alzheimers Dement. 2013;9(2):132–6.

    Article  PubMed  Google Scholar 

  72. Johnson SC et al. The effect of TOMM40 poly-T length on gray matter volume and cognition in middle-aged persons with APOE ɛ3/ɛ3 genotype. Alzheimer’s Dementia. 2011;7(4):456–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hayden KM et al. A homopolymer polymorphism in the TOMM40 gene contributes to cognitive performance in aging. Alzheimers Dement. 2012;8(5):381–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Huson DH, Scornavacca C. A survey of combinatorial methods for phylogenetic networks. Genome Biol Evol. 2011;3:23–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tewhey R et al. The importance of phase information for human genomics. Nat Rev Genet. 2011;12(3):215–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Dickson SP et al. Rare variants create synthetic genome-wide associations. PLoS Biol. 2010;8(1), e1000294.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lescai, F., et al., An APOE haplotype associated with decreased epsilon4 expression increases the risk of late onset Alzheimer’s disease. J Alzheimers Dis. 2011;24(2):235–45.

  78. Hendrie HC et al. Incidence of dementia and Alzheimer disease in 2 communities: Yoruba residing in Ibadan, Nigeria, and African Americans residing in Indianapolis, Indiana. JAMA. 2001;285(6):739–47.

    Article  CAS  PubMed  Google Scholar 

  79. Hendrie HC et al. Prevalence of Alzheimer’s disease and dementia in two communities: Nigerian Africans and African Americans. Am J Psychiatry. 1995;152(10):1485–92.

    Article  CAS  PubMed  Google Scholar 

  80. Hall KS et al. Prevalence rates for dementia and Alzheimer’s disease in African Americans: 1992 versus 2001. Alzheimers Dement. 2009;5(3):227–33.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Tang MX et al. Relative risk of Alzheimer disease and age-at-onset distributions, based on APOE genotypes among elderly African Americans, Caucasians, and Hispanics in New York City. Am J Hum Genet. 1996;58(3):574–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Tang MX et al. Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan. Neurology. 2001;56(1):49–56.

    Article  CAS  PubMed  Google Scholar 

  83. Roses AD et al. African-American TOMM40’523-APOE haplotypes are admixture of West African and Caucasian alleles. Alzheimers Dement. 2014;10(6):592–601. e2.

    Article  PubMed  Google Scholar 

  84. Bekris LM, Lutz F, Yu C-E. Functional analysis of APOE locus genetic variation implicates regional enhancers in the regulation of both TOMM40 and APOE. J Hum Genet. 2012;57(1):18–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Jun G et al. Comprehensive search for Alzheimer disease susceptibility loci in the APOE region. Arch Neurol. 2012;69(10):1270–9.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Cruchaga C et al. Association and expression analyses with single-nucleotide polymorphisms in TOMM40 in Alzheimer disease. Arch Neurol. 2011;68(8):1013–9.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Li G et al. TOMM40 intron 6 poly-T length, age at onset, and neuropathology of AD in individuals with APOE epsilon3/epsilon3. Alzheimers Dement. 2013;9(5):554–61.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Akai J, Kimura A, Hata RI. Transcriptional regulation of the human type I collagen alpha2 (COL1A2) gene by the combination of two dinucleotide repeats. Gene. 1999;239(1):65–73.

    Article  CAS  PubMed  Google Scholar 

  89. Chiba-Falek O, Nussbaum RL. Effect of allelic variation at the NACP-Rep1 repeat upstream of the alpha-synuclein gene (SNCA) on transcription in a cell culture luciferase reporter system. Hum Mol Genet. 2001;10(26):3101–9.

    Article  CAS  PubMed  Google Scholar 

  90. Okladnova O et al. A promoter-associated polymorphic repeat modulates PAX-6 expression in human brain. Biochem Biophys Res Commun. 1998;248(2):402–5.

    Article  CAS  PubMed  Google Scholar 

  91. Peters DG et al. Functional polymorphism in the matrix metalloproteinase-9 promoter as a potential risk factor for intracranial aneurysm. Stroke. 1999;30(12):2612–6.

    Article  CAS  PubMed  Google Scholar 

  92. Searle S, Blackwell JM. Evidence for a functional repeat polymorphism in the promoter of the human NRAMP1 gene that correlates with autoimmune versus infectious disease susceptibility. J Med Genet. 1999;36(4):295–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Shimajiri S et al. Shortened microsatellite d(CA)21 sequence down-regulates promoter activity of matrix metalloproteinase 9 gene. FEBS Lett. 1999;455(1-2):70–4.

    Article  CAS  PubMed  Google Scholar 

  94. Hefferon TW et al. A variable dinucleotide repeat in the CFTR gene contributes to phenotype diversity by forming RNA secondary structures that alter splicing. Proc Natl Acad Sci U S A. 2004;101(10):3504–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Mirkin SM. Expandable DNA repeats and human disease. Nature. 2007;447(7147):932–40.

    Article  CAS  PubMed  Google Scholar 

  96. Pearson CE, Nichol Edamura K, Cleary JD. Repeat instability: mechanisms of dynamic mutations. Nat Rev Genet. 2005;6(10):729–42.

    Article  CAS  PubMed  Google Scholar 

  97. Willems T et al. The landscape of human STR variation. Genome Res. 2014;24(11):1894–904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Sudmant PH et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526(7571):75–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Linnertz C et al. Characterization of the poly-T variant in the TOMM40 gene in diverse populations. PLoS One. 2012;7(2):e30994.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Gottschalk WK et al. The Broad Impact of TOM40 on Neurodegenerative Diseases in Aging. J Parkinson’s Dis Alzheimer’s Dis. 2014;1(1):12. This article summarizes the molecular and genetic evidence relating Tom40 protein and the Translocase of the Outer Mitochondrial Membrane complex to mitochondrial dysfunction and neurological disease, including LOAD and Parkinson’s disease.

    Google Scholar 

  101. Bender A et al. TOM40 mediates mitochondrial dysfunction induced by alpha-synuclein accumulation in Parkinson’s disease. PLoS One. 2013;8(4):e62277.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Joseph and Kathleen Bryan Alzheimer’s Disease Center, Department of Neurology, Duke University Medical Center, 2200 W Main Street, Suite A200, Durham, NC, 27705, USA

    Michael W. Lutz, Kathleen A. Welsh-Bohmer & Allen D. Roses

  2. Zinfandel Pharmaceuticals, Inc., Durham, NC, USA

    Donna Crenshaw, Daniel K. Burns & Allen D. Roses

Authors
  1. Michael W. Lutz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donna Crenshaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kathleen A. Welsh-Bohmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel K. Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Allen D. Roses
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kathleen A. Welsh-Bohmer.

Ethics declarations

Conflict of Interest

Michael Lutz has received a grant from NIA/NIH and personal fees from Zinfandel Pharmaceuticals.

Donna Crenshaw is a paid consultant to Zinfandel Pharmaceuticals.

Kathleen Welsh-Bohmer has received a grant from Takeda Pharmaceutical Company.

Daniel K. Burns is an employee of Zinfandel Pharmaceuticals.

Allen D. Roses is sole shareholder of NC S-Corp. (Zinfandel Pharmaceuticals, Inc.)

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical Collection on Dementia

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lutz, M.W., Crenshaw, D., Welsh-Bohmer, K.A. et al. New Genetic Approaches to AD: Lessons from APOE-TOMM40 Phylogenetics. Curr Neurol Neurosci Rep 16, 48 (2016). https://doi.org/10.1007/s11910-016-0643-8

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s11910-016-0643-8

Keywords

Search

Navigation