Abstract
In this chapter, genetic modifiers are defined, and the rationale for investigating them in HD explained. Issues involved in modeling the phenotype are discussed, using age at motor onset as an example. The statistical methods for analyzing genetic data (linkage and association) are discussed, along with the advantages and disadvantages of each. In particular, the advantage of a genome-wide approach over one based on candidate genes is stressed. Genome-wide association studies (GWAS) are current method of choice to detect genetic modifiers. The power of GWAS is discussed, along with sources of error, and how these might be detected and corrected. Extensions to GWAS, such as gene- and pathway-wide analyses, are discussed, and also how GWAS may be used to estimate genetic risks and trait heritability. Since GWAS are most effective to detect common genetic variants, methods for analyzing rare variation are also discussed. The uses of other types of genomic data (notably, expression) are discussed, and how they might be integrated with genetic data to find causal genes and variants. The chapter ends with a short overview of future prospects for detecting genetic modifiers of HD.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Génin E, Feingold J, Clerget-Darpoux F (2008) Identifying modifier genes of monogenic disease: strategies and difficulties. Hum Genet 124:357–368
Futuyma DJ (1998) Evolutionary biology, 3rd edn. Sinauer, MA
Hall J, Horton W (1997) Genetics glossary. Growth Genet Horm J. online available from: http://www.kumc.edu/gec/gloss.html
Suzuki T, Kashiwagi A, Mori K et al (2004) History dependent eVects on phenotypic expression of a newly emerged gene. Biosystems 77:137–141
Grüneberg H (1963) The pathology of development; a study of inherited skeletal disorders in animals. Wiley, New York
Bečanović K, Nørremølle A, Neal SJ et al (2015) A SNP in the HTT promoter alters NF-κB binding and is a bidirectional genetic modifier of Huntington disease. Nat Neurosci 18:807–816
Bates GP, Dorsey R, Gusella JF et al (2015) Huntington disease. Nat Rev Dis Primers 1:15005
Gusella JF, MacDonald ME, Lee JM (2014) Genetic modifiers of Huntington’s disease. Mov Disord 29:1359–1365
Wexler NS, Lorimer J, Porter J et al (2004) Venezuelan kindreds reveal that genetic and environmental factors modulate Huntington’s disease age of onset. Proc Natl Acad Sci U S A 101:3498–3503
Nelson MR, Tipney H, Painter JL et al (2015) The support of human genetic evidence for approved drug indications. Nat Genet 47:856–860
Manolio TA, Collins FS, Cox NJ et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753
Chapman JM, Cooper JD, Todd JA, Clayton DG (2003) Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum Hered 56:18–31
Everett E, Holmans P, Jones L, the Registry Investigators (2014) The effect of country of origin on the age of onset – CAG repeat length relationship in Huntington’s disease In Europe. J Neurol Neurosurg Psychiatry 85:A76
Lee JM, Ramos EM, Lee JH et al (2012) HD-MAPS Study Group; COHORT study of the HSG CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashion. Neurology 78:690–695
Langbehn DR, Hayden MR, Paulsen JS et al (2010) CAG-repeat length and the age of onset in Huntington disease (HD): a review and validation study of statistical approaches. Am J Med Genet B Neuropsychiatr Genet 153B:397–408
Genetic Modifiers of Huntington’s Disease (GeM-HD) Consortium (2015) Identification of genetic factors that modify clinical onset of Huntington’s disease. Cell 162:516–526
Almasy L, Blangero J (1998) Multipoint quantitative trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211
Huang Q, Shete S, Amos CI (2004) Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am J Hum Genet 75:1106–1112
Li JL, Hayden MR, Almqvist EW et al (2003) A genome scan for modifiers of age at onset in Huntington disease: the HD MAPS study. Am J Hum Genet 73:682–687
Li JL, Hayden MR, Warby SC et al (2006) Genome-wide significance for a modifier of age at neurological onset in Huntington’s disease at 6q23-24: the HD MAPS study. BMC Med Genet 7:71
Gayán J, Brocklebank D, Andresen JM et al (2008) Genome-wide linkage scan reveals novel loci modifying age of onset of Huntington’s disease in the Venezuelan HD kindreds. Genet Epidemiol 32:445–453
Gilissen C, Hoischen A, Brunner HG, Veltman JA (2012) Disease gene identification strategies for exome sequencing. Eur J Hum Genet 20:490–497
Ott J, Wang J, Leal SM (2015) Genetic linkage analysis in the age of whole-genome sequencing. Nat Rev Genet 16:275–284
Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517
Weiss LA, Arking DE, Daly MJ et al (2009) A genome-wide linkage and association scan reveals novel loci for autism. Nature 461:802–808
Rubinsztein DC, Leggo J, Chiano M et al (1997) Genotypes at the GluR6 kainate receptor locus are associated with variation in the age of onset of Huntington disease. Proc Natl Acad Sci U S A 94:3872–3876
Lee JH, Lee JM, Ramos EM et al (2012) TAA repeat variation in the GRIK2 gene does not influence age at onset in Huntington’s disease. Biochem Biophys Res Commun 424:404–408
Metzger S, Saukko M, Van Che H et al (2010) Age at onset in Huntington’s disease is modified by the autophagy pathway: implication of the V471A polymorphism in Atg7. Hum Genet 128:453–459
Metzger S, Walter C, Riess O et al (2013) The V471A polymorphism in autophagy-related gene ATG7 modifies age at onset specifically in Italian Huntington disease patients. PLoS One 8:e68951
Taherzadeh-Fard E, Saft C, Andrich J et al (2009) PGC-1alpha as modifier of onset age in Huntington disease. Mol Neurodegener 4:10
Weydt P, Soyal SM, Gellera C et al (2009) The gene coding for PGC-1alpha modifies age at onset in Huntington’s disease. Mol Neurodegener 4:3
Che HV, Metzger S, Portal E et al (2011) Localization of sequence variations in PGC-1α influence their modifying effect in Huntington disease. Mol Neurodegener 6:1
Ioannidis JP, Tarone R, McLaughlin JK (2011) The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 22:450–456
Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2(8):e124
Button KS, Ioannidis JP, Mokrysz C et al (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376
Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24
Purcell S, Neale B, Todd-Brown K (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
Chang CC, Chow CC, Tellier LC et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7
Aulchenko YS, Ripke S, Isaacs A, van Duijn CM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23:1294–1296
McCarthy MI, Abecasis GR, Cardon LR et al (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356–369
Bush WS, Moore JH (2012) Chapter 11: Genome-wide association studies. PLoS Comput Biol 8:e1002822
Pe'er I, Yelensky R, Altshuler D, Daly MJ (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 32:381–385
Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004
Freedman ML, Reich D, Penney KL et al (2004) Assessing the impact of population stratification on genetic association studies. Nat Genet 36:388–393
Bulik-Sullivan BK, Loh PR, Finucane HK (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295
Price AL, Butler J, Patterson N et al (2008) Discerning the ancestry of European Americans in genetic association studies. PLoS Genet 4:e236
Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909
Purcell SM, Moran JL, Fromer M et al (2014) A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506:185–190
Chen WM, Abecasis GR (2007) Family-based association tests for genome wide association scans. Am J Hum Genet 81:913–926
Lange C, DeMeo DL, Laird NM (2002) Power and design considerations for a general class of family-based association tests: quantitative traits. Am J Hum Genet 71:1330–1341
Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 46:100–106
Loh PR, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284–290
Peloso GM, Rader DJ, Gabriel S et al (2016) Phenotypic extremes in rare variant study designs. Eur J Hum Genet 24:924–930
de Bakker PI, Ferreira MA, Jia X et al (2008) Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 17:R122–R128
McCarthy S, Das S, Kretzschmar W et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279–1283
Marchini J, Howie B (2010) Genotype imputation for genome-wide association studies. Nat Rev Genet 11:499–511
Liu JZ, McRae AF, Nyholt DR et al (2010) A versatile gene-based test for genome-wide association studies. Am J Hum Genet 87:139–145
Li MX, Kwan JS, Sham PC (2012) HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am J Hum Genet 91:478–488
Escott-Price V, Bellenguez C, Wang LS et al (2012) Gene-wide analysis detects two new susceptibility genes for Alzheimer’s disease. PLoS One 9:e94661
Holmans P (2010) Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. Adv Genet 72:141–179
Wang K, Li M, Hakonarson H (2010) Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11:843–854
de Leeuw CA, Neale BM, Heskes T, Posthuma D (2016) The statistical properties of gene-set analysis. Nat Rev Genet 17:353–364
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82
Bulik-Sullivan B, Finucane HK, Anttila V et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241
International Schizophrenia Consortium, Purcell SM, Wray NR et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–752
Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9:e1003348
Euesden J, Lewis CM, O’Reilly PF (2015) PRSice: polygenic risk score software. Bioinformatics 31:1466–1468
Pritchard JK (2001) Are rare variants responsible for susceptibility to complex diseases? Am J Hum Genet 69:124–137
Goldstein DB, Allen A, Keebler J et al (2013) Sequencing studies in human genetics: design and interpretation. Nat Rev Genet 14:460–470
Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5(5):e1000477
Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188–193
Lee S, Wu MC, Lin X (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13:762–775
Wu MC, Lee S, Cai T et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93
Lee S, Choi S, Kim YJ et al (2016) Pathway-based approach using hierarchical components of collapsed rare variants. Bioinformatics 32:i586–i594
Wu G, Zhi D (2013) Pathway-based approaches for sequencing-based genome-wide association studies. Genet Epidemiol 37:478–494
Barnett IJ, Lee S, Lin X (2013) Detecting rare variant effects using extreme phenotype sampling in sequencing association studies. Genet Epidemiol 37:142–151
Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210
Hodges A, Strand AD, Aragaki AK et al (2006) Regional and cellular gene expression changes in human Huntington's disease brain. Hum Mol Genet 15:965–977
Labadorf A, Hoss AG, Lagomarsino V et al (2015) RNA sequence analysis of human Huntington disease brain reveals an extensive increase in inflammatory and developmental gene expression. PLoS One 10(12):e0143563
Mastrokolias A, Ariyurek Y, Goeman JJ et al (2015) Huntington’s disease biomarker progression profile identified by transcriptome sequencing in peripheral blood. Eur J Hum Genet 23:1349–1356
Mina E, van Roon-Mom W, Hettne K et al (2016) Common disease signatures from gene expression analysis in Huntington’s disease human blood and brain. Orphanet J Rare Dis 11:97
Miller JR, Lo KK, Andre R et al (2016) RNA-Seq of Huntington’s disease patient myeloid cells reveals innate transcriptional dysregulation associated with proinflammatory pathway activation. Hum Mol Genet 25:2893–2904
Pinto RM, Dragileva E, Kirby A et al (2013) Mismatch repair genes Mlh1 and Mlh3 modify CAG instability in Huntington’s disease mice: genome-wide and candidate approaches. PLoS Genet 9(10):e1003930
Langfelder P, Cantle JP, Chatzopoulou D et al (2016) Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice. Nat Neurosci 19:623–633
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559
Reimand J, Arak T, Adler P et al (2016) g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 44:W83–W89
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 4:44–57
Clarke C, Madden SF, Doolan P et al (2013) Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis 34:2300–2308
Neueder A, Bates GP (2014) A common gene expression signature in Huntington’s disease patient brain regions. BMC Med Genet 7:60
Pirhaji L, Milani P, Leidl M et al (2016) Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 13:770–776
Langfelder P, Mischel PS, Horvath S (2013) When is hub gene selection better than standard meta-analysis? PLoS One 8(4):e61505
Benner C, Spencer CC, Havulinna AS (2016) FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32:1493–1501
Giambartolomei C, Vukcevic D, Schadt EE et al (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10(5):e1004383
Zhu Z, Zhang F, Hu H et al (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48:481–487
Gusev A, Ko A, Shi H et al (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252
Moore CC, Basile AO, Wallace JR et al (2016) A biologically informed method for detecting rare variant associations. BioData Min 9:27
ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nat Rev Genet 16:197–212
Finucane HK, Bulik-Sullivan B, Gusev A et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228–1235
International Genomics of Alzheimer’s Disease Consortium (2015) Convergent genetic and expression data implicate immunity in Alzheimer’s disease. Alzheimers Dement 11:658–671
Hillenmeyer S, Davis LK, Gamazon ER et al (2016) STAMS: STRING-assisted module search for genome wide association studies and application to autism. Bioinformatics 32:3815–3822
Eddy CM, Parkinson EG, Rickards HE (2016) Changes in mental state and behaviour in Huntington’s disease. Lancet Psychiatry 3:1079–1086
Bettencourt C, Hensman-Moss D, Flower M et al (2016) DNA repair pathways underlie a common genetic mechanism modulating onset in polyglutamine diseases. Ann Neurol 79:983–990
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Holmans, P., Stone, T. (2018). Using Genomic Data to Find Disease-Modifying Loci in Huntington’s Disease (HD). In: Precious, S., Rosser, A., Dunnett, S. (eds) Huntington’s Disease. Methods in Molecular Biology, vol 1780. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7825-0_20
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7825-0_20
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7824-3
Online ISBN: 978-1-4939-7825-0
eBook Packages: Springer Protocols