The Relationship Between CAG Repeat Length and Age of Onset ...

doi: 10.1111/j.1469-1809.2006.00335.x

The Relationship Between CAG Repeat Length and Age of Onset Differs for Huntington’s Disease Patients with Juvenile Onset or Adult Onset ´ 2 , Luc Djousse´ 3 , Simone Roberts4 , Denise Brocklebank2 , J. Michael Andresen1, ∗ , Javier Gayan 5 Stacey S. Cherny , The US-Venezuela Collaborative Research Group6 , The HD MAPS Collaborative Research Group7 , Lon R. Cardon2 , James F. Gusella8 , Marcy E. MacDonald8 , Richard H. Myers3 , David E. Housman1 and Nancy S. Wexler4 1 Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA 2 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK 3 Boston University School of Medicine, 715 Albany Street, Boston, MA 02118, USA 4 Columbia University, 1051 Riverside Drive, Unit 6, New York, NY 10032, USA 5 Department of Psychiatry and Genome Research Centre, University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong 6 The U.S.-Venezuela Collaborative Research Project: Nancy S. Wexler a , Judith Lorimer a , Julie Porter a , Fidela Gomeza , Carol Moskowitza , Kelly Posner Gerstenhaber a , Edith Shackella , Karen Marder a , Graciela Penchaszadeha , Simone A. Robertsa , Adam Brickmana , Javier Gayánb , Denise Brocklebankb , Stacey S. Chernyb , Lon R. Cardonb , Jacqueline Grayc , Stephen R. Dlouhyc , Sandra Wiktorskic , Marion E. Hodesc,† , P. Michael Conneallyc , John B. Penneyd,† , James F. Gusellad , Jang-Ho Chad , Michael Irizarryd , Diana Rosasd , Steven Herschd , Zane Hollingsworthd , Marcy E. MacDonaldd , Anne B. Youngd , J. Michael Andresene , David E. Housmane , Margot Mieja de Youngf , Ernesto Bonillaf , Theresa Stillingsf , Americo Negrettef ,† , S. Robert Snodgrassg , Maria Dolores Martinez-Jaurrietah , Maria A. Ramos-Arroyoh , Jacqueline Bickhami , Juan Sanchez Ramosj , Frederick Marshallk , Ira Shoulsonk , Gustavo J. Reyl , Andrew Feiginm , Norman Arnheimn , Amarilis Acevedo-Cruzo , Leticia Acostap , Jose Alvir q , Kenneth Fischbeckr , Leslie M. Thompsons , Angela Youngt , Leon Duret , Christopher J. O’Brienu , Jane Paulsenv , Shelley Peery Moranw , Denise Krchx , Penelope Hogarthy , Donald S. Higgins, Jr.z , Bernhard Landwehrmeyer aa . a Columbia University, New York, NY; b Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; c Indiana University, Indianapolis, IN; d Massachusetts General Hospital, Boston, MA; e Massachusetts Institute of Technology, Cambridge, MA; f Universidad del Zulia, Maracaibo, Venezuela; g Children’s Hospital, Los Angeles, CA; h Hospital Virgen del Camino, Iruna, Spain; i University of Texas, Houston, TX; j University of South Florida, Tampa, FL; k University of Rochester, Rochester NY; l Miami Children’s Hospital, Miami FL; m North Shore University Hospital, Manhasset, NY; n University of Southern California, Los Angeles, CA; o Mt. Sinai Medical Center, Miami Beach, FL; p Health & Human Services Agency, San Diego, CA; q New York University, New York, NY; r NINDS, NIH, Bethesda, MD; s University of California, Irvine, CA; t University of Alabama, Birmingham, AL; u Thomas Jefferson University, Philadelphia, PA; v University of Iowa, Iowa City, IA; w New York University Medical Center, New York, NY; x City University of New York, New York, NY; y Oregon Health Sciences University, Portland, OR; z Albany Medical College, Albany, NY; aa University of Ulm, Ulm, Germany. 7 The HD MAPS Collaborative Research Project: Michael R. Haydena , Elisabeth W. Almqvista , Ryan R. Brinkmana , Oksana Suchowerskyb , Alexandra Durr c , Catherine Dodéd , Ferdinando Squitierie , Patrick J. Morrisonf , Martha Nanceg , Christopher A. Rossh , Russell L. Margolish , Adam Rosenblatth , Estrella Gómez-Tortosai , David Mayo Cabreroi , Ronald J. A. Trentj , Elizabeth McCusker k , Andrea Novellettol , Marina Frontalim , Jane S. Paulsenn , Randi Joneso , Andrea Zankop , Tetsuo Ashizawaq , Alice Lazzarinir , Jian-Liang Lis,t,u , Vanessa C. Wheeler 26 , Ana L. Russs , Gang Xus , Jayalakshmi S. Mysorev , Tammy Gillisv , Michael Hakkyv , L. Adrienne Cupplest , Marie Saint-Hilaires , Jang-Ho J. Chav , Steven M. Herschv , John B. Penneyv,† , Madeline Harrisonw , Karen Marder x , Ruth K. Abramsony , P. Michael Conneallyz , James F. Gusellav,aa , Marcy E. MacDonaldv , Richard H. Myerss,t

∗ Corresponding author: Tel.: (617) 253-0261; fax: (617) 2535202. E-mail: [email protected] † Deceased. C 2006 The Authors C 2006 University College London Journal compilation

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University of British Columbia, Vancouver, Canada; b University of Calgary, Alberta, Canada; c Hôpital de la Salpêtrière, Paris, France; d Hôpital Cochin, Paris, France; e IRCCS Neuromed, Pozzilli, Italy; f Belfast City Hospital, Belfast, UK & University of Ulster, Coleraine, UK; g Hennepin County Medical Center, Minneapolis, MN; h John Hopkins University, Baltimore, MD; i Fundación Jiménez D´ıaz, Madrid, Spain; j University of Sydney, Sydney, Australia; k Westmead Hospital, Sydney, Australia; l University of Calabria, Rende, Italy; m Institute of Neurobiology and Molecular Medicine, CNR, Rome, Italy; n University of Iowa, Iowa City, IA; o Emory University, Atlanta, GA; p University of California, San Francisco; q Baylor College of Medicine, Houston, TX; r Robert Wood Johnson School of Medicine and Dentistry of New Jersey & Novartis Pharmaceuticals, New Brunswick, NJ; s Boston University School of Medicine, Boston, MA; t Boston University, Boston, MA; u Wyeth Research, Cambridge, MA; v Massachusetts General Hospital, Boston, MA; w University of Virginia, Charlottesville, VA; x Columbia University, New York, NY & Terrance Cardinal Cook Hospital, NJ; y WMS Hall Psychiatric Institute, Columbia, SC; z Indiana University School of Medicine, Indianapolis, IN; aa Harvard Medical School, Boston, MA. 8

Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA

Summary Age of onset for Huntington’s disease (HD) varies inversely with the length of the disease-causing CAG repeat expansion in the HD gene. A simple exponential regression model yielded adjusted R-squared values of 0.728 in a large set of Venezuelan kindreds and 0.642 in a North American, European, and Australian sample (the HD MAPS cohort). We present evidence that a two-segment exponential regression curve provides a significantly better fit than the simple exponential regression. A plot of natural log-transformed age of onset against CAG repeat length reveals this segmental relationship. This two-segment exponential regression on age of onset data increases the adjusted R-squared values by 0.012 in the Venezuelan kindreds and by 0.035 in the HD MAPS cohort. Although the amount of additional variance explained by the segmental regression approach is modest, the two slopes of the two-segment regression are significantly different from each other in both the Venezuelan kindreds [F(2, 439) = 11.13, P = 2 × 10− 5 ] and in the HD MAPS cohort [F(2, 688) = 38.27, P = 2 × 10− 16 ]. In both populations, the influence of each CAG repeat on age of onset appears to be stronger in the adult-onset range of CAG repeats than in the juvenile-onset range. Keywords: Huntington’s disease, multiple linear regression

Introduction Huntington’s disease (HD, MIM 143100) is a neurodegenerative disorder characterized by involuntary movements, emotional disturbance, and cognitive deficits. It is an autosomal dominant disorder with a prevalence of 1 in 10 000 in Europe, North America, and South America (Bates et al. 2002). HD is caused by the expansion of a CAG triplet repeat in the first exon of the HD gene on chromosome 4p16.3, which produces an expanded polyglutamine repeat in the huntingtin protein (Huntington’s Disease Collaborative Research Group, 1993). A CAG triplet repeat length of 40 or more is fully penetrant and will lead to disease in a normal human 296


lifespan (Bates et al. 2002). Alleles with between 36 and 39 repeats may show variable penetrance (Bates et al. 2002). Individuals with 60 or more CAG repeats invariably manifest the symptoms of HD at age 20 or younger. The larger the CAG repeat, the earlier the age of onset. Juvenile and adult onset HD differ profoundly in both clinical and neuropathological features. Neuropathologically, the brains of children dying of juvenile-onset HD have much more extreme atrophy of all parts of the brain than those dying of adult-onset HD (Bates et al. 2002). Chorea, the involuntary jerky, dance-like movements that encompass the entire body, is the primary motor abnormality found in adults. Children, on the other hand, suffer from a severe paucity of movement, characterized C 2006 The Authors C 2006 University College London Journal compilation

The Correlation Between HD Age of Onset and CAG Repeat Length Appears Segmental

by parkinsonism, bradykinesia, rigidity, tremor and a higher incidence of epilepsy. The hyperkinetic adult form stands in stark contrast to the hypokinetic juvenileonset form. A strong correlation exists between the length of the CAG triplet repeat and the age at which motor symptoms appear. A simple exponential regression curve explains up to 70% of the variability in age of motor onset in all populations studied (Gusella & MacDonald, 1995). We examined the relationship between HD CAG repeat length and age of motor onset in two populations: large kindreds from Venezuela (Wexler et al. 2004) and the HD MAPS cohort consisting of sibling pairs collected from clinics in North America, Europe and Australia (Li et al. 2003). Although age of onset varies inversely with CAG repeat length across the mutational spectrum, we find that the best fit for the exponential regression curve is not a single line across the entire range of CAG repeat lengths. The slope of the curve correlating CAG repeat length with age of onset is steeper in the adult-onset range of fewer repeats than in the juvenileonset range of more numerous CAG repeats. We propose that a two-segment exponential regression curve provides a better model to explain this correlation.

Methods The large kindreds from Venezuela (Wexler et al. 2004) and the HD MAPS cohort (Li et al. 2003) have been described in detail in prior publications, including the number of kindreds and the number of relationship pairs available for analysis (see Table 1). To fit the simple exponential model, we used the equation LAO = α + βX, where X is the expanded CAG repeat length and LAO is the natural log of age of onset. This is a commonly utilized regression model, though others are possible (Langbehn et al. 2004). This regression model is improved in the HD MAPS cohort (but not in the Venezuela cohort) if the interaction between normal and expanded CAG repeat is included (Djoussé et al. 2003). For the current analysis we used only the expanded CAG repeat length and age of motor onset in our regression models. To fit the two-segment model at each CAG repeat inflection point (I), we encoded two new variables (X 1 and X 2 ) for each individual: X 1 = X – I for X ≤ I and C 2006 The Authors C 2006 University College London Journal compilation

X 1 = 0 for X > I; and X 2 = 0 for X ≤ I, and X 2 = X – I for X > I. The two-segment exponential model is then LAO = α + β 1 X 1 + β 2 X 2 . These models are nested because equating the slopes and eliminating the inflection point parameter (which changes only the intercept) yields an equivalent equation to the single predictor model (LAO = α + βX). For each regression model we calculated adjusted Rsquared values, which controls for the possible different number of variables used in the regression model. This allows more meaningful comparisons to be made between two regression models with different numbers of independent variables. Because the models are nested, we tested for the significance of the segmental nature of the two-segment line by estimating the likelihood of the two slopes being identical under the null hypothesis. At the same time we estimated the inflection point that gave the maximum increase in the adjusted R-squared value. The significance test therefore had two degrees of freedom, and the F-values were adjusted accordingly. We re-analyzed the HD MAPS data set using a family weighting scheme, which reduces the impact of each sibling in a nuclear family so that each family has the same weight. If a family has four children, for example, each child is weighted by 0.25. Because there are so many different familial relationships in the Venezuelan kindreds, accounting for each correlation would add an unacceptable level of complexity.

Results The Venezuelan Kindreds A regression equation often used to model HD age of onset is LAO = α + βX, where X is the expanded CAG repeat length and LAO is the natural log of age of onset. We plotted the CAG repeat length for the longer HD allele against the LAO for each individual in the Venezuelan kindreds (Fig. 1). The sample included everyone ascertained during the course of this longitudinal study that was heterozygous for a fully penetrant HD mutation (i.e. those whose longer allele was over 39 repeats and whose shorter allele was under 36 CAG repeats) and who had clinical onset. These comprised a total of 443 individuals. Annals of Human Genetics (2007) 71,295–301

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Figure 1 Relationship between age of onset of motor symptoms and CAG repeat length in the Venezuelan cohort. The natural log-transformed age of onset was plotted against CAG repeat length in the HD gene for 443 individuals in the study. (Each × symbol represents one or more individuals in the cohort. Because CAG repeat length is discrete and age of onset was rounded to the nearest year, some of the symbols between 41 and 60 CAG repeats represent multiple individuals.) The dotted line represents the best-fit simple linear regression line. The solid line represents the best-fit multiple linear regression line with the inflection point at 53 repeats, which gave the largest increase in adjusted R-squared value. Age of motor onset data was acquired as previously described (Wexler et al. 2004).

We performed simple linear regressions of LAO on CAG repeat length for the single line model, and multiple linear regression for the two-segment line fit in order to compare adjusted R-squared values for the two regression lines (see Methods). The maximum adjusted R-squared improvement (to 0.741 from 0.728, a difference of 0.013) occured at 53 CAG repeats. The single-line slope was − 0.049 ± 0.001, while the twosegment slopes were − 0.061 ± 0.003 and − 0.040 ± 0.002 (mean ± standard error). The probability of this effect arising by chance is very small [F(2, 439) = 11.13, P = 2 × 10− 5 ]. The difference in fit between the single-slope and double-slope regression lines is striking (Fig. 1). A disproportionate number of the individuals with the largest CAG repeat numbers fall above the simple linear regression line. In this range a simple regression does not explain the age of onset data well. In contrast, multiple linear regression fits the data in this range much better. The double-slope line also highlights the inflection point in the plotted data. As an additional query into the segmental nature of the curve, we split the data into two groups separated 298


by the inflection point of 53 CAG repeats. When we did this, the slope and intercept statistics remained the same for the two groups, although the standard error was larger for individuals with greater than 53 repeats (as there are only 35 of them). Individuals with 53 or fewer CAG repeats had a single-line slope of − 0.061 ± 0.003 while individuals with more than 53 CAG repeats had a single-line slope of − 0.039 ± 0.005. Even the intercept statistic (of the shifted curve, using X – 53 as the regression variable as described in the Methods) did not change. It was 3.10 ± 0.02 for the segmental curve, 3.10 ± 0.02 for the individuals with 53 or fewer CAG repeats, and 3.10 ± 0.07 for individuals with more than 53 CAG repeats. We feel that the fact that the slopes remain unchanged provides further evidence for the segmental nature of the curve. Finally, we repeated this last type of analysis but broke the data according to age of onset instead of repeat length. We used the established cutoff of 20 years of age to define juvenile onset. For individuals with onset at 20 years of age or older, the regression slope was − 0.054 ± 0.003 and the intercept was 3.16 ± 0.02. For individuals with onset before 20 years of age, the regression slope was − 0.031 ± 0.004 and the intercept was 2.91 ± 0.07. This large difference in the slopes further supports our approach.

The HD MAPS Cohort We also performed multiple regression analyses on age of onset data from individuals in the HD MAPS study. This sample includes all individuals who were heterozygous for an HD mutation and who had clinical onset: a total of 692 individuals. Again, the segmental nature of the relationship was clearly evident and highlighted by the double-slope curve (Fig. 2). The maximum adjusted Rsquared improvement (to 0.677 from 0.642, a difference of 0.035) occured at 49 repeats. The single-line slope was − 0.046 ± 0.001, while the two-segment slopes were − 0.067 ± 0.003 and − 0.032 ± 0.002 (mean ± standard error). The probability of this effect arising by chance is also very small [F(2, 688) = 38.27, P = 2 × 10− 16 ]. We found equivalent results when we repeated the analyses on the HD MAPS sample using a family weighting scheme. We weighted observations from each C 2006 The Authors C 2006 University College London Journal compilation


Figure 2 Relationship between age of motor onset and CAG repeat length in the HD MAPS cohort. The natural log-transformed age of onset was plotted against CAG repeat length in the HD gene for 692 individuals in the study. (Each × symbol represents one or more individuals in the cohort. Because CAG repeat length is discrete and age of onset was rounded to the nearest year, some of the symbols between 40 and 65 CAG repeats represent multiple individuals.) The dotted line represents the best-fit simple linear regression line. The solid line represents the best-fit multiple linear regression line with the inflection point at 49 repeats, which gave the largest increase in adjusted R-squared value. Age of onset data was acquired as described previously (Li et al. 2003).

family so that each family had the same contribution to the analysis. This controls for possible bias caused by the fact that observations within a family are correlated, or possible bias caused by variability of family size in the sample. The maximum adjusted R-squared improvement was to 0.696 from 0.662 at 49 repeats. The single-line slope was − 0.048 ± 0.001 and the two-line slopes were − 0.068 ± 0.003 and − 0.033 ± 0.002 with F(2, 688) = 38.77 and P < 1 × 10− 16 for comparison of the slopes. We did not perform the family weighting scheme on the Venezuelan sample because it is primarily comprised of one large interconnected kindred.

Changes in Adjusted R-squared Because the maximum increase in adjusted R-squared value occurred at different repeat lengths in the two populations (at 53 CAG repeats for the Venezuelan kindreds and at 49 repeats for the HD MAPS sample), we plotted the improvement in adjusted R-squared when different inflection points are used to separate the phases of the two-line curve (Fig. 3). Neither sample’s maxi C 2006 The Authors C 2006 University College London Journal compilation

Figure 3 Degree of improvement of adjusted R-squared values calculated in a two-segment regression assuming different HD CAG repeat inflection points. The difference in the adjusted R-squared values between the simple linear regression and the two-segment regression analyses is plotted against the CAG repeat length chosen as the inflection point for the two-segment analysis using data from the HD MAPS cohort (squares) or the Venezuelan cohort (triangles).

mum value appeared as a sharp peak. Each peak was broad, stretching from CAG repeat lengths in the upper 40s to the upper 50s. This difference observed in the maximal inflection point in the two samples may not be meaningful. Some of the variability between the shapes of the peaks in Fig. 3 may be due to inherent differences in the study populations. The U.S.-Venezuela Collaborative Research Project is a prospective, longitudinal study in Venezuela striving for near complete ascertainment (Wexler et al. 2004). Individuals in the HD MAPS were ascertained during a visit to one of a number of HD clinics over many geographical regions (Li et al. 2003). These cohorts have different genetic backgrounds and may segregate independent modifiers of age of onset. The two populations also have quite different environmental conditions: the majority of those in Venezuela live in extreme poverty in small fishing villages around Lake Maracaibo, while the HD MAPS cohort is drawn from more affluent regions of the world. We believe that the broad peaks in Fig. 3 represent overlapping “confidence intervals” for the precise transition point and that the segmental nature of the correlation between LAO and CAG repeat length in both populations shares the same underlying cause.

Discussion We have used age of motor onset data from two independent HD populations to demonstrate that the Annals of Human Genetics (2007) 71,295–301

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Table 1 Comparison of the Venezuela and HD MAPS cohorts

Number of individuals analyzed Age of onset (mean ± standard deviation) Adjusted R-squared for simple regression (LAO vs. CAG repeat) Simple regression line slope (LAO per CAG repeat, mean ± standard error) Adjusted R-squared for two-segment regression (LAO vs. CAG repeat) Two-segment regression inflection point Slope for CAG repeat lengths smaller than the inflection point (LAO per CAG repeat, mean ± standard error) Slope for CAG repeat lengths larger than the inflection point (LAO per CAG repeat, mean ± standard error) P-value of significance test for equality of the two-segment regression slopes

correlation between age of onset and expanded CAG repeat length is modelled significantly better by two lines than by one line. This occurs when regression of natural log-transformed data is performed. An inflection point in the data is easily observed in the natural logtransformed plots for both the Venezuelan HD kindreds and the HD MAPS study (Figs. 1 and 2). By adjusted R-squared values this transition is broad, occurring with CAG repeat lengths from the upper 40s to the upper 50s (Fig. 3). These results show that the relationship between CAG repeat length and age of onset varies for different repeat lengths. When CAG repeat lengths are 60 repeats or greater the addition of a single CAG repeat has a lesser influence on age of onset. In contrast, the addition of each new CAG repeat has a more significant impact on age of onset when the initiating CAG repeat sizes are in the 40s and 50s. Our analyses of two independent samples from large, ethnically and geographically dissimilar populations suggest that this difference is unlikely to have arisen by chance. The similarity between the results in these two highly variant populations suggests that there may be fundamental biological explanations underlying the results observed in both populations. A number of hypotheses might explain this transition in the age of onset data. For example, the location of the mutant protein in the cell may cause a difference in the impact of each additional glutamine residue. In the juvenile form of HD aggregates of the mutant protein are more typically found in the nucleus of neurons, while aggregates in the perinuclear cytoplasm and dystrophic neurites are more frequent features of the adult form (DiFiglia et al. 1997). Different protein clearance mechanisms may be required, given the variability in 300


Venezuela

HD MAPS

443 34.3 ± 9.9 years 0.728 − 0.049 ± 0.001 0.741 53 CAG repeats − 0.061 ± 0.003

692 39.3 ± 12.0 years 0.642 − 0.046 ± 0.001 0.677 49 CAG repeats − 0.067 ± 0.003

− 0.040 ± 0.002

− 0.032 ± 0.002

P = 2 × 10− 5

P = 2 × 10− 16

the cellular localization of aggregates in the juvenile and adult onset cases. Another possible explanation for this disparity is a difference in the pathological mechanisms of the polyglutamine repeats in each range. Although both juvenile-onset and adult-onset HD are caused by an expanded CAG repeat, it may be that they have different pathogenic mechanisms. These mechanisms differ sufficiently to cause quite distinct clinical phenotypes, as well as to produce two slopes in the age of onset curve. For example, the addition of each CAG repeat in the smaller CAG ranges might cause dysfunction within the protein context. The addition of a new CAG repeat in the larger ranges, however, may increase the toxicity of the polyglutamine stretch itself, independent of protein context. If this is the case, we might expect to see similar differences in adult-onset symptoms and juvenile-onset symptoms in other polyglutamine disorders. To date, eight other diseases are known to be caused by CAG expansion mutations: dentatorubral-pallidoluysian atrophy (DRPLA, MIM 125370), spinal and bulbar muscular atrophy (SBMA, MIM 313200), and spinocerebellar ataxia types 1, 2, 3, 6, 7, and 17 (SCA1, MIM 164400; SCA2, MIM 183090; SCA3, MIM 109150; SCA6, MIM 183086; SCA7, MIM 164500; and SCA17, MIM 607136). These disorders share a number of important features with HD. All are late-onset neurodegenerative disorders showing dominant inheritance with similar repeat thresholds for disease. All mutant proteins are widely expressed, contain an expanded polyglutamine repeat, and show a propensity to form insoluble protein aggregates. All demonstrate an inverse correlation between age of onset and CAG repeat length. C 2006 The Authors C 2006 University College London Journal compilation


Despite these basic similarities, the adult forms of these disorders vary widely in their symptomology and patterns of neurodegeneration. HD is characterized by chorea, cognitive and psychiatric symptoms. SBMA presents with fasciculations and gynecomastia, and DRPLA with ataxia, chorea, and dementia. The spinocerebellar ataxias all primarily demonstrate progressive cerebellar ataxia and opthalmoplegia. SCA6 has distinct oculomotor and vestibular abnormalities. SCA7 also is characterized by retinal degeneration, while SCA17 additionally manifests with psychiatric symptoms and dementia. Even among the most clinically similar of the CAG triplet repeat diseases (SCA1, SCA2, and SCA3), distinct differences in the eye movement phenotype allow 90% of cases to be correctly assigned based on clinical symptoms alone (Rivaud-Pechoux et al. 1998). The juvenile forms of the eight CAG repeat diseases, on the other hand, appear to be much more similar. In each, the juvenile form tends to be more severe clinically, with more widespread neurodegeneration. For SCA2 (Babovic-Vuksanovic et al. 1998), SCA17 (Maltecca et al. 2003), and HD (Bates et al. 2002) children with juvenile onset frequently have seizures, a symptom rarely seen in adult-onset forms of the diseases. In individuals with the adult-onset form of HD seizures never occur unless they are part of some other disorder. In DRPLA, epilepsy is occasionally seen in adult-onset cases, but is frequent in the juvenile form of the disorder (Koide et al. 1997). As the CAG repeat lengths expand and transition from adult-onset disease to juvenile-onset disease, the family of polyglutamine disorders also appears to change from highly variable symptomology to more similar clinical manifestations. We have demonstrated that each CAG repeat has a greater impact on HD age of onset for smaller CAG repeat lengths than for the larger CAG repeat lengths. Though many possible explanatory mechanisms may be involved, we find it highly suggestive that the transition between the two slopes overlaps with the transition from primarily adult-onset to primarily juvenile-onset HD. Analyzing this same age of onset relationship for other CAG expansion disorders could give us clues about the possible universality of these characteristics. In turn, we might learn something about the underlying pathogenesis of all polyglutamine expansion disorders.

C 2006 The Authors C 2006 University College London Journal compilation

Acknowledgements We wish to thank HD families throughout the world, the Hereditary Disease Foundation, the Wellcome Trust, NINDS/NIH NS16367 (JFG) and NS32765 (MEM), the Huntington’s Disease Society of America Coalition for the Cure, The Jerry McDonald Huntington’s Disease Research Fund, and the W. M. Keck Foundation for funding the Venezuela study.

References Babovic-Vuksanovic, D., Snow, K. et al. (1998) Spinocerebellar ataxia type 2 (SCA 2) in an infant with extreme CAG repeat expansion. Am J Med Genet 79, 383–387. Bates, G., Harper, P. & Jones, L. (2002) Huntington’s Disease (3rd Edition) (Oxford, UK, Oxford University Press). Djoussé, L., Knowlton, B. et al. (2003) Interaction of normal and expanded CAG repeat sizes influences age at onset of Huntington disease. Am J Med Genet 119A, 279–282. Gusella, J. F. & MacDonald, M. E. (1995) Huntington’s disease. Semin Cell Biol 6, 21–28. Huntington’s Disease Collaborative Research Group (1993) A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72, 971–983. Koide, R., Onodera, O. et al. (1997) Atrophy of the cerebellum and brainstem in dentatorubral pallidoluysian atrophy. Influence of CAG repeat size on MRI findings. Neurology 49, 1605–1612. Langbehn, D. R., Brinkman, R. R. et al. (2004) A new model for prediction of the age of onset and penetrance for Huntington’s disease based on CAG length. Clin Genet 65, 267– 277. Li, J. L., Hayden, M. R. 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. Maltecca, F., Filla, A. et al. (2003) Intergenerational instability and marked anticipation in SCA-17. Neurology 61, 1441– 1443. Rivaud-Pechoux, S., Durr, A. et al. (1998) Eye movement abnormalities correlate with genotype in autosomal dominant cerebellar ataxia type I. Ann Neurol 43, 297–302. Wexler, N. S., Lorimer, 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.

Received: 2 May 2006 Accepted: 8 November 2006


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