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A Correction for Ascertainment Bias in Estimating Rates of Onset of Highly Penetrant Genetic Disorders

Published online by Cambridge University Press:  17 April 2015

Carolina Espinosa  and
Angus Macdonald
Angus Macdonald
Affiliation:
Department of Actuarial Mathematics and Statistics, and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom, Tel: +44(0)131-451-3209, Fax: +44(0)131-451-3249, E-mail: [email protected]
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Abstract

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Estimation of rates of onset of rare, late-onset dominantly inherited genetic disorders is complicated by: (a) probable ascertainment bias resulting from the ‘recruitment’ of strongly affected families into studies; and (b) inability to identify the true ‘at risk’ population of mutation carriers. To deal with the latter, Gui & Macdonald (2002a) proposed a non-parametric (Nelson-Aalen) estimate (x) of a simple function Λ(x) of the rate of onset at age x. The function Λ(x) had a finite bound, which was an increasing function of the probability p that a child of an affected parent inherits the mutation and σ the life-time penetrance. However if (x) exceeds this bound, it explodes to infinity, and this can happen at quite low ages. We show that such ‘failure’ may in fact be a useful measure of ascertainment bias. Gui & Macdonald assumed that p = 1/2 and σ = 1, but ascertainment bias means that p > 1/2 and σ ≠ 1 in the sample. The maximum attained by (x) allows us to estimate a range for the product pσ, and therefore the degree of ascertainment bias that may be present, leading to bias-corrected estimates of rates of onset. However, we find that even classical independent censoring, prior to ascertainment, can introduce new bias. We apply these results to early-onset Alzheimer’s disease associated with mutations in the Presenilin-1 gene.

Keywords

Ascertainment BiasEarly-Onset Alzheimer’s DiseaseNelson-Aalen EstimatePresenilin-1 GeneRate of Onset
Type
Articles
Information
ASTIN Bulletin: The Journal of the IAA , Volume 37 , Issue 2 , November 2007 , pp. 429 - 452
Copyright
Copyright © ASTIN Bulletin 2007

References

Andersen, P.K., Borgan, Ø., Gill, R.D. and Keiding, N. (1993) Statistical models based on counting processes, Springer-Verlag, New York.CrossRefGoogle Scholar
Daykin, C.D., Akers, D.A., Macdonald, A.S., McGleenan, T., Paul, D. and Turvey, P.J. (2003) Genetics and insurance – some social policy issues (with discussions), British Actuarial Journal, 9, 787–874.CrossRefGoogle Scholar
Elandt-Johnson, R.C. (1973) Age-at-onset distribution in chronic diseases. A life table approach to analysis of family data, Journal of Chronic Disability, 26, 529–545.CrossRefGoogle ScholarPubMed
Elston, R.C. (1973) Ascertainment and age at onset in pedigree analysis, Human Heridity, 23, 105–112.CrossRefGoogle ScholarPubMed
Ewens, W.J. and Shute, N.C.E. (1986) A resolution of the ascertainment sampling problem I: Theory, Theoretical Population Biology, 30, 388–412.CrossRefGoogle ScholarPubMed
Ewens, W.J. and Shute, N.C.E. (1988a) A resolution of the ascertainment sampling problem II: Generalizations and numerical results, American Journal of Human Genetics, 43, 374–386.Google Scholar
Ewens, W.J. and Shute, N.C.E. (1988b) A resolution of the ascertainment sampling problem III: Pedigrees, American Journal of Human Genetics, 43, 387–395.Google Scholar
Ford, D., Easton, D.F., Stratton, M., Narod, S., Goldgar, D., Devilee, P., Bishop, D.T., Weber, B., Lenoir, G., Chang-Claude, J., Sobol, H., Teare, M.D., Struewing, J., Arason, A., Scherneck, S., Peto, J., Rebbeck, T.R., Tonin, P., Neuhausen, S., Barkardottir, R., Eyfjord, J., Lynch, H., Ponder, B.A.J., Gayther, S.A., Birch, J.M., Lindblom, A., Stoppa-Lyonnet, D., Bignon, Y., Borg, A., Hamann, U., Haites, N., Scott, R.J., Maugard, C.M., Vasen, H., Seitz, S., Cannon-Albright, L.A., Schofield, A., Zeladahedman, M. and The Breast Cancer Linkage Consortium (1998) Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families, American Journal of Human Genetics, 62, 676–689.CrossRefGoogle ScholarPubMed
Gui, E.H. and Macdonald, A.S. (2002a) A Nelson-Aalen estimate of the incidence rates of early-onset Alzheimer’s disease associated with the Presenilin-1 gene, ASTIN Bulletin, 32, 1–42.CrossRefGoogle Scholar
Gui, E.H. and Macdonald, A.S. (2002b) Early-onset Alzheimer’s disease, critical illness insurance and life insurance, Research Report No. 02/2, Genetics and Insurance Research Centre, Heriot-Watt University, Edinburgh.Google Scholar
Harper, P.S. and Newcombe, R.G. (1992) Age at onset and life table risks in genetic counselling for Huntington’s disease, Journal of Medical Genetics, 29, 239–242.CrossRefGoogle ScholarPubMed
Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2003) The genetics of breast and ovarian cancer I: A model of family history, Scandinavian Actuarial Journal, 2003, 1–27.CrossRefGoogle Scholar
Meiser, B. and Dunn, S. (2000) Psychological impact of genetic testing for Huntington’s disease: an update of the literature, J. Neurol. Neurosurg. Psychiatry, 69, 574–578.CrossRefGoogle ScholarPubMed
Newcombe, R.G. (1981) A life table for onset of Huntington’s Chorea, Annals of Human Genetics, 45, 375–385.CrossRefGoogle ScholarPubMed
Palamidas, A. (2001) Ascertainment bias in genetic epidemiology. M.Sc. dissertation, Heriot-Watt University, Edinburgh.Google Scholar
Sham, P. (1998) Statistics in Human Genetics. Arnold, London.Google Scholar
Thompson, E. (1993) Sampling and ascertainment in genetic epidemiology: A tutorial review, Technical Report 243, Department of Statistics, University of Washington.Google Scholar