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ABSTRACT: When conducting genetic studies for complex traits, large samples are commonly required to detect new genetic factors. A possible strategy to decrease the sample size is to reduce heterogeneity using available information. In this paper we propose a new class of model-free linkage analysis statistics which takes into account the information given by the ungenotyped affected relatives (positive family history). This information is included into the scoring function of classical allele-sharing statistics. We studied pedigrees of affected sibling pairs with one ungenotyped affected relative. We show that, for rare allele common complex diseases, the proposed method increases the expected power to detect linkage. Allele-sharing methods were applied to the symptomatic osteoarthritis GARP study where taking into account the family-history increased considerably the evidence of linkage in the region of the DIO2 susceptibility locus.
Annals of Human Genetics 11/2010; 74(6):547-54. · 2.57 Impact Factor
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ABSTRACT: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset.
Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation).
In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.
Human Heredity 12/2009; 69(2):80-90. · 1.79 Impact Factor
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ABSTRACT: A new score statistic is derived, which uses information from registries (age-specific incidences) and family studies (sib-sib marginal correlation) to weight affected sibling pairs according to their age at onset. Age at onset of sibling pairs is modelled by a gamma frailty model. From this model we derive a bivariate survival function, which depends on the marginal survival and on the marginal correlation. The score statistic for linkage is a classical nonparametric linkage (NPL) statistic where the identical by descent sharing is weighted by a particular function of the age at onset data. Since the statistic is based on survival models, it can also be applied to discordant and healthy sibling pairs. Simulation studies show that the proposed method is robust and more powerful than standard NPL methods. As illustration we apply the new score statistic to data from a breast cancer study.
Statistics in Medicine 04/2009; 28(14):1913-26. · 1.88 Impact Factor
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ABSTRACT: Typically long-lived sibling pairs have been collected for linkage analysis of human longevity and information on life span of first-degree relatives is available to assess familial aggregation of life span. We propose a new weighted statistic for aggregation analysis, which tests for a relationship between a family history of excessive survival of the sibships of the long-lived pairs and the survival of their parents and their offspring. For linkage analysis, we derive a new weighted score statistic from a simple gamma frailty model, which assigns more weight to excessive long-lived pairs. We apply the methods to data from the Leiden Longevity Study, which consists of sibling pairs of age 90 years or above and their first-degree relatives. The pairs have been genotyped for microsatellite markers in a candidate region. Association was present between survival within the sibships and survival of the offspring, but not with the parental generation. For linkage analysis, weighting increased the value of the test statistic, but the result was not statistically significant. About the methods we conclude that the statistic for aggregation provides insight into clustering of life span and the statistic for linkage provides a new tool to include demographic information into the analysis.
Statistics in Medicine 10/2008; 28(1):140-51. · 1.88 Impact Factor
