Biomedical subjects
Publications and source records attributed to N C Shute.
Genetic Analysis Workshop data come from various sources, with possibly different ascertainment procedures in each. It is plausible that classical ascertainment models do not accurately describe the ascertainment process in each data source. For this reason, we review the biases which can arise from an incorrect specification on the ascertainment process and discuss their relevance for the GAW5 data analysis.
Haplotype sharing patterns of sibs identical-by-descent are examined following the method of Ethier and Hodge [1985] but taking account of which parent contributes the shared genes. A means of specification of all distinct sibship configurations is considered, along with the corresponding probability of each configuration and the total number of configurations for differing sibship sizes.
Comparisons of bias and standard errors of genetic parameter estimates arising from various estimation procedures are made by using contrived genetic data in which parental haplotypes are known in addition to the haplotypes of affected sibs. These comparisons show that knowledge of parental haplotypes can reduce standard errors of genetic parameter estimates by between 10% and 45% as compared to estimates when parental information is not available. The added information does not appear to change the standard errors of ascertainment-assumption-free estimates relative to those from classical methods. This implies that the decision to accept possible bias arising from a classical estimation method or to accept slightly increased standard errors under an ascertainment-assumption-free approach is comparable to that involved when parental haplotypes are unknown.
The ascertainment problem arises when families are sampled by a nonrandom process and some assumption about this sampling process must be made in order to estimate genetic parameters. Under classical ascertainment assumptions, estimation of genetic parameters cannot be separated from estimation of the parameters of the ascertainment process, so that any misspecification of the ascertainment process causes biases in estimation of the genetic parameters. Ewens and Shute proposed a resolution to this problem, involving conditioning the likelihood of the sample on the part of the data which is "relevant to ascertainment." The usefulness of this approach can only be assessed by examining the properties (in particular, bias and standard error) of the estimates which arise by using it for a wide range of parameter values and family size distributions and then comparing these biases and standard errors with those arising under classical ascertainment procedures. These comparisons are carried out in the present paper, and we also compare the proposed method with procedures which condition on, or ignore, parts of the data.
When nuclear families are sampled by an ascertainment procedure whose properties are not known, biased estimates of genetic parameters will arise if an incorrect specification of the ascertainment procedure is made. Elsewhere we have put forward a resolution of this problem by introducing an ascertainment-assumption-free (AAF) method, for nuclear family data, which gives asymptotically unbiased estimators no matter what the true nature of the ascertainment process. In the present paper we extend this method to cover pedigree data. Problems that arise with pedigrees but not with families--for example, the question of which families in a pedigree are "ascertainable"--are also considered. Comparisons of numerical results for pedigrees and nuclear families are also made.
A likelihood ratio test concerning a genetic parameter describing in part the nature of a genetic disease is investigated. The test has the undesirable characteristic that its power curve is not a monotonically increasing function of the true value of the parameter being tested.
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We consider the "ascertainment problem" arising when families are sampled by a nonrandom sampling process and, for the purpose of estimating genetic parameters, some assumption must be made about the process by which families enter the sample. A resolution of this problem, involving conditioning the likelihood of the sample on that part of the data relevant to ascertainment, is put forward. Numerical examples illustrating the properties of the procedure are provided.
The concept of a proband, and a formula frequently used to give the probability that a family with r affected children has t probands, have led to frequently used, but incorrect, 'limits of ascertainment' in estimation problems involving ascertainment sampling. Data suggesting a sampling scheme outside these incorrect limits are discussed. An estimation procedure not using the proband concept is available which is not bounded by the incorrect limits.