In the paper by Goldstein et al. (Genomics, 1995), the authors carried out a simulation study to investigate the relative efficiencies of a no interference linkage analysis to an analysis with certain models that allow for interference. They showed that, for completely informative and independent recombination data, the analysis with the no interference model was inefficient, in the present of interference. In practice, the assumption of completely informative markers is unrealistic with data from human pedigrees. We report the results of a study investigating whether this conclusion still holds for gametes arising within pedigrees. We consider the same two mapping problems as Goldstein et al.: exclusion mapping and gene ordering. The results obtained were consistent with their findings, although the efficiency gains for analyses using the chi-square model were not as great in some cases. This is not unexpected with less than fully informative data. These results point to the need for research of developing new statistical and computational methods to incorporate interference into multipoint linkage mapping using pedigree data. This would make efficient use of available, but sometimes scarce data, especially in disease gene mapping.

An efficient test of deviation from Hardy–Weinberg frequencies with one degree of freedom was applied to 44 marker loci in a genome scan, and 7 loci had a significant excess of apparent homozygotes (χ2(1) > 6) suggestive of typing error. In this example evidence for linkage did not increase when outliers were censored. Statistical quality control is an essential part of genotyping, and the effect of mistyping and map error should be considered in evaluating any genome scan.

Xiong et al. (1998) proposed a general transmission disequilibrium test (TDT) for quantitative traits. Their TDT for quantitative traits permits more than one child and more than one heterozygous parent per family. This TDT is also appropriate for the situation where the trait and marker loci are at varying degrees of linkage and linkage disequilibrium. Their generalized TDT statistic for a multi-allele marker locus may be more useful in practice. However, there are several mistakes in their mathematical presentation for this generalized TDT statistic for a multi-allele marker locus, and it confuses readers. Here, we present a corrected TDT statistic for a multi-allele marker locus with a clearer presentation. We hope that this new presentation will remove some confusion for readers.

In its usual form, the multifactorial model of disease transmission assumes that the liabilities to disease have a multivariate normal distribution. This paper studies how sensitive to this assumption are the quantitative results from the model. Accordingly, bounds are established for the probability of a child having a disease, given that both parents have it and taking the heritability of the disease to be known. Unfortunately, these bounds turn out to be wide. For example, a probability that is 0.38 under the trivariate normal model may be as low as 0.12 or as high as 0.78 under other trivariate models, even if attention is restricted to those of variables-in-common form. The broader statistical issue of the meaning of trivariate dependence, as distinct from bivariate dependence, is also discussed.

Previously we showed (Wang, Guerra & Cohen 1998) that a statistically robust version of the Haseman & Elston (1972) sib-pair method greatly increased power to detect linkage in the presence of outliers. In this paper we report on M-estimation to accommodate outliers in the variance-components approach to linkage analysis developed by Amos (1994). Simulations show that in the presence of outliers the robust variance-components approach provides substantially greater power, more precise estimation of heritabilities, and better false–positive rates than the original Gaussian based approach. In the absence of outliers the performance of the robust variance-components approach is similar to that of the Gaussian based approach. For illustration we apply the method to two well characterized lipoprotein systems.

The chromosomal localization of the human CD83 gene was determined using somatic cell hybrids, a radiation hybrid mapping panel and FISH analysis on human metaphase chromosomes. PCR-based analysis of a single chromosome hybrid panel identified the presence of the CD83 gene on human chromosome 6 and subsequent analysis of the Genebridge4 radiation panel located the gene between AFMa192wg9 and AFMb322wd1 with a lod score of 9.2. Finally, using FISH analysis the CD83 gene was localized to chromosome 6 band p23.

Expressions are derived for the sample size required to achieve a given power in variance component linkage analysis of a quantitative trait in unascertained samples. For simplicity an additive model, comprising effects due to a single QTL, residual additive genetic factors, and individual-specific random environmental variation, is considered. Equations are given relating sample size to trait heritability for sibpairs, sib trios, nuclear families having two and three sibs, and arbitrary relative pairs. The effects of nonzero residual additive genetic variance and parental information are discussed, and a scale relationship for sample sizes with sibships and nuclear families is derived. For larger sampling structures such as extended pedigrees the inheritance space is randomly sampled and the relevant equations are solved numerically. Comparative power curves are presented for sibships of size 2–4 and for an extended pedigree of 48 individuals. Simulation results for sibpairs confirm the validity of the theoretical results.

We have developed a method for meta-analysis of genome scans which allows systematic integration of data from published results. The Genome Search Meta-analysis method (GSMA) uses a non-parametric ranking method to identify genetic regions that show consistently increased sharing statistics or lod scores. The GSMA ranks genetic regions according to the lod score or p-value achieved in each scan. The summed rank across studies is compared to its probability distribution assuming ranks are randomly assigned. The GSMA can confirm evidence for regions highlighted in the original genome scans, and identify novel regions, which did not reach significance in any scan. In this paper, the GSMA was applied to four genome screens in multiple sclerosis and across 11 screens from autoimmune disorders. The GSMA is appropriate for studies with different family ascertainment, markers, and statistical analysis methods. The method increases the power to detect individual linkages in a clinically homogeneous dataset and has the potential to detect susceptibility loci in clinically distinct diseases which show involvement of common pathogenetic pathways.