Supplementary data are available at Bioinformatics online.

Down syndrome (DS), or trisomy 21, is a common disorder associated with several complex clinical phenotypes. Although several hypotheses have been put forward, it is unclear as to whether particular gene loci on chromosome 21 (HSA21) are sufficient to cause DS and its associated features. Here we present a high-resolution genetic map of DS phenotypes based on an analysis of 30 subjects carrying rare segmental trisomies of various regions of HSA21. By using state-ofthe-art genomics technologies we mapped segmental trisomies at exon-level resolution and identified discrete regions of 1.8 -16.3 Mb likely to be involved in the development of 8 DS phenotypes, 4 of which are congenital malformations, including acute megakaryocytic leukemia, transient myeloproliferative disorder, Hirschsprung disease, duodenal stenosis, imperforate anus, severe mental retardation, DS-Alzheimer Disease, and DS-specific congenital heart disease (DSCHD). Our DS-phenotypic maps located DSCHD to a <2-Mb interval. Furthermore, the map enabled us to present evidence against the necessary involvement of other loci as well as specific hypotheses that have been put forward in relation to the etiology of DS-i.e., the presence of a single DS consensus region and the sufficiency of DSCR1 and DYRK1A, or APP, in causing several severe DS phenotypes. Our study demonstrates the value of combining advanced genomics with cohorts of rare patients for studying DS, a prototype for the role of copy-number variation in complex disease. copy number variants ͉ genomic structural variation ͉ human genome ͉ congenital heart disease ͉ leukemia F or over two decades trisomy 21 has represented a prototype disorder for the study of human aneuploidy and copy-number variation (1, 2), but the genes responsible for most Down syndrome (DS) phenotypes are still unknown. The analysis of several overlapping segmental trisomies 21 has led to the suggestion that dosage alteration through duplication of an extended region on chromosome 21 (HSA21) is associated with DS features (2-5, 42). However, humans with segmental trisomy 21 are rare, and thus humanbased DS-phenotypic maps have been of low resolution, far beyond the level of few or single genes, or even exons. Consequently, gene-disease links have often been based on indirect evidence from cellular or animal models (6, 7). Moreover, current hypotheses argue for the existence of a critical region, the DS consensus region (DSCR), responsible for most severe DS features (6, 8, 9), or presume the causative role of a small set of genes including DSCR1 and DYRK1A, or APP, for these phenotypes (6, 7).By using state-of-the-art genomics together with a large panel of partially trisomic individuals, we present the highest resolution DS phenotype map to date and identify distinct genomic regions that likely contribute to the manifestation of 8 DS features. Four of these phenotypes have never been associated with a particular region of HSA21. The map also enables us to rule out the necessary contribution of other HSA21 regions, thus pr...

Detection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.