Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations.Method: The present article evaluates the performance of lasso penalized logistic regression in case–control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression.Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs.Availability: The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site.Contact: klange@ucla.eduSupplementary information: 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-of-the-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.

Objectives: To describe, implement, and test an efficient algorithm to obtain multipoint identity-by-descent (IBD) probabilities at arbitrary positions among marker loci for general pedigrees. Unlike existing programs, our algorithm can analyze data sets with large numbers of people and markers. The algorithm has been implemented in the SimWalk2 computer package. Methods: Using a rigorous testing regimen containing five pedigrees of various sizes with realistic marker data, we compared several widely used IBD computation programs: Allegro, Aspex, GeneHunter, MapMaker/Sibs, Mendel, Sage, SimWalk2, and Solar. Results: The testing revealed a few discrepancies, particularly on consanguineous pedigrees, but overall excellent results in the deterministic multipoint packages. SimWalk2 was also found to be in good agreement with the deterministic multipoint programs, usually matching to two decimal places the kinship coefficient that ranges from 0 to 1. However, the packages based on single-point IBD estimation, while consistent with each other, often showed poor results, disagreeing with the multipoint kinship results by as much as 0.5. Conclusions: Our testing has clearly shown that multipoint IBD estimation is much better than single-point estimation. In addition, our testing has validated our algorithm for estimating IBD probabilities at arbitrary positions on general pedigrees.