Publications

Abstract

Celiac disease (CD) is an intolerance to dietary proteins of wheat, barley, and rye. CD may have substantial morbidity, yet it is quite common with a prevalence of 1%-2% in Western populations. It is not clear why the CD phenotype is so prevalent despite its negative effects on human health, especially because appropriate treatment in the form of a gluten-free diet has only been available since the 1950s, when dietary gluten was discovered to be the triggering factor. The high prevalence of CD might suggest that genes underlying this disease may have been favored by the process of natural selection. We assessed signatures of selection for ten confirmed CD-associated loci in several genome-wide data sets, comprising 8154 controls from four European populations and 195 individuals from a North African population, by studying haplotype lengths via the integrated haplotype score (iHS) method. Consistent signs of positive selection for CD-associated derived alleles were observed in three loci: IL12A, IL18RAP, and SH2B3. For the SH2B3 risk allele, we also show a difference in allele frequency distribution (F(st)) between HapMap phase II populations. Functional investigation of the effect of the SH2B3 genotype in response to lipopolysaccharide and muramyl dipeptide revealed that carriers of the SH2B3 rs3184504*A risk allele showed stronger activation of the NOD2 recognition pathway. This suggests that SH2B3 plays a role in protection against bacteria infection, and it provides a possible explanation for the selective sweep on SH2B3, which occurred sometime between 1200 and 1700 years ago.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

Abstract

Sign language lexicography has thus far been a relatively obscure area in the world of lexicography. Therefore, this article will contain background information on signed languages and the communities in which they are used, on the lexicography of sign languages, the situation in the Netherlands as well as a review of a sign language dictionary that has recently been published in the Netherlands.