Large Sample Size,Wide Variant Spectrum,and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel Disease

We performed risk assessment for Crohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium’s Immunochip project. This data set contains ∼17,000 CD cases, ∼13,000 UC cases, and ∼22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.     

Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data

Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.     

Genomic Resources Notes accepted 1 April 2013–31 May 2013

This article documents the public availability of approximately 15 000 polymorphic SNP loci for the bighorn sheep Ovis canadensis.     

Genetic diversity and investigation of polledness in divergent goat populations using 52 088 SNPs

The recent availability of a genome‐wide SNP array for the goat genome dramatically increases the power to investigate aspects of genetic diversity and to conduct genome‐wide association studies in this important domestic species. We collected and analysed genotypes from 52 088 SNPs in Boer, Cashmere and Rangeland goats that had both polled and horned individuals. Principal components analysis revealed a clear genetic division between animals for each population, and model‐based clustering successfully detected evidence of admixture that matched aspects of their recorded history. For example, shared co‐ancestry was detected, suggesting Boer goats have been introgressed into the Rangeland population. Further, allele frequency data successfully tracked the altered genetic profile that has taken place after 40 years of breeding Australian Cashmere goats using the Rangeland animals as the founding population. Genome‐wide association mapping of the POLL locus revealed a strong signal on goat chromosome 1. The 769‐kb critical interval contained the polled intersex syndrome locus, confirming the genetic basis in non‐European animals is the same as identified previously in Saanen goats. Interestingly, analysis of the haplotypes carried by a small set of sex‐reversed animals, known to be associated with polledness, revealed some animals carried the wild‐type chromosome associated with the presence of horns. This suggests a more complex basis for the relationship between polledness and the intersex condition than initially thought while validating the application of the goat SNP50 BeadChip for fine‐mapping traits in goat.