Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.     

Incorporating functional annotation information in prioritizing disease associated SNPs from genome wide association studies

With recent advances in genotyping and sequencing technologies,many disease susceptibility loci have been identified.However,much of the genetic heritability remains unexplained and the replication rate between independent studies is still low.Meanwhile,there have been increasing efforts on functional annotations of the entire human genome,such as the Encyclopedia of DNA Elements(ENCODE)project and other similar projects.It has been shown that incorporating these functional annotations to prioritize genome wide association signals may help identify true association signals.However,to our knowledge,the extent of the improvement when functional annotation data are considered has not been studied in the literature.In this article,we propose a statistical framework to estimate the improvement in replication rate with annotation data,and apply it to Crohn’s disease and DNase I hypersensitive sites.The results show that with cell line specific functional annotations,the expected replication rate is improved,but only at modest level.     

Genome-wide association analysis of clinical vs. nonclinical origin provides insights into Saccharomyces cerevisiae pathogenesis

Because domesticated Saccharomyces cerevisiae strains have been used to produce fermented food and beverages for centuries without apparent health implications, S. cerevisiae has always been considered a Generally Recognized As Safe (GRAS) microorganism. However, the number of reported mucosal and systemic S. cerevisiae infections in the human population has increased and fatal infections have occurred even in relatively healthy individuals. In order to gain insight into the pathogenesis of S. cerevisiae and improve our understanding of the emergence of fungal pathogens, we performed a population-based genome-wide environmental association analysis of clinical vs. nonclinical origin in S. cerevisiae. Using tiling array-based, high-density genotypes of 44 clinical and 44 nonclinical S. cerevisiae strains from diverse geographical origins and source substrates, we identified several genetic loci associated with clinical background in S. cerevisiae. Associated polymorphisms within the coding sequences of VRP1, KIC1, SBE22 and PDR5, and the 5' upstream region of YGR146C indicate the importance of pseudohyphal formation, robust cell wall maintenance and cellular detoxification for S. cerevisiae pathogenesis, and constitute good candidates for follow-up verification of virulence and virulence-related factors underlying the pathogenicity of S. cerevisiae.