Differential Gene Expression and Epiregulation of Alpha Zein Gene Copies in Maize Haplotypes

Multigenic traits are very common in plants and cause diversity. Nutritional quality is such a trait, and one of its factors is the composition and relative expression of storage protein genes. In maize, they represent a medium-size gene family distributed over several chromosomes and unlinked locations. Two inbreds, B73 and BSSS53, both from the Iowa Stiff Stock Synthetic collection, have been selected to analyze allelic and non-allelic variability in these regions that span between 80–500 kb of chromosomal DNA. Genes were copied to unlinked sites before and after allotetraploidization of maize, but before transposition enlarged intergenic regions in a haplotype-specific manner. Once genes are copied, expression of donor genes is reduced relative to new copies. Epigenetic regulation seems to contribute to silencing older copies, because some of them can be reactivated when endosperm is maintained as cultured cells, indicating that copy number variation might contribute to a reserve of gene copies. Bisulfite sequencing of the promoter region also shows different methylation patterns among gene clusters as well as differences between tissues, suggesting a possible position effect on regulatory mechanisms as a result of inserting copies at unlinked locations. The observations offer a potential paradigm for how different gene families evolve and the impact this has on their expression and regulation of their members

SNP Discovery and Chromosome Anchoring Provide the First Physically-Anchored Hexaploid Oat Map and Reveal Synteny with Model Species

A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat (Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

BackgroundOncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.ResultsIn reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100x more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.ConclusionThese new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.Peer reviewe

Phylogeny of the genus Azospirillum based on 16S rDNA sequence

The 16S rDNA of 17 strains of Azospirillum, 14 assigned to one of the known species A. amazonense A. brasilense A. halopraeferens A. irakense and A. lipoferum, and the other three of uncertain taxonomic position, was sequenced after polymerase chain reaction amplification and analysed in order to investigate the phylogenetic relationships at the intra-generic and super-generic level. The phylogenetic analysis confirms that the genus Azospirillum constitutes a phylogenetically separate entity within the a subclass of Proteobacteria and that the five species are well defined. A. brasilense and A. lipoferum are closely related species and form one cluster together with A. halopraeferens; the pair of species A. amazonense and A. irakense forms a second cluster in which Rhodospirillum centenum is also placed

Batch Effects in the BRLMM Genotype Calling Algorithm Influence GWAS Results for the Affymetrix 500K Array.

The Affymetrix GeneChip Human Mapping 500K array is common for genome-wide association studies (GWASs). Recent findings highlight the importance of accurate genotype calling algorithms to reduce the inflation in Type I and Type II error rates. Differential results due to genotype calling errors can introduce severe bias in case-control association study results. Using data from the Wellcome Trust Case Control Consortium, 1991 individuals with coronary artery disease (CAD) and 1500 controls from the UK Blood Services (NBS) were genotyped on the Affymetrix 500K array. Different batch sizes and compositions were used in the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) genotype calling algorithm to assess the batch effect on downstream association analysis. Results show that composition (cases and controls genotyped simultaneously or separate) and size (number of individuals processed by BRLMM at a time) can create 2-3% discordance in the results for quality control and statistical analysis and may contribute to the lack of reproducibility between GWASs. The changes in batch size are largely responsible for differential single- nucleotide polymorphism results, yet we observe evidence of an interactive effect of batch size and composition that contributes to discordant results in the list of significantly associated loci