Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

BACKGROUND: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. RESULTS: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. CONCLUSIONS: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

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

BACKGROUND: Oncopanel 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. RESULTS: In 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 5-100× 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. CONCLUSION: These 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.

Breeding-assisted genomics: Applying meta-GWAS for milling and baking quality in CIMMYT wheat breeding program.

One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop and animal breeding where many individuals are only evaluated for a single year and large but unbalanced datasets can be generated over multiple years. Many wheat breeding programs have focused on increasing bread wheat (Triticum aestivum L.) yield, but processing and end-use quality are critical components when considering its use in feeding the rising population of the next century. The challenges with end-use quality trait improvements are high cost and seed amounts for testing, the latter making selection in early breeding populations impossible. Here we describe a novel approach to identify marker-trait associations within a breeding program using a meta-genome wide association study (GWAS), which combines GWAS analysis from multi-year unbalanced breeding nurseries, in a manner reflecting meta-GWAS in humans. This method facilitated mapping of processing and end-use quality phenotypes from advanced breeding lines (n = 4,095) of the CIMMYT bread wheat breeding program from 2009 to 2014. Using the meta-GWAS we identified marker-trait associations, allele effects, candidate genes, and can select using markers generated in this process. Finally, the scope of this approach can be broadly applied in 'breeding-assisted genomics' across many crops to greatly increase our functional understanding of plant genomes.

Comparative systems biology reveals allelic variation modulating Tocochromanol profiles in Barley (Hordeum vulgare L.).

Tocochromanols are recognized for nutritional content, plant stress response, and seed longevity. Here we present a systems biological approach to characterize and develop predictive assays for genes affecting tocochromanol variation in barley. Major QTL, detected in three regions of a SNP linkage map, affected multiple tocochromanol forms. Candidate genes were identified through barley/rice orthology and sequenced in genotypes with disparate tocochromanol profiles. Gene-specific markers, designed based on observed polymorphism, mapped to the originating QTL, increasing R2 values at the respective loci. Polymorphism within promoter regions corresponded to motifs known to influence gene expression. Quantitative PCR analysis revealed a trend of increased expression in tissues grown at cold temperatures. These results demonstrate utility of a novel method for rapid gene identification and characterization, and provide a resource for efficient development of barley lines with improved tocochromanol profiles.

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.

Technical reproducibility of genotyping SNP arrays used in genome-wide association studies.

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders' quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.

Geographical genomics of human leukocyte gene expression variation in southern Morocco.

Studies of the genetics of gene expression can identify expression SNPs (eSNPs) that explain variation in transcript abundance. Here we address the robustness of eSNP associations to environmental geography and population structure in a comparison of 194 Arab and Amazigh individuals from a city and two villages in southern Morocco. Gene expression differed between pairs of locations for up to a third of all transcripts, with notable enrichment of transcripts involved in ribosomal biosynthesis and oxidative phosphorylation. Robust associations were observed in the leukocyte samples: cis eSNPs (P < 10(-08)) were identified for 346 genes, and trans eSNPs (P < 10(-11)) for 10 genes. All of these associations were consistent both across the three sample locations and after controlling for ancestry and relatedness. No evidence of large-effect trans-acting mediators of the pervasive environmental influence was found; instead, genetic and environmental factors acted in a largely additive manner.

SNP selection and multidimensional scaling to quantify population structure.

In the new era of large-scale collaborative Genome Wide Association Studies (GWAS), population stratification has become a critical issue that must be addressed. In order to build upon the methods developed to control the confounding effect of a structured population, it is extremely important to visualize and quantify that effect. In this work, we develop methodology for single nucleotide polymorphism (SNP) selection and subsequent population stratification visualization based on deviation from Hardy-Weinberg equilibrium in conjunction with non-metric multidimensional scaling (MDS); a distance-based multivariate technique. Through simulation, it is shown that SNP selection based on Hardy-Weinberg disequilibrium (HWD) is robust against confounding linkage disequilibrium patterns that have been problematic in past studies and methods as well as producing a differentiated SNP set. Non-metric MDS is shown to be a multivariate visualization tool preferable to principal components in conjunction with HWD SNP selection through theoretical and empirical study from HapMap samples. The proposed selection tool offers a simple and effective way to select appropriate substructure-informative markers for use in exploring the effect that population stratification may have in association studies.