The genome of Chenopodium quinoa.

Chenopodium quinoa (quinoa) is a highly nutritious grain identified as an important crop to improve world food security. Unfortunately, few resources are available to facilitate its genetic improvement. Here we report the assembly of a high-quality, chromosome-scale reference genome sequence for quinoa, which was produced using single-molecule real-time sequencing in combination with optical, chromosome-contact and genetic maps. We also report the sequencing of two diploids from the ancestral gene pools of quinoa, which enables the identification of sub-genomes in quinoa, and reduced-coverage genome sequences for 22 other samples of the allotetraploid goosefoot complex. The genome sequence facilitated the identification of the transcription factor likely to control the production of anti-nutritional triterpenoid saponins found in quinoa seeds, including a mutation that appears to cause alternative splicing and a premature stop codon in sweet quinoa strains. These genomic resources are an important first step towards the genetic improvement of quinoa.

Corrigendum: The genome of Chenopodium quinoa.

This corrects the article DOI: 10.1038/nature21370.

OMWare: a tool for efficient assembly of genome-wide physical maps.

BACKGROUND: Physical mapping of DNA with restriction enzymes allows for the characterization and assembly of much longer molecules than is feasible with sequencing. However, assemblies of physical map data are sensitive to input parameters, which describe noise inherent in the data collection process. One possible way to determine the parameter values that best describe a dataset is by trial and error. RESULTS: Here we present OMWare, a tool that efficiently generated 405 de novo map assemblies of a single datasets collected from the cotton species Gossypium raimondii. The assemblies were generated using various input parameter values, and were completed more efficiently by re-using compatible intermediate results. These assemblies were assayed for contiguity, internal consistency, and accuracy. CONCLUSIONS: Resulting assemblies had variable qualities. Although highly accurate assemblies were found, contiguity and internal consistency metrics were poor predictors of accuracy.

Population substructure in Cache County, Utah: the Cache County study.

BACKGROUND: Population stratification is a key concern for genetic association analyses. In addition, extreme homogeneity of ethnic origins of a population can make it difficult to interpret how genetic associations in that population may translate into other populations. Here we have evaluated the genetic substructure of samples from the Cache County study relative to the HapMap Reference populations and data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS: Our findings show that the Cache County study is similar in ethnic diversity to the self-reported "Whites" in the ADNI sample and less homogenous than the HapMap CEU population. CONCLUSIONS: We conclude that the Cache County study is genetically representative of the general European American population in the USA and is an appropriate population for conducting broadly applicable genetic studies.

Variant Tool Chest: an improved tool to analyze and manipulate variant call format (VCF) files.

BACKGROUND: Since the advent of next-generation sequencing many previously untestable hypotheses have been realized. Next-generation sequencing has been used for a wide range of studies in diverse fields such as population and medical genetics, phylogenetics, microbiology, and others. However, this novel technology has created unanticipated challenges such as the large numbers of genetic variants. Each caucasian genome has more than four million single nucleotide variants, insertions and deletions, copy number variants, and structural variants. Several formats have been suggested for storing these variants; however, the variant call format (VCF) has become the community standard. RESULTS: We developed new software called the Variant Tool Chest (VTC) to provide much needed tools to work with VCF files. VTC provides a variety of tools for manipulating, comparing, and analyzing VCF files beyond the functionality of existing tools. In addition, VTC was written to be easily extended with new tools. CONCLUSIONS: Variant Tool Chest brings new and important functionality that complements and integrates well with existing software. VTC is available at https://github.com/mebbert/VariantToolChest.

Genome Mapping in Plant Comparative Genomics.

Genome mapping produces fingerprints of DNA sequences to construct a physical map of the whole genome. It provides contiguous, long-range information that complements and, in some cases, replaces sequencing data. Recent advances in genome-mapping technology will better allow researchers to detect large (>1kbp) structural variations between plant genomes. Some molecular and informatics complications need to be overcome for this novel technology to achieve its full utility. This technology will be useful for understanding phenotype responses due to DNA rearrangements and will yield insights into genome evolution, particularly in polyploids. In this review, we outline recent advances in genome-mapping technology, including the processes required for data collection and analysis, and applications in plant comparative genomics.

Population-based analysis of Alzheimer's disease risk alleles implicates genetic interactions.

BACKGROUND: Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer's disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and nonadditive interactions on LOAD status prediction performance. METHODS: 2419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and receiver operator characteristic analysis to assess the LOAD status prediction performance of these loci using additive and nonadditive models and compared odds ratios and PAFs between AlzGene.org and Cache County. RESULTS: Odds ratios were comparable between Cache County and AlzGene.org when identical single nucleotide polymorphisms were genotyped. PAFs from AlzGene.org ranged from 2.25% to 37%; those from Cache County ranged from .05% to 20%. Including non-APOE alleles significantly improved LOAD status prediction performance (area under the curve = .80) over APOE alone (area under the curve = .78) when not constrained to an additive relationship (p < .03). We identified potential allelic interactions (p values uncorrected): CD33-MS4A4E (synergy factor = 5.31; p < .003) and CLU-MS4A4E (synergy factor = 3.81; p < .016). CONCLUSIONS: Although nonadditive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer's disease etiology.