Harvesting the Promising Fruits of Genomics: Applying Genome Sequencing Technologies to Crop Breeding

Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. When combined with precise phenotyping methods, these technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population. Here we summarize current trends and future prospects for utilizing NGS-based technologies to develop crops with improved trait performance and increase the efficiency of modern plant breeding. It is our hope that the application of NGS technologies to plant breeding will help us to meet the challenge of feeding a growing world population.

This article is part of the PLOS Biology Collection “The Promise of Plant Translational Research.”

     

A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.     

Wham: Identifying Structural Variants of Biological Consequence

Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools–Lumpy, Delly and SoftSearch–and demonstrate Wham’s ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.

This is PLOS Computational Biology software paper.

     

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels.     

Anticipation of Personal Genomics Data Enhances Interest and Learning Environment in Genomics and Molecular Biology Undergraduate Courses

An important discussion at colleges is centered on determining more effective models for teaching undergraduates. As personalized genomics has become more common, we hypothesized it could be a valuable tool to make science education more hands on, personal, and engaging for college undergraduates. We hypothesized that providing students with personal genome testing kits would enhance the learning experience of students in two undergraduate courses at Brigham Young University: Advanced Molecular Biology and Genomics. These courses have an emphasis on personal genomics the last two weeks of the semester. Students taking these courses were given the option to receive personal genomics kits in 2014, whereas in 2015 they were not. Students sent their personal genomics samples in on their own and received the data after the course ended. We surveyed students in these courses before and after the two-week emphasis on personal genomics to collect data on whether anticipation of obtaining their own personal genomic data impacted undergraduate student learning. We also tested to see if specific personal genomic assignments improved the learning experience by analyzing the data from the undergraduate students who completed both the pre- and post-course surveys. Anticipation of personal genomic data significantly enhanced student interest and the learning environment based on the time students spent researching personal genomic material and their self-reported attitudes compared to those who did not anticipate getting their own data. Personal genomics homework assignments significantly enhanced the undergraduate student interest and learning based on the same criteria and a personal genomics quiz. We found that for the undergraduate students in both molecular biology and genomics courses, incorporation of personal genomic testing can be an effective educational tool in undergraduate science education.     

STORMSeq: An Open-Source,User-Friendly Pipeline for Processing Personal Genomics Data in the Cloud

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5–10 hours to process a full exome sequence and $30 and 3–8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.     

The Genomics of Speciation in Drosophila: Diversity, Divergence, and Introgression Estimated Using Low-Coverage Genome Sequencing

In nature, closely related species may hybridize while still retaining their distinctive identities. Chromosomal regions that experience reduced recombination in hybrids, such as within inversions, have been hypothesized to contribute to the maintenance of species integrity. Here, we examine genomic sequences from closely related fruit fly taxa of the Drosophila pseudoobscura subgroup to reconstruct their evolutionary histories and past patterns of genic exchange. Partial genomic assemblies were generated from two subspecies of Drosophila pseudoobscura (D. ps.) and an outgroup species, D. miranda. These new assemblies were compared to available assemblies of D. ps. pseudoobscura and D. persimilis, two species with overlapping ranges in western North America. Within inverted regions, nucleotide divergence among each pair of the three species is comparable, whereas divergence between D. ps. pseudoobscura and D. persimilis in non-inverted regions is much lower and closer to levels of intraspecific variation. Using molecular markers flanking each of the major chromosomal inversions, we identify strong crossover suppression in F₁ hybrids extending over 2 megabase pairs (Mbp) beyond the inversion breakpoints. These regions of crossover suppression also exhibit the high nucleotide divergence associated with inverted regions. Finally, by comparison to a geographically isolated subspecies, D. ps. bogotana, our results suggest that autosomal gene exchange between the North American species, D. ps. pseudoobscura and D. persimilis, occurred since the split of the subspecies, likely within the last 200,000 years. We conclude that chromosomal rearrangements have been vital to the ongoing persistence of these species despite recent hybridization. Our study serves as a proof-of-principle on how whole genome sequencing can be applied to formulate and test hypotheses about species formation in lesser-known non-model systems.     

Exome Sequencing of Only Seven Qataris Identifies Potentially Deleterious Variants in the Qatari Population

The Qatari population, located at the Arabian migration crossroads of African and Eurasia, is comprised of Bedouin, Persian and African genetic subgroups. By deep exome sequencing of only 7 Qataris, including individuals in each subgroup, we identified 2,750 nonsynonymous SNPs predicted to be deleterious, many of which are linked to human health, or are in genes linked to human health. Many of these SNPs were at significantly elevated deleterious allele frequency in Qataris compared to other populations worldwide. Despite the small sample size, SNP allele frequency was highly correlated with a larger Qatari sample. Together, the data demonstrate that exome sequencing of only a small number of individuals can reveal genetic variations with potential health consequences in understudied populations.     

Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies

There is strong evidence that rare variants are involved in complex disease etiology. The first step in implicating rare variants in disease etiology is their identification through sequencing in both randomly ascertained samples (e.g., the 1,000 Genomes Project) and samples ascertained according to disease status. We investigated to what extent rare variants will be observed across the genome and in candidate genes in randomly ascertained samples, the magnitude of variant enrichment in diseased individuals, and biases that can occur due to how variants are discovered. Although sequencing cases can enrich for casual variants, when a gene or genes are not involved in disease etiology, limiting variant discovery to cases can lead to association studies with dramatically inflated false positive rates.     

Long Span DNA Paired-End-Tag (DNA-PET) Sequencing Strategy for the Interrogation of Genomic Structural Mutations and Fusion-Point-Guided Reconstruction of Amplicons

Structural variations (SVs) contribute significantly to the variability of the human genome and extensive genomic rearrangements are a hallmark of cancer. While genomic DNA paired-end-tag (DNA-PET) sequencing is an attractive approach to identify genomic SVs, the current application of PET sequencing with short insert size DNA can be insufficient for the comprehensive mapping of SVs in low complexity and repeat-rich genomic regions. We employed a recently developed procedure to generate PET sequencing data using large DNA inserts of 10–20 kb and compared their characteristics with short insert (1 kb) libraries for their ability to identify SVs. Our results suggest that although short insert libraries bear an advantage in identifying small deletions, they do not provide significantly better breakpoint resolution. In contrast, large inserts are superior to short inserts in providing higher physical genome coverage for the same sequencing cost and achieve greater sensitivity, in practice, for the identification of several classes of SVs, such as copy number neutral and complex events. Furthermore, our results confirm that large insert libraries allow for the identification of SVs within repetitive sequences, which cannot be spanned by short inserts. This provides a key advantage in studying rearrangements in cancer, and we show how it can be used in a fusion-point-guided-concatenation algorithm to study focally amplified regions in cancer.     

Searching for Optimal Sensory Signals: Iterative Stimulus Reconstruction in Closed-Loop Experiments

Shaped by evolutionary processes, sensory systems often represent behaviorally relevant stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability could therefore provide an important clue in a search for relevant sensory signals. We explore this relation and introduce a novel iterative algorithm that allows one to find stimuli that are reliably represented by the sensory system under study. To assess the quality of a neural representation, we use stimulus reconstruction methods. The algorithm starts with the presentation of an initial stimulus (e.g. white noise). The evoked spike train is recorded and used to reconstruct the stimulus online. Within a closed-loop setup, this reconstruction is then played back to the sensory system. Iterating this procedure, the newly generated stimuli can be better and better reconstructed. We demonstrate the feasibility of this method by applying it to auditory receptor neurons in locusts. Our data show that the optimal stimuli often exhibit pronounced sub-threshold periods that are interrupted by short, yet intense pulses. Similar results are obtained for simple model neurons and suggest that these stimuli are encoded with high reliability by a large class of neurons.     

A Systematic Assessment of Accuracy in Detecting Somatic Mosaic Variants by Deep Amplicon Sequencing: Application to NF2 Gene

The accurate detection of low-allelic variants is still challenging, particularly for the identification of somatic mosaicism, where matched control sample is not available. High throughput sequencing, by the simultaneous and independent analysis of thousands of different DNA fragments, might overcome many of the limits of traditional methods, greatly increasing the sensitivity. However, it is necessary to take into account the high number of false positives that may arise due to the lack of matched control samples. Here, we applied deep amplicon sequencing to the analysis of samples with known genotype and variant allele fraction (VAF) followed by a tailored statistical analysis. This method allowed to define a minimum value of VAF for detecting mosaic variants with high accuracy. Then, we exploited the estimated VAF to select candidate alterations in NF2 gene in 34 samples with unknown genotype (30 blood and 4 tumor DNAs), demonstrating the suitability of our method. The strategy we propose optimizes the use of deep amplicon sequencing for the identification of low abundance variants. Moreover, our method can be applied to different high throughput sequencing approaches to estimate the background noise and define the accuracy of the experimental design.