Dominik Grimm

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

Summary Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.

Genome-wide analysis of local chromatin packing in Arabidopsis thaliana

The spatial arrangement of interphase chromosomes in the nucleus is important for gene expression and genome function in animals and in plants. The recently developed Hi-C technology is an efficacious method to investigate genome packing. Here we present a detailed Hi-C map of the three-dimensional genome organization of the plant Arabidopsis thaliana. We find that local chromatin packing differs from the patterns seen in animals, with kilobasepair-sized segments that have much higher intrachromosome interaction rates than neighboring regions, representing a dominant local structural feature of genome conformation in A. thaliana. These regions, which appear as positive strips on two-dimensional representations of chromatin interaction, are enriched in epigenetic marks H3K27me3, H3.1, and H3.3. We also identify more than 400 insulator-like regions. Furthermore, although topologically associating domains (TADs), which are prominent in animals, are not an obvious feature of A. thaliana genome packing, we found more than 1000 regions that have properties of TAD boundaries, and a similar number of regions analogous to the interior of TADs. The insulator-like, TAD-boundary-like, and TAD-interior-like regions are each enriched for distinct epigenetic marks and are each correlated with different gene expression levels. We conclude that epigenetic modifications, gene density, and transcriptional activity combine to shape the local packing of the A. thaliana nuclear genome.

The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity

Prioritizing missense variants for further experimental investigation is a key challenge in current sequencing studies for exploring complex and Mendelian diseases. A large number of in silico tools have been employed for the task of pathogenicity prediction, including PolyPhen‐2, SIFT, FatHMM, MutationTaster‐2, MutationAssessor, Combined Annotation Dependent Depletion, LRT, phyloP, and GERP++, as well as optimized methods of combining tool scores, such as Condel and Logit. Due to the wealth of these methods, an important practical question to answer is which of these tools generalize best, that is, correctly predict the pathogenic character of new variants. We here demonstrate in a study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. We show that comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Efficient network-guided multi-locus association mapping with graph cuts

Motivation: As an increasing number of genome-wide association studies reveal the limitations of the attempt to explain phenotypic heritability by single genetic loci, there is a recent focus on associating complex phenotypes with sets of genetic loci. Although several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci or do not scale to genome-wide settings. Results: We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype while being connected in an underlying network. Our approach is based on a minimum cut reformulation of the problem of selecting features under sparsity and connectivity constraints, which can be solved exactly and rapidly. SConES outperforms state-of-the-art competitors in terms of runtime, scales to hundreds of thousands of genetic loci and exhibits higher power in detecting causal SNPs in simulation studies than other methods. On flowering time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci that enable accurate phenotype prediction and that are supported by the literature. Availability: Code is available at http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/. Contact: chloe-agathe.azencott@tuebingen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Accurate indel prediction using paired-end short reads

BackgroundOne of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives.ResultsHere, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project (http://www.1001genomes.org) in Arabidopsis thaliana.ConclusionIn this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/.

easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

The easyGWAS platform is a powerful online resource for computing, storing, sharing, and comparing the results of GWAS in both inbred and outbred plant and animal species. The ever-growing availability of high-quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating, and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and data sets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.

Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids

Significance Hybrid progeny of inbred parents are often more fit than their parents. Such hybrid vigor, or heterosis, is the focus of many plant breeding programs, and the rewards are evident. Hybrid maize has for many decades accounted for the majority of seed planted each year in North America and Europe. Despite the prevalence of this phenomenon and its agricultural importance, the genetic basis of heterotic traits is still unclear. We have used a large collection of first-generation hybrids in Arabidopsis thaliana to characterize the genetics of heterosis in this model plant. We have identified loci that contribute substantially to hybrid vigor and show that a subset of these exhibits classical dominance, an important finding with direct implications for crop improvement. The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.

Improved EGFR mutation detection using combined exosomal RNA and circulating tumor DNA in NSCLC patient plasma

Abstract Background A major limitation of circulating tumor DNA (ctDNA) for somatic mutation detection has been the low level of ctDNA found in a subset of cancer patients. We investigated whether using a combined isolation of exosomal RNA (exoRNA) and cell-free DNA (cfDNA) could improve blood-based liquid biopsy for EGFR mutation detection in non-small-cell lung cancer (NSCLC) patients. Patients and methods Matched pretreatment tumor and plasma were collected from 84 patients enrolled in TIGER-X (NCT01526928), a phase 1/2 study of rociletinib in mutant EGFR NSCLC patients. The combined isolated exoRNA and cfDNA (exoNA) was analyzed blinded for mutations using a targeted next-generation sequencing panel (EXO1000) and compared with existing data from the same samples using analysis of ctDNA by BEAMing. Results For exoNA, the sensitivity was 98% for detection of activating EGFR mutations and 90% for EGFR T790M. The corresponding sensitivities for ctDNA by BEAMing were 82% for activating mutations and 84% for T790M. In a subgroup of patients with intrathoracic metastatic disease (M0/M1a; n = 21), the sensitivity increased from 26% to 74% for activating mutations (P = 0.003) and from 19% to 31% for T790M (P = 0.5) when using exoNA for detection. Conclusions Combining exoRNA and ctDNA increased the sensitivity for EGFR mutation detection in plasma, with the largest improvement seen in the subgroup of M0/M1a disease patients known to have low levels of ctDNA and poses challenges for mutation detection on ctDNA alone. Clinical Trials NCT01526928

Genomic Profiles of Diversification and Genotype–Phenotype Association in Island Nematode Lineages

Understanding how new species form requires investigation of evolutionary forces that cause phenotypic and genotypic changes among populations. However, the mechanisms underlying speciation vary and little is known about whether genomes diversify in the same ways in parallel at the incipient scale. We address this using the nematode, Pristionchus pacificus, which resides at an interesting point on the speciation continuum (distinct evolutionary lineages without reproductive isolation), and inhabits heterogeneous environments subject to divergent environmental pressures. Using whole genome re-sequencing of 264 strains, we estimate FST to identify outlier regions of extraordinary differentiation (∼1.725 Mb of the 172.5 Mb genome). We find evidence for shared divergent genomic regions occurring at a higher frequency than expected by chance among populations of the same evolutionary lineage. We use allele frequency spectra to find that, among lineages, 53% of divergent regions are consistent with adaptive selection, whereas 24% and 23% of such regions suggest background selection and restricted gene flow, respectively. In contrast, among populations from the same lineage, similar proportions (34-48%) of divergent regions correspond to adaptive selection and restricted gene flow, whereas 13-22% suggest background selection. Because speciation often involves phenotypic and genomic divergence, we also evaluate phenotypic variation, focusing on pH tolerance, which we find is diverging in a manner corresponding to environmental differences among populations. Taking a genome-wide association approach, we functionally validate a significant genotype-phenotype association for this trait. Our results are consistent with P. pacificus undergoing heterogeneous genotypic and phenotypic diversification related to both evolutionary and environmental processes.

AraPheno: a public database for Arabidopsis thaliana phenotypes

Natural genetic variation makes it possible to discover evolutionary changes that have been maintained in a population because they are advantageous. To understand genotype–phenotype relationships and to investigate trait architecture, the existence of both high-resolution genotypic and phenotypic data is necessary. Arabidopsis thaliana is a prime model for these purposes. This herb naturally occurs across much of the Eurasian continent and North America. Thus, it is exposed to a wide range of environmental factors and has been subject to natural selection under distinct conditions. Full genome sequencing data for more than 1000 different natural inbred lines are available, and this has encouraged the distributed generation of many types of phenotypic data. To leverage these data for meta analyses, AraPheno (https://arapheno.1001genomes.org) provide a central repository of population-scale phenotypes for A. thaliana inbred lines. AraPheno includes various features to easily access, download and visualize the phenotypic data. This will facilitate a comparative analysis of the many different types of phenotypic data, which is the base to further enhance our understanding of the genotype–phenotype map.