Explore the vast range of titles available.

Background Cassava is an important food crop in tropical and sub-tropical regions worldwide. In Africa, cassava production is widely affected by cassava mosaic disease (CMD), which is caused by the African cassava mosaic geminivirus that is transmitted by whiteflies. Cassava breeders often use a single locus, CMD2 , for introducing CMD resistance into susceptible cultivars. The CMD2 locus has been genetically mapped to a 10-Mbp region, but its organization and genes as well as their functions are unknown. Results We report haplotype-resolved de novo assemblies and annotations of the genomes for the African cassava cultivar TME (tropical Manihot esculenta ), which is the origin of CMD2 , and the CMD-susceptible cultivar 60444. The assemblies provide phased haplotype information for over 80% of the genomes. Haplotype comparison identified novel features previously hidden in collapsed and fragmented cassava genomes, including thousands of allelic variants, inter-haplotype diversity in coding regions, and patterns of diversification through allele-specific expression. Reconstruction of the CMD2 locus revealed a highly complex region with nearly identical gene sets but limited microsynteny between the two cultivars. Conclusions The genome maps of the CMD2 locus in both 60444 and TME3, together with the newly annotated genes, will help the identification of the causal genetic basis of CMD2 resistance to geminiviruses. Our de novo cassava genome assemblies will also facilitate genetic mapping approaches to narrow the large CMD2 region to a few candidate genes for better informed strategies to develop robust geminivirus resistance in susceptible cassava cultivars.

Cell identity is determined by the selective activation or silencing of specific genes via transcription factor binding and epigenetic modifications on the genome. Chromatin immunoprecipitation (ChIP) has been the standard technique for mapping the sites of transcription factor binding and histone modification. Recently, alternative methods to ChIP have been developed for addressing the increasing demands for low-input epigenomic profiling. Chromatin integration labeling (ChIL) followed by sequencing (ChIL-seq) has been demonstrated to be particularly useful for epigenomic profiling of low-input samples or even single cells because the technique amplifies the target genomic sequence before cell lysis. After labeling the target protein or modification in situ with an oligonucleotide-conjugated antibody (ChIL probe), the nearby genome sequence is amplified by Tn5 transposase-mediated transposition followed by T7 RNA polymerase-mediated transcription. ChIL-seq enables the detection of the antibody target localization under a fluorescence microscope and at the genomic level. Here we describe the detailed protocol of ChIL-seq with assessment methods for the key steps, including ChIL probe reaction, transposition, in situ transcription and sequencing library preparation. The protocol usually takes 3 d to prepare the sequencing library, including overnight incubations for the ChIL probe reaction and in situ transcription. The ChIL probe can be separately prepared and stored for several months, and its preparation and evaluation protocols are also documented in detail. An optional analysis for multiple targets (multitarget ChIL-seq) is also described. We anticipate that the protocol presented here will make the ChIL technique more widely accessible for analyzing precious samples and facilitate further applications. The authors describe detailed procedures for an epigenomic profiling method suitable for low-input samples that is based on in situ labeling with an oligonucleotide-conjugated antibody.

Faithful reconstruction of haplotypes from diploid marker data (phasing) is important for many kinds of genetic analyses, including mapping of trait loci, prediction of genomic breeding values, and identification of signatures of selection. In human genetics, phasing most often exploits population information (linkage disequilibrium), while in animal genetics the primary source of information is familial (Mendelian segregation and linkage). We herein develop and evaluate a method that simultaneously exploits both sources of information. It builds on hidden Markov models that were initially developed to exploit population information only. We demonstrate that the approach improves the accuracy of allele phasing as well as imputation of missing genotypes. Reconstructed haplotypes are assigned to hidden states that are shown to correspond to clusters of genealogically related chromosomes. We show that these cluster states can directly be used to fine map QTL. The method is computationally effective at handling large data sets based on high-density SNP panels.

We identified quantitative trait loci (QTL) underlying variation for flowering time in a doubled haploid (DH) population of vernalisation—responsive canola (Brassica napus L.) cultivars Skipton and Ag-Spectrum and aligned them with physical map positions of predicted flowering genes from the Brassica rapa genome. Significant genetic variation in flowering time and response to vernalisation were observed among the DH lines from Skipton/Ag-Spectrum. A molecular linkage map was generated comprising 674 simple sequence repeat, sequence-related amplified polymorphism, sequence characterised amplified region, Diversity Array Technology, and candidate gene based markers loci. QTL analysis indicated that flowering time is a complex trait and is controlled by at least 20 loci, localised on ten different chromosomes. These loci each accounted for between 2.4 and 28.6 % of the total genotypic variation for first flowering and response to vernalisation. However, identification of consistent QTL was found to be dependant upon growing environments. We compared the locations of QTL with the physical positions of predicted flowering time genes located on the sequenced genome of B. rapa. Some QTL associated with flowering time on A02, A03, A07, and C06 may represent homologues of known flowering time genes in Arabidopsis; VERNALISATION INSENSITIVE 3, APETALA1, CAULIFLOWER, FLOWERING LOCUS C, FLOWERING LOCUS T, CURLY LEAF, SHORT VEGETATIVE PHASE, GA3 OXIDASE, and LEAFY. Identification of the chromosomal location and effect of the genes influencing flowering time may hasten the development of canola varieties having an optimal time for flowering in target environments such as for low rainfall areas, via marker-assisted selection.

Interactions among loci or between genes and environmental factors make a substantial contribution to variation in complex traits such as disease susceptibility. Nonetheless, many studies that attempt to identify the genetic basis of complex traits ignore the possibility that loci interact. We argue that epistasis should be accounted for in complex trait studies; we critically assess current study designs for detecting epistasis and discuss how these might be adapted for use in additional populations, including humans.

Upland cotton plays a critical role not only in the textile industry, but also in the production of important secondary metabolites, such as oil and proteins. Construction of a high-density linkage map and identifying yield and seed trait quantitative trail loci (QTL) are prerequisites for molecular marker-assisted selective breeding projects. Here, we update a high-density upland cotton genetic map from recombinant inbred lines. A total of 25,313 SSR primer pairs were screened for polymorphism between Yumian 1 and T586, and 1712 SSR primer pairs were used to genotype the mapping population and construct a map. An additional 1166 loci have been added to our previously published map with 509 SSR markers. The updated genetic map spans a total recombinant length of 3338.2 cM and contains 1675 SSR loci and nine morphological markers, with an average interval of 1.98 cM between adjacent markers. Green lint (Lg) mapped on chromosome 15 in a previous report is mapped in an interval of 2.6 cM on chromosome 21. Based on the map and phenotypic data from multiple environments, 79 lint percentage and seed nutrient trait QTL are detected. These include 8 lint percentage, 13 crude protein, 15 crude oil, 8 linoleic, 10 oleic, 13 palmitic, and 12 stearic acid content QTL. They explain 3.5–62.7 % of the phenotypic variation observed. Four morphological markers identified have a major impact on lint percentage and cottonseed nutrients traits. In this study, our genetic map provides new sights into the tetraploid cotton genome. Furthermore, the stable QTL and morphological markers could be used for fine-mapping and map-based cloning.

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10−6). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10−15), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size. •A novel reference guided multivariate approach to reveal relationships of features.•Designed for imaging genomics to extract specific genetic factors from the genome•Simulation and real data application demonstrate its feasibility.•Schizophrenia-related gray mater reduction related to multiple genetic variants

Background The construction of linkage maps is a first step in exploring the genetic basis for adaptive phenotypic divergence in closely related species by quantitative trait locus (QTL) analysis. Linkage maps are also useful for comparative genomics in non-model organisms. Advances in genomics technologies make it more feasible than ever to study the genetics of adaptation in natural populations. Restriction-site associated DNA (RAD) sequencing in next-generation sequencers facilitates the development of many genetic markers and genotyping. We aimed to construct a linkage map of the gudgeons of the genus Gnathopogon (Cyprinidae) for comparative genomics with the zebrafish Danio rerio (a member of the same family as gudgeons) and for the future QTL analysis of the genetic architecture underlying adaptive phenotypic evolution of Gnathopogon . Results We constructed the first genetic linkage map of Gnathopogon using a 198 F 2 interspecific cross between two closely related species in Japan: river-dwelling Gnathopogon elongatus and lake-dwelling Gnathopogon caerulescens . Based on 1,622 RAD-tag markers, a linkage map spanning 1,390.9 cM with 25 linkage groups and an average marker interval of 0.87 cM was constructed. We also identified a region involving female-specific transmission ratio distortion (TRD). Synteny and collinearity were extensively conserved between Gnathopogon and zebrafish. Conclusions The dense SNP-based linkage map presented here provides a basis for future QTL analysis. It will also be useful for transferring genomic information from a “traditional” model fish species, zebrafish, to screen candidate genes underlying ecologically important traits of the gudgeons.

Microsatellites are tandemly repeated short DNA sequences that are favored as molecular-genetic markers due to their high polymorphism index. Plant genomes characterized to date exhibit taxon-specific differences in frequency, genomic location, and motif structure of microsatellites, indicating that extant microsatellites originated recently and turn over quickly. With the goal of using microsatellite markers to integrate the physical and genetic maps of Medicago truncatula, we surveyed the frequency and distribution of perfect microsatellites in 77 Mbp of gene-rich BAC sequences, 27 Mbp of nonredundant transcript sequences, 20 Mbp of random whole genome shotgun sequences, and 49 Mbp of BAC-end sequences. Microsatellites are predominantly located in gene-rich regions of the genome, with a density of one long (i.e., ≥20 nt) microsatellite every 12 kbp, while the frequency of individual motifs varied according to the genome fraction under analysis. A total of 1,236 microsatellites were analyzed for polymorphism between parents of our reference intraspecific mapping population, revealing that motifs (AT)n, (AG)n, (AC)n, and (AAT)n exhibit the highest allelic diversity. A total of 378 genetic markers could be integrated with sequenced BAC clones, anchoring 274 physical contigs that represent 174 Mbp of the genome and composing an estimated 70% of the euchromatic gene space.

The tagging of genomic loci in living cells provides visual evidence for the study of genomic spatial organization and gene interaction. CRISPR/dCas9 (clustered regularly interspaced short palindromic repeats/deactivated Cas9) labeling system labels genes through binding of the dCas9/sgRNA/fluorescent protein complex to repeat sequences in the target genomic loci. However, the existence of numerous fluorescent proteins in the nucleus usually causes a high background fluorescent readout. This study aims to limit the number of fluorescent modules entering the nucleus by redesigning the current CRISPR/dCas9-SunTag labeling system consisting of dCas9-SunTag-NLS (target module) and scFv-sfGFP-NLS (signal module). We removed the nuclear location sequence (NLS) of the signal module and inserted two copies of EGFP into the signal module. The ratio of the fluorescent intensity of the nucleus to that of the cytoplasm (N/C ratio) was decreased by 71%, and the ratio of the signal to the background (S/B ratio) was increased by 1.6 times. The system can stably label randomly selected genomic loci with as few as 9 repeat sequences.