Integrative genome-wide association studies (GWAS) to understand complex genetic architecture of quantitative traits in chickpea

Development of high-yielding stress-tolerant chickpea cultivars is essential to enhance its yield potential and productivity amidst climate change scenario. Unfortunately, superior lines/recombinants producing higher pod and seed yield under stress are not available in world chickpea collection. Therefore, genetic dissection of complex stress tolerance and yield-contributing quantitative traits is the prime objective in current chickpea genomics and breeding research. Our study employed diverse GWAS-assisted integrated genomic strategies involving classical genetic inheritance analysis, QTL mapping, differential transcript profiling, molecular haplotyping and haplotype-based gene domestication/ evolution study for rapid quantitative dissection of complex yield and stress tolerance traits in chickpea. To accomplish this, multi-location/years replicated yield traits-related field phenotyping and high-throughput marker genotyping information generated from numerous natural germplasm accessions (association panel) and multiple intra- and inter-specific mapping populations of chickpea were deployed in the aforesaid combinatorial genomic approaches. These analyses delineated 12 novel alleles and six haplotypes from 10 transcription factor genes and 16 major QTLs/eQTLs governing yield and stress tolerance traits that were mapped on 10 ultra-high density chickpea genetic linkage maps. The superior natural alleles/haplotypes of two major genes (QTLs) regulating seed weight and pod/seed number identified from cultivated and wild Cicer gene pools are being introduced into multiple high-yielding Indian varieties of chickpea for its marker-assisted genetic improvement. The potential molecular signatures delineated using integrated genomics- assisted breeding strategies have functional significance to understand the molecular genetic mechanism and natural allelic diversity-led domestication pattern underlying these complex quantitative traits at a genome-wide scale, leading to fast-paced translational genomics for chickpea genetic enhancement. These essential outcomes will be useful for devising the most efficient strategies to produce high-yielding climate-resilient chickpea cultivars for sustaining global food security

A genome-wide SNP scan accelerates trait-regulatory genomic loci identification in chickpea

We identified 44844 high-quality SNPs by sequencing 92 diverse chickpea accessions belonging to a seed and pod trait-specific association panel using reference genome- and de novo-based GBS (genotyping-by-sequencing) assays. A GWAS (genome-wide association study) in an association panel of 211, including the 92 sequenced accessions, identified 22 major genomic loci showing significant association (explaining 23–47% phenotypic variation) with pod and seed number/plant and 100-seed weight. Eighteen trait-regulatory major genomic loci underlying 13 robust QTLs were validated and mapped on an intra-specific genetic linkage map by QTL mapping. A combinatorial approach of GWAS, QTL mapping and gene haplotype-specific LD mapping and transcript profiling uncovered one superior haplotype and favourable natural allelic variants in the upstream regulatory region of a CesA-type cellulose synthase (Ca_Kabuli_CesA3) gene regulating high pod and seed number/plant (explaining 47% phenotypic variation) in chickpea. The up-regulation of this superior gene haplotype correlated with increased transcript expression of Ca_Kabuli_CesA3 gene in the pollen and pod of high pod/seed number accession, resulting in higher cellulose accumulation for normal pollen and pollen tube growth. A rapid combinatorial genome-wide SNP genotyping-based approach has potential to dissect complex quantitative agronomic traits and delineate trait-regulatory genomic loci (candidate genes) for genetic enhancement in crop plants, including chickpea

Employing genome-wide SNP discovery and genotyping strategy to extrapolate the natural allelic diversity and domestication patterns in chickpea

The genome-wide discovery and high-throughput genotyping of SNPs in chickpea natural germplasm lines is indispensable to extrapolate their natural allelic diversity, domestication, and linkage disequilibrium (LD) patterns leading to the genetic enhancement of this vital legume crop. We discovered 44,844 high-quality SNPs by sequencing of 93 diverse cultivated desi, kabuli, and wild chickpea accessions using reference genome- and de novo-based GBS (genotyping-by-sequencing) assays that were physically mapped across eight chromosomes of desi and kabuli. Of these, 22,542 SNPs were structurally annotated in different coding and non-coding sequence components of genes. Genes with 3296 non-synonymous and 269 regulatory SNPs could functionally differentiate accessions based on their contrasting agronomic traits. A high experimental validation success rate (92%) and reproducibility (100%) along with strong sensitivity (93–96%) and specificity (99%) of GBS-based SNPs was observed. This infers the robustness of GBS as a high-throughput assay for rapid large-scale mining and genotyping of genome-wide SNPs in chickpea with sub-optimal use of resources. With 23,798 genome-wide SNPs, a relatively high intra-specific polymorphic potential (49.5%) and broader molecular diversity (13–89%)/functional allelic diversity (18–77%) was apparent among 93 chickpea accessions, suggesting their tremendous applicability in rapid selection of desirable diverse accessions/inter-specific hybrids in chickpea crossbred varietal improvement program. The genome-wide SNPs revealed complex admixed domestication pattern, extensive LD estimates (0.54–0.68) and extended LD decay (400–500 kb) in a structured population inclusive of 93 accessions. These findings reflect the utility of our identified SNPs for subsequent genome-wide association study (GWAS) and selective sweep-based domestication trait dissection analysis to identify potential genomic loci (gene-associated targets) specifically regulating important complex quantitative agronomic traits in chickpea. The numerous informative genome-wide SNPs, natural allelic diversity-led domestication pattern, and LD-based information generated in our study have got multidimensional applicability with respect to chickpea genomics-assisted breeding

ABC Transporter-Mediated Transport of Glutathione Conjugates Enhances Seed Yield and Quality in Chickpea

The identification of functionally relevant molecular tags is vital for genomics-assisted crop improvement and enhancement of seed yield, quality, and productivity in chickpea (Cicer arietinum). The simultaneous improvement of yield/productivity as well as quality traits often requires pyramiding of multiple genes, which remains a major hurdle given various associated epistatic and pleotropic effects. Unfortunately, no single gene that can improve yield/productivity along with quality and other desirable agromorphological traits is known, hampering the genetic enhancement of chickpea. Using a combinatorial genomics-assisted breeding and functional genomics strategy, this study identified natural alleles and haplotypes of an ABCC3-type transporter gene that regulates seed weight, an important domestication trait, by transcriptional regulation and modulation of the transport of glutathione conjugates in seeds of desi and kabuli chickpea. The superior allele/haplotype of this gene introgressed in desi and kabuli near-isogenic lines enhances the seed weight, yield, productivity, and multiple desirable plant architecture and seed-quality traits without compromising agronomic performance. These salient findings can expedite crop improvement endeavors and the development of nutritionally enriched high-yielding cultivars in chickpea

The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability

High density SNP and DArT-based genetic linkage maps of two closely related oil palm populations

Oil palm (Elaeis guineensis Jacq.) is an outbreeding perennial tree crop with long breeding cycles, typically 12 years. Molecular marker technologies can greatly improve the breeding efficiency of oil palm. This study reports the first use of the DArTseq platform to genotype two closely related self-pollinated oil palm populations, namely AA0768 and AA0769 with 48 and 58 progeny respectively. Genetic maps were constructed using the DArT and SNP markers generated in combination with anchor SSR markers. Both maps consisted of 16 major independent linkage groups (2n = 2× = 32) with 1399 and 1466 mapped markers for the AA0768 and AA0769 populations, respectively, including the morphological trait “shell-thickness” (Sh). The map lengths were 1873.7 and 1720.6 cM with an average marker density of 1.34 and 1.17 cM, respectively. The integrated map was 1803.1 cM long with 2066 mapped markers and average marker density of 0.87 cM. A total of 82% of the DArTseq marker sequence tags identified a single site in the published genome sequence, suggesting preferential targeting of gene-rich regions by DArTseq markers. Map integration of higher density focused around the Sh region identified closely linked markers to the Sh, with D.15322 marker 0.24 cM away from the morphological trait and 5071 bp from the transcriptional start of the published SHELL gene. Identification of the Sh marker demonstrates the robustness of using the DArTseq platform to generate high density genetic maps of oil palm with good genome coverage. Both genetic maps and integrated maps will be useful for quantitative trait loci analysis of important yield traits as well as potentially assisting the anchoring of genetic maps to genomic sequences

A genome-scale integrated approach aids in genetic dissection of complex flowering time trait in chickpea

A combinatorial approach of candidate gene-based association analysis and genome-wide association study (GWAS) integrated with QTL mapping, differential gene expression profiling and molecular haplotyping was deployed in the present study for quantitative dissection of complex flowering time trait in chickpea. Candidate gene-based association mapping in a flowering time association panel (92 diverse desi and kabuli accessions) was performed by employing the genotyping information of 5724 SNPs discovered from 82 known flowering chickpea gene orthologs of Arabidopsis and legumes as well as 832 gene-encoding transcripts that are differentially expressed during flower development in chickpea. GWAS using both genome-wide GBS- and candidate gene-based genotyping data of 30,129 SNPs in a structured population of 92 sequenced accessions (with 200–250 kb LD decay) detected eight maximum effect genomic SNP loci (genes) associated (34 % combined PVE) with flowering time. Six flowering time-associated major genomic loci harbouring five robust QTLs mapped on a high-resolution intra-specific genetic linkage map were validated (11.6–27.3 % PVE at 5.4–11.7 LOD) further by traditional QTL mapping. The flower-specific expression, including differential up- and down-regulation (>three folds) of eight flowering time-associated genes (including six genes validated by QTL mapping) especially in early flowering than late flowering contrasting chickpea accessions/mapping individuals during flower development was evident. The gene haplotype-based LD mapping discovered diverse novel natural allelic variants and haplotypes in eight genes with high trait association potential (41 % combined PVE) for flowering time differentiation in cultivated and wild chickpea. Taken together, eight potential known/candidate flowering time-regulating genes [efl1 (early flowering 1), FLD (Flowering locus D), GI (GIGANTEA), Myb (Myeloblastosis), SFH3 (SEC14-like 3), bZIP (basic-leucine zipper), bHLH (basic helix-loop-helix) and SBP (SQUAMOSA promoter binding protein)], including novel markers, QTLs, alleles and haplotypes delineated by aforesaid genome-wide integrated approach have potential for marker-assisted genetic improvement and unravelling the domestication pattern of flowering time in chickpea