Biology subjects

Farmer, A.

Publications and source records attributed to Farmer, A..

17 recordsLinked to original sources

bioRxiv2026

Novel quantitative trait loci conferring broad-based resistance to root-knot nematodes in lima bean (Phaseolus lunatus)

Lima bean (Phaseolus lunatus) is a broadly adapted, economically important leguminous crop and a susceptible host of root-knot nematodes (Meloidogyne spp.; RKN), which are a devastating plant pathogen in agricultural systems worldwide. To date, there have been few studies to elucidate the genetic determinants of RKN resistance in lima beans. Understanding the genetic mechanisms underlying resistance is essential for improving resistance traits and incorporating them into lima bean breeding programs. To assist in marker-assisted selection, we aimed to identify and map quantitative trait loci (QTLs) conferring RKN resistance-related traits. Three recombinant inbred line (RIL) populations were used in this study. Three populations were derived by crossing two RKN-resistant parents with the same RKN-susceptible parent and with each other. All populations were genotyped using genome-wide single-nucleotide polymorphism (SNP) markers. Each population was screened for root galling (RG) and RKN egg reproduction (ER) in response to M. incognita and M. javanica in greenhouse experiments. Three major QTLs were detected and mapped on chromosome Pl04 (QRk-pl04.1), Pl05 (QRk-pl05.1) and Pl10 (QRk-pl10.1) across populations. Among them, QRk-pl05.1 and QRk-pl10.1 affected levels of RG and ER of both RKN species, while QRk-pl04.1 suppressed root galling and reproduction responses of M. incognita but not of M. javanica. These chromosomal regions defined by flanking markers will help guide marker-assisted breeding and gene discovery for broad-based RKN resistance in lima beans.

plant biology↗

bioRxiv2026

Haplotype-resolved genome of autotetraploid alfalfa (Medicago sativa) Regen-SY27x uncovers large scale structural variation and resistance gene dynamics

Polyploid genome assembly presents unique challenges due to extensive heterozygosity and complex haplotype structure. We report a haplotype-resolved, chromosome-scale assembly of Regen-SY27x, a genotype of autotetraploid alfalfa (Medicago sativa), which is widely used for genetic modification because of its excellent regenerative capacity in tissue culture. Using PacBio HiFi long reads, Omni-C scaffolding, and linkage map guided phasing, we generated a 3.2 GB assembly comprising four haplotypes with high contiguity and completeness. Kmer-based validation confirmed accurate haplotype separation, while linkage map integration and dotplot analysis identified and corrected chimeric scaffolds. Gene annotation yielded 221,688 protein-coding genes, with more than 99% assigned to pseudochromosomes. Repetitive elements accounted for 62.7% of the genome, dominated by long terminal repeat retrotransposons and a high fraction of Helitrons. The spatial enrichment of Helitrons within gene-dense distal chromosome arms underscores their pivotal role as key drivers of genomic innovation and gene family expansion. We identified 3,696 nucleotide-binding leucine-rich repeat R genes, with Toll/interleukin-1 receptor-like and Rx-type subclasses forming large tandem clusters across haplotypes. Comparative analyses revealed strong macrosyntenic conservation among Regen-SY27x and the publicly available Chinese alfalfa genomes but extensive structural variation both within Regen-SY27x haplotypes and between Regen-SY27x and the Chinese genotypes with tens of thousands of duplications, inversions, and translocations detected. These results demonstrate that a single autotetraploid individual captures extensive structural diversity, but individuals from different populations vary greatly. The Regen-SY27x assembly provides a foundational genomic resource for investigating polyploid genome evolution and identifying genetic variation relevant to biological and agronomic improvement in alfalfa. Article SummaryThis study presents the first chromosome-scale, haplotype-resolved genome assembly of the US alfalfa germplasm, Regen-SY27x, a key alfalfa genotype used widely for genetic engineering. We integrated HiFi long reads, Omni-CTM scaffolding, and linkage map-guided phasing to reconstruct all four haplotypes of this complex autotetraploid. Our results identified 221,688 protein-coding genes and reveal immense intra-individual structural variations dominated by small duplications. This high-quality reference serves as a foundational tool for the alfalfa community, enabling researchers to link complex structural diversity with agronomic traits and further enhance the biotechnological potential of this essential forage crop.

genomics↗

bioRxiv2025

A domestication change at PvMYB26 in common bean sheds light on the origins of Middle American agriculture

Domestication imposed radically different selection pressures on plants, eventually transforming them into the cultivated forms that support global populations today. Here, we investigate the loss of seed dispersal via pod shattering during common bean (Phaseolus vulgaris L.) domestication. We identified an eight-kb deletion eliminating the transcription start site and promoter of the candidate gene PvMYB26. Mutants express PvMYB26 at < 1% of the level of wild types and produce 44% less pod lignin. Whole-genome sequencing revealed that the mutation is nearly diagnostic for domestication status among Middle American common bean, indicating its importance in domestication. We also identified a high-frequency PvMYB26 frameshift/premature stop mutation unique to Andean domesticates. Wild haplotypes most like Middle American domesticates are found in eastern Jalisco, Mexico. Our results suggest that West-Central Mexico was the site of common bean domestication and suggest that this region may have been important in the rise of Middle American agriculture.

evolutionary biology↗

bioRxiv2024

The topography of gene tree topology space in a plant genus with a legacy of recent polyploidy and introgression

The eukaryotic genome has been described as a collection of different histories; for any set of taxa one of these histories is the record of cladogenic events that together comprise the species tree. Among the other histories expected to occur are those attributable to deep coalescence/lineage sorting; to biological causes such as introgression and horizontal transfer; or to pseudo-orthology, long branch attraction, and other technical issues. Gene tree topology space is the portion of tree space occupied by the gene trees reconstructed for a particular dataset of sampled genetic loci. Because coalescent theory predicts that the species tree topology will generally be the most frequent among gene trees, a reasonable expectation is that there will be a peak in gene tree topology space at the species tree topology, with secondary peaks present due to trees tracking other histories. Gene tree topology space in the small ([~]30 species, including the cultivated soybean) legume genus, Glycine should not only have signals from the species tree and from lineage sorting, but also from a likely introgression event that created incongruence between the plastid and nuclear genomes. Additionally, Glycine is the product of a relatively recent (<13 million years) whole genome duplication, raising the possibility of pseudo-orthology. We explored this space using a set of 2389 nuclear genes and representative accessions from a 570-taxon concatenation tree, reconstructing gene trees for all nuclear loci and from complete plastid genomes and partial mitochondrial genomes. Species trees (ASTRAL) and maximum likelihood (ML) concatenation trees were congruent for a 61-taxon dataset but were incongruent with organellar genome trees. Gene tree topology space was flat: No topology was represented by more than one gene tree. This was also true for a reduced dataset of 27 taxa; only when the dataset was reduced to six ingroup taxa were multiple gene trees having the species tree topology observed, along with a topology congruent with the chloroplast genome topology, presumably representing nuclear loci introgressed along with the plastome. Clustering failed to identify any regional differentiation of gene tree topology space populated by loci with similar topologies. Pseudo-orthology did not contribute meaningfully to incongruence, in agreement with recent modeling work that minimizes concerns about this phenomenon. Clearly, different genes have different historical signals, but these signals are complex and exist at the level of clades within trees rather than as entire gene trees.

evolutionary biology↗

bioRxiv2024

A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically. In this study, we benchmarked five commonly used scCNV inference methods, HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms derived data from a multicenter study. We further evaluated the scCNV performance using scRNA-seq datasets derived from mixed samples consisting of five human lung adenocarcinoma cell lines and generated a clinical scRNA-seq dataset from a human small cell lung cancer patient to validate our findings. Our evaluation criteria included sensitivity and specificity of CNV detection, and subclone identification from mixed cancer samples. We found that the sensitivity and specificity of the five scCNV inference methods varied, depending on the selection of reference data, sequencing depths, and read lengths. Overall, CopyKAT and CaSpER exhibited superior performance to other methods, while inferCNV, sciCNV, and CopyKAT outperformed other methods in subclone identification accuracy. Remarkably, inferCNV achieved high accuracy in subclone identification when using data from a "single scRNA-seq protocol", however, when applying these methods to a dataset derived from multiple scRNA-seq platforms from the mixed samples, we found that batch effects significantly affected the performance of subclone identification for most methods, except for HoneyBADGER. Our benchmarking study revealed the strengths and weaknesses of each of the five scCNV inference methods and provided guidance for selecting the optimal CNV inference method using scRNA-seq data.

bioinformatics↗

bioRxiv2024

Epigenomic, transcriptomic and proteomic characterizations of reference samples

A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced a large amount of different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structural variations (SVs). However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of the epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform. Protein expression of the transcripts overlapping CGIs were much higher than the protein expression of the non-CGI transcripts in both cell lines. We further demonstrated the evidence that certain SNVs were incorporated into mutated peptides. We observed that open chromatin regions had low methylation which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expression. The CG-poor regions had higher repressive epigenetic regulations (higher DNA methylation) and less open chromatin, resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.

genomics↗

bioRxiv2023

Sequence characterization of T, Bip, and Phbw demonstrates the role of MYB-bHLH-WD40 complexes and temperature in common bean seed color pattern formation

Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also critical for determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for three important seed pattern loci, T, Bip, and phbw, which co-segregated completely with PvTTG1, PvMYC1, and PvTT8, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat. Whole-genome sequencing of 37 diverse accessions identified putative mutations in each gene, including seven unique parallel mutations in T (PvTTG1) and a non-synonymous SNP in a conserved residue in bipana (PvMYC1). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the origins of the Papilionoidea and corresponded to a 20-fold reduction in transcript abundance. Mutations in MBW candidate genes for Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials with seven varieties with partial seed coat patterning, pigmented seed coat area was highly unstable and correlated with temperature, with up to 11-fold differences in pigmented area between the warmest and the coolest environments. In controlled growth chamber conditions, an increase of 4 {degrees}C was sufficient to cause pigmentation on an additional 21% of the seed coat area. Our results shed light on the fundamental activation of flavonoid biosynthesis in common bean and will be instrumental for maximizing consumer appeal in this nutritious staple crop. Summary- Seed colors and patterns are critical for the survival of wild plants, and are important in differentiating crop market classes, but the genetic control of these in the staple crop common bean (Phaseolus vulgaris) is largely unknown. - The genetic, transcriptional, and environmental basis of common bean seed color patterning was explored through QTL mapping, whole-genome sequencing, RT-qPCR, and automated pigmentation quantification of seed grown in multi-location field trials and growth chamber environments. - MYB-bHLH-WD40 complex-forming genes PvTTG1, PvMYC1, and PvTT8 co-segregated completely with the color patterning genes T, Bip, and phbw. Mutations were identified in each gene, including seven unique parallel mutations in T (PvTTG1), a non-synonymous SNP in a conserved residue in bipana (PvMYC1), and an intron deletion in phbw (PvTT8) eliminating highly conserved motifs and corresponding to 20-fold lower PvTT8 transcript abundance. Mutations in MBW candidate genes Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials, pigmented seed coat area was highly unstable and corresponded to temperature. In growth chamber conditions, an increase of 4 {degrees}C caused pigmentation on an additional 21% of the seed coat area. - Our results highlight the critical interaction between MYB-bHLH-WD40 complex components and temperature in establishing seed pattern diversity.

plant biology↗

bioRxiv2021

Human Pre-Implantation Embryos Are Permissive to SARS-CoV-2 Entry

The spread of SARS-CoV-2 has led to a devastating pandemic, with infections resulting in a range of symptoms collectively known as COVID-19. The full repertoire of human tissues and organs susceptible to infection is an area of active investigation, and some studies have implicated the reproductive system. The effects of COVID-19 on human reproduction remain poorly understood, and particularly the impact on early embryogenesis and establishment of a pregnancy are not known. In this work, we explore the susceptibility of early human embryos to SARS-CoV-2 infection. We note that ACE2 and TMPRSS2, two canonical cell entry factors for SARS-CoV-2, are co-expressed in cells of the trophectoderm in blastocyst-stage preimplantation embryos. Using fluorescent reporter virions pseudotyped with Spike (S) glycoprotein from SARS-CoV-2, we observe robust infection of trophectoderm cells, and this permissiveness could be attenuated with blocking antibodies targeting S or ACE2. When exposing human blastocysts to the live, fully infectious SARS-CoV-2, we detected cases of infection that compromised embryo health. Therefore, we identify a new human target tissue for SARS-CoV-2 with potential medical implications for reproductive health during the COVID-19 pandemic and its aftermath.

developmental biology↗

bioRxiv2020

Genetics of nodulation in Aeschynomene evenia uncovers new mechanisms of the rhizobium-legume symbiosis

Among legumes (Fabaceae) capable of nitrogen-fixing nodulation, several Aeschynomene spp. use a unique symbiotic process that is independent of Nod factors and infection threads. They are also distinctive in developing root and stem nodules with photosynthetic bradyrhizobia. Despite the significance of these symbiotic features, their understanding remains limited. To overcome such limitations, we conducted genetic studies of nodulation in Aeschynomene evenia, supported by the development of a genome sequence for A. evenia and transcriptomic resources for 10 additional Aeschynomene spp. Comparative analysis of symbiotic genes substantiated singular mechanisms in the early and late nodulation steps. A forward genetic screen also showed that AeCRK, coding a novel receptor-like kinase, and the symbiotic signaling genes AePOLLUX, AeCCamK, AeCYCLOPS, AeNSP2 and AeNIN, are required to trigger both root and stem nodulation. This work demonstrates the utility of the A. evenia model and provides a cornerstone to unravel new mechanisms underlying the rhizobium-legume symbiosis.

plant biology↗

bioRxiv2020

A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

bioinformatics↗

bioRxiv2020

The impact of chromatin remodeling on gene expression at the single cell level in Arabidopsis thaliana

Similar to other complex organisms, plants consist of diverse and highly specialized cell types. The gain of unique biological functions of these different cell types is the consequence of the establishment of cell-type-specific transcriptional programs and their associated regulatory mechanisms. Recently, single cell transcriptomic approaches have been applied on Arabidopsis thaliana root protoplasts allowing the accurate characterization of the transcriptional profiles of the cell-types composing seedling roots. As a first step in gaining a deeper understanding of the regulatory mechanisms controlling Arabidopsis gene expression, we report the use of single nucleus RNA sequencing (sNucRNA-seq) and single nucleus Assay for Transposase Accessible Chromatin sequencing (sNucATAC-seq) technologies on Arabidopsis roots. The comparison of our single nuclei transcriptomes to previously published protoplast transcriptomes validated the use of nuclei as biological entities to establish cell-type specific transcriptomes from multicellular organs. Furthermore, our sNucRNA-seq results uncovered the transcriptome of additional cell subtypes not identified by scRNA-seq. Similar to our transcriptomic approach, the sNucATAC-seq approach led to the distribution of the Arabidopsis nuclei into distinct clusters suggesting the differential remodeling of the chromatin between groups of cells according to their identity. To reveal the impact of chromatin remodeling on gene transcription, we integrated sNucRNA-seq and sNucATAC-seq data and demonstrated that cell-type-specific marker genes also display cell-type-specific pattern of chromatin accessibility. Our data suggest that the differential remodeling of the chromatin is a critical mechanism to regulate gene activity at the cell-type level.

plant biology↗

bioRxiv2020

A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

Single-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithms logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

genomics↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗

bioRxiv2018

High-throughput chromatin accessibility profiling at single-cell resolution

We have developed a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4-5 hour on-chip processing time) and cost (~98{cents} per cell) compared to prior microfluidic implementations. We applied this method to measure regulatory variation in Peripheral Blood Mononuclear Cells (PBMCs) and show robust, de-novo clustering of single cells by hematopoietic cell type.

genomics↗

bioRxiv2018

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects.\n\nCYP2C19 phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was percentage symptom improvement and remission. Side effect data were available at weeks 2-4, 6 and 9 in three of the investigated samples. A fixed-effects meta-analysis was performed using EM as the reference group.\n\nAnalysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD=0.43, CI=0.19-0.66) and higher remission rates (OR=1.55, CI=1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR=1.26, CI=1.08-1.47), neurological (OR=1.28, CI=1.07-1.53) and sexual side effects (OR=1.52, CI=1.23-1.87; week 6 values similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups.\n\nCYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs are relatively rare among Caucasians (~2%).

genetics↗

bioRxiv2017

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.

cell biology↗

bioRxiv2017

New insights on the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation.\n\nA meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at SNP, gene and pathway level. Coverage of genetic variants was increased compared to previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) served for replication.\n\n7,062,950 SNPs were analysed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (p=1.80e-08, ITGA9 (integrin alpha 9)) and rs76191705 (p=2.59e-08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At gene level, no consistent effect was found. At pathway level, the Gene Ontology terms GO:0005694 (chromosome) and GO:0044427 (chromosomal part) were associated with improvement (corrected p=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (p=0.047), while rs76191705 was not. The two SNPs did not replicate in NEWMEDS.\n\nITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, no convincing replication was achieved. Further studies may help in clarifying their role.

genomics↗