Genotype-phenotype Association Studies Research Articles

Whole-genome Sequencing Association Analysis of Quantitative Platelet Traits in A Large Cohort of β-thalassemia.

Platelet acts as a crucial monitoring indicator for hypercoagulability and thrombosis and a key target for drug regulation. Genotype-phenotype association studies have confirmed that platelet traits are quantitatively regulated by multiple genes. However, there is currently a lack of genetic studies on the heterogeneity of platelet traits in β-thalassemia under hypercoagulable state. Here, we studied the phenotypic heterogeneity of platelet count (PLT) and mean platelet volume (MPV) in 1020 β-thalassemia patients. We further performed a functionally informed whole genome sequencing association analysis of common variants and rare variants (RVs) for PLT and MPV in 916 patients through integrative analysis of whole-genome sequencing data and functional annotation data. Extreme phenotypic heterogeneity of platelet traits was observed in β-thalassemia patients. Additionally, the common variant based gene-level analysis identified the novel gene of RNF144B associated with MPV. The RV analysis identified several novel associations in both coding and noncoding genome, including missense RVs of PPP2R5C associated with PLT and missense RVs of TSSK1B associated with MPV. In conclusion, we performed a comprehensive and systematic whole genome scan of platelet traits in the β-thalassemia cohort, demonstrating the specificity of genetic regulation of platelet traits in the context of β-thalassemia, providing potential targets for intervention.

Genetic variations in HsP90AA1 chaperone gene across diverse Indian native cattle (<i>Bos indicus</i>) breeds adapted to varied agroclimatic regions

Indian zebu cattle during their separate evolution from Bos taurus, have acquired certain changes in a set of genes that regulate the body temperature in response to heat stress. Heat shock proteins (HSPs), the abundant and ubiquitous molecular chaperones play essential role in many cellular biological processes, and are preferentially transcribed in response to heat stress. The objective of this study was to comparatively screen variants/ single nucleotide polymorphisms (SNPs) in the HSP90AA1 gene in cattle breeds, viz., Gir, Kankrej, Ladakhi, Nagori, Nimari, Ongole, Rathi and Sahiwal, adapted to different agroclimatic regions of India including the exotic breed Holstein Friesian and yak (Bos grunniens). Sanger sequencing was carried out in all the translatable exons of the gene. The analysis revealed 6 SNPs – 3 were located in exon 3, 1 SNP each in exon 4 and 6, and another SNP in exon 9. We also identified a novel SNP 2937 (G>T) nucleotide position of exon 9. Interestingly, 2 variants detected at position 1924 (A>G) of exon 3 and 2343 (T>C) of exon 6 with a frequency of 0.9 and 0.73, respectively, were found to be almost fixed in nearly all of the Indian native cattle breeds. These SNPs could potentially be utilized to undertake genotype-phenotype association studies for superior heat stress tolerance trait in Indian native cattle.

Intelligent mutation based evolutionary optimization algorithm for genomics and precision medicine.

In this paper, genomics and precision medicine have witnessed remarkable progress with the advent of high-throughput sequencing technologies and advances in data analytics. However, because of the data's great dimensionality and complexity, the processing and interpretation of large-scale genomic data present major challenges. In order to overcome these difficulties, this research suggests a novel Intelligent Mutation-Based Evolutionary Optimization Algorithm (IMBOA) created particularly for applications in genomics and precision medicine. In the proposed IMBOA, the mutation operator is guided by genome-based information, allowing for the introduction of variants in candidate solutions that are consistent with known biological processes. The algorithm's combination of Differential Evolution with this intelligent mutation mechanism enables effective exploration and exploitation of the solution space. Applying a domain-specific fitness function, the system evaluates potential solutions for each generation based on genomic correctness and fitness. The fitness function directs the search toward ideal solutions that achieve the problem's objectives, while the genome accuracy measure assures that the solutions have physiologically relevant genomic properties. This work demonstrates extensive tests on diverse genomics datasets, including genotype-phenotype association studies and predictive modeling tasks in precision medicine, to verify the accuracy of the proposed approach. The results demonstrate that, in terms of precision, convergence rate, mean error, standard deviation, prediction, and fitness cost of physiologically important genomic biomarkers, the IMBOA consistently outperforms other cutting-edge optimization methods.

Toward Next-Generation Phenomics: Precision Medicine, Spaceflight, Astronaut Omics, and Beyond.

Large investments over many decades in genomics in diverse fields such as precision medicine, plant biology, and recently, in space life science research and astronaut omics were not accompanied by a commensurate focus on high-throughput and granular characterization of phenotypes, thus resulting in a "phenomics lag" in systems science. There are also limits to what can be achieved through increases in sample sizes in genotype-phenotype association studies without commensurate advances in phenomics. These challenges beg a question. What might next-generation phenomics look like, given that the Internet of Things and artificial intelligence offer prospects and challenges for high-throughput digital phenotyping as a key component of next-generation phenomics? While attempting to answer this question, I also reflect on governance of digital technology and next-generation phenomics. I argue that it is timely to broaden the technical discourses through a lens of political theory. In this context, this analysis briefly engages with the recent book "The Earthly Community: Reflections on the Last Utopia," written by the historian and political theorist Achille Mbembe. The question posed by the book, "Will we be able to invent different modes of measuring that might open up the possibility of a different aesthetics, a different politics of inhabiting the Earth, of repairing and sharing the planet?" is directly relevant to healing of human diseases in ways that are cognizant of the interdependency of human and nonhuman animal health, and critical and historically informed governance of digital technologies that promise to benefit next-generation phenomics.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Characterization of microsatellite markers in the coding regions of the Penaeus vannamei genome.

In this study, an extensive analysis of microsatellite markers (Single Tandem Repeats-STRs) in Penaeus vannamei was conducted at an advanced level. The markers were thoroughly examined, characterized, and specific markers located within coding regions were identified. Out of a total of 306 STRs, 117 were classified as perfect markers based on their single repeat motif. Among these perfect markers, 62 were found to be associated with predicted coding genes (mRNA), which were involved in various functions such as binding, catalytic activity, ATP-dependent activity, transcription, structural and molecular regulation. To validate the accuracy of the findings, a sample of nine markers was subjected to in vitro testing, which confirmed the presence of polymorphisms within the population. These results suggest the existence of different protein isoforms within the population, indicating the potential of these markers for application in both population and phenotype-genotype association studies. This innovative approach opens up new possibilities for investigating the impact of genomic plasticity in populations of P. vannamei.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer’s disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer’s disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities

The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype–phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned de novo, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.

A high-resolution genotype–phenotype map identifies the TaSPL17 controlling grain number and size in wheat

BackgroundLarge-scale genotype–phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments.ResultsWe perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection.ConclusionOur study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.

A Chromosome-Level Reference Genome for the Black-Legged Kittiwake (Rissa tridactyla), a Declining Circumpolar Seabird.

Amidst the current biodiversity crisis, the availability of genomic resources for declining species can provide important insights into the factors driving population decline. In the early 1990s, the black-legged kittiwake (Rissa tridactyla), a pelagic gull widely distributed across the arctic, subarctic, and temperate zones, suffered a steep population decline following an abrupt warming of sea surface temperature across its distribution range and is currently listed as Vulnerable by the International Union for the Conservation of Nature. Kittiwakes have long been the focus for field studies of physiology, ecology, and ecotoxicology and are primary indicators of fluctuating ecological conditions in arctic and subarctic marine ecosystems. We present a high-quality chromosome-level reference genome and annotation for the black-legged kittiwake using a combination of Pacific Biosciences HiFi sequencing, Bionano optical maps, Hi-C reads, and RNA-Seq data. The final assembly spans 1.35 Gb across 32 chromosomes, with a scaffold N50 of 88.21 Mb and a BUSCO completeness of 97.4%. This genome assembly substantially improves the quality of a previous draft genome, showing an approximately 5× increase in contiguity and a more complete annotation. Using this new chromosome-level reference genome and three more chromosome-level assemblies of Charadriiformes, we uncover several lineage-specific chromosome fusions and fissions, but find no shared rearrangements, suggesting that interchromosomal rearrangements have been commonplace throughout the diversification of Charadriiformes. This new high-quality genome assembly will enable population genomic, transcriptomic, and phenotype-genotype association studies in a widely studied sentinel species, which may provide important insights into the impacts of global change on marine systems.

De Novo Dissecting the Three-Dimensional Facial Morphology of 2379 Han Chinese Individuals

Phenotypic diversity, especially that of facial morphology, has not been fully investigated in the Han Chinese, which is the largest ethnic group in the world. In this study, we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional (3D) manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals. Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions: Zhengzhou, Taizhou, and Nanning. We identified 1560 shared features from extracted phenotypes, which characterized well the basic facial morphology of the Han Chinese. In particular, heterogeneous phenotypes showing population structures corresponded to geographical subpopulations. The greatest facial variation among these geographical populations was the angle of glabella, left subalare, and right cheilion (p = 3.4 × 10−161). Interestingly, we found that Han Chinese populations could be classified into northern Han, central Han, and southern Han at the phenotypic level, and the facial morphological variation pattern of central Han Chinese was between the typical differentiation of northern and southern Han Chinese. This result was highly consistent with the results revealed by the genetic data. These findings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.

High-throughput calculation of organ-scale traits with reconstructed accurate 3D canopy structures using a UAV RGB camera with an advanced cross-circling oblique route

The measurement of organ-scale traits in large-scale fields remains a bottleneck in genotype-phenotype association studies for crop improvement. To address this issue, an advanced cross-circling oblique (CCO) route was proposed, consisting of multiple single-circle routes. Using multi-view images from lightweight UAVs with the CCO route, 3D crop canopies were reconstructed for maize, cotton, and sugar beet. Organ-scale traits were estimated from the CCO-derived and traditional five-directional oblique (FDO)-derived 3D canopy models and were compared to manual measurements. An algorithm was further proposed to compare the image utilisation efficiency between the CCO route and FDO route with three indicators, including the total utilisation (TU), effective utilisation (EU), and occlusion rate (OR). The results showed that the proposed CCO route can obtain complete canopy structures throughout the growth stages for crops with relatively wide-row planting, such as maize. For densely planted crops like cotton, the full canopy structure at early growth stages (47 d after sowing (DAS)) and the upper parts of the canopy architecture at later growth stages (71 and 90 DAS) could be obtained. The leaf length and width from different layers estimated from CCO-derived models were in good agreement with manual measurements for maize (R2 = 0.93, RMSE = 3.05 cm, nRMSE = 4.7% and R2 = 0.88, RMSE = 0.49 cm, nRMSE = 5.5% for leaf length and leaf width, respectively) and cotton (R2 = 0.81, RMSE = 1.16 cm, nRMSE = 8.6% and R2 = 0.93, RMSE = 0.99 cm, nRMSE = 7.2% for leaf length and leaf width, respectively). CCO-derived 3D canopy models also provided higher accuracy for organ-scale trait estimation than FDO-derived 3D canopy models (R2 of 0.80 versus 0.76 for both leaf length and width estimation, respectively). Regarding image utilisation, the CCO route outperformed the FDO route, with an average value of 124% higher in EU and 11.7% lower in OR, tested on sugar beet varieties with different canopy structures. The proposed CCO route adopts a non-linear mobile route to obtain canopy images from more perspectives, taking into account both the model accuracy and efficiency. Although the flight altitude of the CCO in this study was relatively low (4 m above the canopy), the same model accuracy can be achieved at higher flight altitudes using a higher-quality camera, thus significantly reducing image acquisition time. This study represents the first time that accurate crop structures in large-scale fields were characterised using a method featuring mobility, affordability, throughput, accuracy, and efficiency. The proposed method could provide novel opportunities for accelerating plant breeding and precision agriculture.

RAS pathway: The new frontier of brain mosaicism in epilepsy

As cells divide during development, errors in DNA replication and repair lead to somatic mosaicism – a phenomenon in which different cell lineages harbor unique constellations of genetic variants. Over the past decade, somatic variants that disrupt mTOR signaling, protein glycosylation, and other functions during brain development have been linked to cortical malformations and focal epilepsy. More recently, emerging evidence points to a role for Ras pathway mosaicism in epilepsy. The Ras family of proteins is a critical driver of MAPK signaling. Disruption of the Ras pathway is most known for its association with tumorigenesis; however, developmental disorders known as RASopathies commonly have a neurological component that sometimes includes epilepsy, offering evidence for Ras involvement in brain development and epileptogenesis. Brain somatic variants affecting the Ras pathway (e.g., KRAS, PTPN11, BRAF) are now strongly associated with focal epilepsy through genotype-phenotype association studies as well as mechanistic evidence. This review summarizes the Ras pathway and its involvement in epilepsy and neurodevelopmental disorders, focusing on new evidence regarding Ras pathway mosaicism and the potential future clinical implications.

PRAWNS: Compact pan-genomic features for whole-genome population genomics.

Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly-complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes. We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii. PRAWNS is implemented in C ++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git). Supplementary data are available at Bioinformatics online.

Quantitative Analysis of Isoniazid and Its Four Primary Metabolites in Plasma of Tuberculosis Patients Using LC-MS/MS

Isoniazid and its metabolites are potentially associated with hepatotoxicity and treatment outcomes in patients who receive antituberculosis (TB) therapy. To further understand the pharmacokinetic profiles of these molecules, a method based on LC-MS/MS was developed to determine the concentration of these compounds in human plasma. Isoniazid, acetylisoniazid, and isonicotinic acid were directly analyzed, whereas hydrazine and acetylhydrazine were determined after derivatization using p-tolualdehyde. Chromatographic separation was conducted on reversed-phase C18 columns with gradient elution, and detection was carried out in multiple reaction monitoring mode. The calibration curves were linear with correlation coefficients (r) greater than 0.9947 for all analytes. The intra- and inter-day precision was less than 13.43%, and the accuracy ranged between 91.63 and 114.00%. The recovery and matrix effect of the analytes were also consistent (coefficient of variation was less than 9.36%). The developed method successfully quantified isoniazid and its metabolites in TB patients. The method has broad applications in clinical research, including isoniazid one-point-based therapeutic drug monitoring, genotype-phenotype association studies of isoniazid metabolic profile and isoniazid-induced hepatotoxicity, and the initial dose prediction of isoniazid using population pharmacokinetic modeling.

On the contribution of the rodent model Plasmodium chabaudi for understanding the genetics of drug resistance in malaria.

Malaria is a devastating disease that still claims over half a million lives every year, mostly in sub-Saharan Africa. One of the main barriers to malaria control is the evolution and propagation of drug-resistant mutant parasites. Knowing the genes and respective mutations responsible for drug resistance facilitates the design of drugs with novel modes of action and allows predicting and monitoring drug resistance in natural parasite populations in real-time. The best way to identify these mutations is to experimentally evolve resistance to the drug in question and then comparing the genomes of the drug-resistant mutants to that of the sensitive progenitor parasites. This simple evolutive concept was the starting point for the development of a paradigm over the years, based on the use of the rodent malaria parasite Plasmodium chabaudi to unravel the genetics of drug resistance in malaria. It involves the use of a cloned parasite isolate (P. chabaudi AS) whose genome is well characterized, to artificially select resistance to given drugs through serial passages in mice under slowly increasing drug pressure. The end resulting parasites are cloned and the genetic mutations are then discovered through Linkage Group Selection, a technique conceived by Prof. Richard Carter and his group, and/or Whole Genome Sequencing. The precise role of these mutations can then be interrogated in malaria parasites of humans through allelic replacement experiments and/or genotype-phenotype association studies in natural parasite populations. Using this paradigm, all the mutations underlying resistance to the most important antimalarial drugs were identified, most of which were pioneering and later shown to also play a role in drug resistance in natural infections of human malaria parasites. This supports the use of P. chabaudi a fast-track predictive model to identify candidate genetic markers of resistance to present and future antimalarial drugs and improving our understanding of the biology of resistance.

Common Genetic Variants of Fetal Hemoglobin Modify Hematological Phenotypes in MDS/MPN/Myeloma Patients Receiving Cytotoxic Drugs

Genetic studies identify common variants within the HBS1L-MYB intergenic region (HMIP), BCL11A, and Xmn1-HBG2 as associated with elevated fetal hemoglobin (HbF) levels and other clinically important human hematological traits. Recent studies suggest HbF is a predictor of outcome in MDS/AML patients receiving decitabine. We assessed effects of HbF genetic variants on hematological traits in Myeloproliferative Neoplasm (MPN), Myelodysplastic Syndrome (MDS) and myeloma on HbF-inducing therapy to determine potential for variants predicting treatment response. Seven common HbF variants at HMIP, BCL11A, Xmn1-HGB2 loci were genotyped in 89 patients with MPN on Hydroxyurea (HU), myeloma on Lenalidomide, and MDS on Azacytidine. HbF genetic association was seen with rs9494142 (HMIP) in MPN on HU (p = 0.04) and rs1427407 (BCL11A) in myeloma on Lenalidomide (p = 0.002). HMIP variants rs9494142 and rs6920211 influenced baseline platelets (p = 0.04) and hemoglobin treatment response (p = 0.02). rs1427407 (BCL11A) was significantly associated with increased platelets (p = 0.04) negating thrombocytopenic tendency of Lenalidomide. These HbF variants showed significantly discordant minor allele frequencies in MDS/MPN/myeloma compared to wider European population data. This small study finding together suggest the implication of these variants in treatment response and disease biology in MDS/MPN/myeloma warranting larger prospective genotype-phenotype association studies.

S5.4b Genomic variation structure in clinical isolate collections alters benefits of association screening: a study of Cryptococcus neoformans var. grubii in Colombia

S5.4 Free oral paper session, September 22, 2022, 3:00 PM - 4:30 PMObjectiveRecent genome-wide genotype-phenotype association studies (GWAS) of sets of clinical fungal isolates have reported problems resulting from strong phylogenetic clustering (apparent quasi-clonal population structure, difficulties in localizing association signals on chromosomes). We wished to directly investigate such difficulties, for whole-genome sequence data, and corresponding phenotypic data we had generated.MethodsOur recently released Illumina read sequences (Bioproject PRJNA669191) for 29 clinical Colombian isolate genomes of Cryptococcus neoformans var. grubii (28 VNI and 1 VNII as outgroup)12 were assembled and aligned to the annotated H99 VNI reference genome. Single nucleotide polymorphisms (SNPs) were called for the VNI alignment. MLST types, phenotypes, and source metadata of the same isolates12 were integrated with the genomic data. Analyses were interpreted in the light of published literature.ResultsThe 28 VNI isolates were assigned an order considering MLST classification and whole-genome phylogenetic distances. Most SNPs were biallelic, allowing straightforward barcoding: each of the 28 isolates was given a bar if it had the minor allele. Barcode profiles (SNP state vectors) were found repeated numerous times within genes and across entire chromosomes, indicating that neither fine nor ‘rough’ mapping of associations would succeed, except where flagged genes had already been characterized by relevant molecular biology experiments. The same repetitiveness can allow amplicons of individual MLST loci to tag and inform on variation present in other loci in the genome. The gene for the capsule-associated protein CAP59 and its promoter region illustrate such implications at a small (genic) scale.ConclusionsMost associations of phenotypes with fungal genomes from modestly sampled clinical isolate populations cannot be localized to one or a few regions in a genome. This problem is less restrictive in human GWAS studies or (possibly) in studies of recombining fungi sampled from the environment3. It limits the benefits for molecular etiologic understanding that one might expect from GWAS of isolates obtained in clinical contexts, where key fungal phenotypes can be under stronger selection pressure. Explanatory power may be low if one compares just MLST types or phylogenetic clusters4 or examines only variation within individual MLST types. More informative results may be gained by restricting the genomic search space a priori to a subset of genes that are likely to be related to a phenotype, and then zooming in to individual SNPs that are known/suspected to affect protein structure/function1 or influence transcription/translation of the gene. Where the modes of regulation of a key gene are still not completely characterized despite decades of research, such as in CAP59, the presence of a phenotypically monitored, lone SNP in a strategic position within an almost SNP-less region can give potentially precious hints where otherwise none exist; we illustrate this strategy of SNP-focused local exploration for the promoter/enhancer and currently used MLST amplicon of CAP59.Sources:Vélez N, Vega-Vela N, et al. (2022), J Fungi 8(3):245.Vélez N, Monteoliva L, et al. (2022). J Fungi 8(1):57.Whiston E, Taylor JW (2015). G3 6(2):235-244.Felsenstein J (2008). Am Nat. 171(6):713-725.

Assessing the prevalence of S-shaped root canal and associated genes in humans

BackgroundMultiple signaling molecules have been shown to play crucial roles in dental root development. Therefore, we aimed to investigate the prevalence of S-shaped roots and also to investigate, if single nucleotide polymorphisms (SNPs) in BMP2, BMP4 and SMAD6 are associated with this phenotype in humans. MethodsThis is a cross-sectional phenotype-genotype association study that used radiographs to determine the phenotypes and DNA to investigate SNPs in candidate genes. During the radiographic exam, teeth presenting root canal(s) doubly curved were considered S-shaped roots. SNPs in BMP2 (rs1005464 and rs235768), BMP4 (rs17563) and SMAD6 (rs2119261 and rs3934908) were blindly genotyped by real-time PCR using TaqMan assay. The relative and absolute frequency of S-shaped roots were calculated. Chi-square test was used to compare the genotype distributions between control and S-shaped groups. ResultsAmong the 578 subjects, 61 (10.6 %) presented at least one tooth with an S-shaped root. The most commonly affected type of tooth was the premolar. rs1005464 in BMP2 was statistically associated with an S-shaped root (p = 0.036). rs235768 in BMP2 was associated with an S-shaped root also in mandibular teeth (p = 0.017). A statistical significance was observed for the rs3934908 in SMAD6 (p = 0.049) for S-shaped root in the mandible. In the analysis stratified according to the type of tooth, rs235768 in BMP2 was associated with S-shaped roots in premolars (p = 0.029). ConclusionThe prevalence of S-shaped roots is 10.6 % in permanent teeth. SNPs in BMP2 and SMAD6 could be involved in a higher chance to present S-shaped roots.

Genetic associations with immune-mediated outcomes after allogeneic hematopoietic cell transplantation

AbstractPrevious studies have identified more than 200 genetic variants associated with acute or chronic graft-versus-host disease (aGVHD; cGVHD) or recurrent malignancy after allogeneic hematopoietic cell transplantation (HCT). We tested these candidate donor and recipient variants in a cohort of 4270 HCT recipients of European ancestry and in subcohorts of 1827 sibling and 1447 unrelated recipients who had 10/10 HLA-A, B, C, DRB1, and DQB1-matched donors. We also carried out a genome-wide association study (GWAS) for these same outcomes. The discovery and replication analysis of candidate variants identified a group of closely linked recipient HLA-DPB1 single-nucleotide polymorphisms (SNPs) associated with an increased risk of aGVHD and a corresponding decreased risk of recurrent malignancy after unrelated HCT. These results reflect a correlation with the level of HLA-DPB1 expression previously shown to affect the risks of aGVHD and relapse in unrelated recipients. Our GWAS identified an association of cGVHD with a locus of X-linked recipient intron variants in NHS, a gene that regulates actin remodeling and cell morphology. Evaluation of this association in a second replication cohort did not confirm the original replication results, and we did not reach any definitive conclusion regarding the validity of this discovery. The cohort used for our study is larger than those used in most previous HCT studies but is smaller than those typically used for other genotype-phenotype association studies. Genomic and disease data from our study are available for further analysis in combination with data from other cohorts.