Genotype Information Research Articles

Maternal Education Potentially Moderates the MAOA uVNTR Effects on Externalizing Behavior in Black South African Children

Interactions between the MAOA uVNTR and rearing environment are suggested to influence the developmental manifestations of childhood internalizing and externalizing behavior. However, few studies in the MAOA literature have included continental African children, or focused on non-clinical samples. We explored the main and interactive effects of the MAOA uVNTR (high and low activity alleles) in Black South African male (n = 478) and female (n = 540) children who were part of the longitudinal Birth to Twenty Plus cohort. Historical data on birth weight, gestational age at delivery, socioeconomic status, and maternal education were combined with genotypic information and analyzed using regression modeling. We found no significant main effects for the MAOA uVNTR on childhood behavior in either sex. A significant interaction (p = .04) was identified between MAOA and maternal education, suggesting that externalizing behavior in boys carrying a low activity MAOA allele varied in direct proportion to the education levels of their mothers. However, the model fit failed to reach significance, possibly due to our inclusion of only non-clinical pre-pubertal males. No significant interactions were detected for female children. Our findings lend tentative credibility to the Environmental Sensitivity metaframework, which suggests that MAOA is an important plasticity factor in childhood development.

Circulating Tumor DNA in Lymphoma.

Recent advances have been made in circulating tumor DNA (ctDNA), the method to minimally invasive detect lymphoma sensitively with tumor-derived DNA in the blood of patients with lymphomas. This article discusses these various methods of ctDNA detection and the clinical context in which they have been applied to for a variety of lymphoma subtypes. ctDNA has been applied to a variety of subtypes of lymphoma and has been used in the context of genotyping somatic mutations and classification of disease, monitoring of response during treatment, detecting minimal residual disease even with radiographic remission, and predicting relapse and long-term survival outcomes. There are a variety of techniques used to measure ctDNA including digital polymerase chain reaction and next-generation sequencing techniques including high-throughput variable-diversity-joining rearrangement sequencing, high-throughput sequencing of somatic mutations, and Cancer Personalized Profiling by deep sequencing. While the greatest data has been generated in diffuse large B cell lymphoma, there have been studies utilizing application of ctDNA in follicular lymphoma, mantle cell lymphoma, Hodgkin's lymphoma, peripheral T cell lymphoma, and primary CNS lymphoma among others. ctDNA is an emerging biomarker in lymphoma that can minimally invasively provide further genotypic information, diagnostic clarification, and treatment prognostication by detection of minimal residual disease even without radiographic evidence of disease. Future studies are needed to standardize the use of ctDNA and translate its use clinically for the management of lymphoma patients.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms.

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

How many fathers? Study design implications when inferring multiple paternity in crocodilians.

Up to 10 males were reported to sire clutches of crocodilian eggs but review of the underlying study designs raised questions of potential upward bias of inferred sire numbers. To test this premise, different scenarios were explored using a published dataset of 16 known single‐sire saltwater crocodile pairs and their offspring which were originally confirmed using a 11 loci microsatellite panel in CERVUS. Varying the number of microsatellites, omitting one or both parental genotypes and using different parentage analysis techniques revealed that total allele number, rather than number of loci, determined inferred sire accuracy in two opposing ways. Using the single‐locus minimum method and GERUD, which both require prior knowledge of family groupings (i.e., nests), fewer alleles (and loci) accurately inferred only one father. In contrast, CERVUS and COLONY required all 11 loci (65 alleles) and both parental genotypes to (a) assign correct family groups and (b) infer the correct sire number, except in one family where two sires were equally assigned based on their number of homozygous loci. When less genotype information was provided, CERVUS and COLONY inferred up to six and seven sires, respectively. Given this data is from confirmed single‐sire matings, and yet up to seven sires could be inferred, the significance of inappropriate study design is clearly demonstrated. Consideration should be carefully given to genotype data, particularly those collected specifically for population diversity studies, which are also used to infer multiple paternity because the underlying data collection assumptions are not equivalent between the two outcomes.

VP.03 Diagnosing Titinopathy: lessons from a multi-omics pilot study

Titinopathy, or skeletal muscular dystrophy caused by autosomal recessive mutations in Titin (<i>TTN</i>), poses a unique diagnostic challenge because of A) significant variation in clinical manifestation, B) presence of rare putative deleterious <i>TTN</i> variants amongst healthy individuals which increases likelihood of incidental findings. Altogether, this makes establishing a definite diagnosis of Titinopathy based solely on phenotypic and genotypic information virtually impossible in the absence of corroborative evidence. We report six patients with Titinopathy from four unrelated families. In each case, prior genomics-based efforts had failed to reach a conclusive diagnosis and a confirmed diagnosis was reached by co-analyzing patient genomic and transcriptomic data. The multi-omics approach provided direct evidence confirming or dismissing impact of plausible candidate variants in TTN and other genes. This clarity of information was critical in solving these cases. The phenotypic diversity of the cohort highlights the diagnostic challenge. The first family included two siblings presented with adolescent onset multiple joint contractures, slowly progressive spine rigidity and elevated Creatine Kinase (CK). The severity of condition varied amongst the siblings with elder brother exhibited mild limb girdle weakness not seen in the younger sister. The second and third families comprise of a girl and boy respectively. Both unrelated patients had similar symptoms characterized by early onset hypotonia, delayed walking and elevated CK. Both had limb girdle muscle weakness and mild scoliosis. Their motor performance had been stable, and both continued to walk independently in adulthood. The fourth family included two siblings presented with arthrogryposis multiplex congenita and hypotonia after birth. Their mother reported decreased fetal movement during both pregnancies. Both elder sister and younger brother had delayed walking and limb girdle weakness. However, their joint contractures improved with age and regular stretching. The younger brother also had autistic features. Both siblings had normal CK. All patients in the study had normal echocardiogram suggesting lack of cardiac involvement. Our multi-omics findings link individualized patient condition to the specific functional impact of their implicated mutations.

Review on trends of Selection superior Dairy cattle through marker assisted selection methods

Marker-Assisted Selection is selection that used for indirect selection of superior breeding animals that depend on identifying association between genetic marker and linked quantitative traits loci. Since the association between marker and quantitative traits loci depends on distance between marker and target traits. As soon as markers linked to quantitative traits loci have been identified, they can be used in selection programme of dairy cattle that is beneficial when the traits are difficult and expensive to measure and low heritability and recessive traits in dairy cattle. Therefore, Marker-Assisted Selection facilitate the exploitation of existing genetic diversity in breeding populations and can be used to improve desirable traits in dairy cattle. Indeed, currently Marker-Assisted Selection is the most widely used application of marker systems in selection of dairy breeding. Quantitative traits loci affecting milk yield has been identified on 20 of the 29 bovine chromosomes. quantitative traits loci for milk yield on chromosome 1, 3, 9 and 20 are with evidence of quantitative traits loci at lower reported frequency on other chromosomes (5, 7, 10, 12, 14 17, 18, 21, 23, 27 and 29). However, the main limitation of marker-assisted selection is increased cost involved in sample collection for genotyping and complete genotype information. Hence, this paper was aimed to review trends on selection of dairy cattle based on marker assisted selection approach.

A literature embedding model for cardiovascular disease prediction using risk factors, symptoms, and genotype information

Accurate prediction of cardiovascular disease (CVD) requires multifaceted information consisting of not only a patient’s medical history, but genomic data, symptoms, lifestyle, and risk factors which are often not incorporated into a decision making process as the data are vast, difficult to obtain, and require complex algorithms. However, given the vast amount of publicly available information and recent advances in machine and deep learning techniques, acquiring multifaceted data for accurate diagnosis and medical decision making via artificial intelligence has become more attainable. In this work we illustrate a literature embedding model, which identifies various risk factors, symptoms, mechanisms, and genes associated with CVD using a query word (i.e., “stroke”), and then based on the collected information the model predicts whether or not a person will be susceptible to CVD. For this purpose, we collected published literature from PubMed using search keywords consisting of a word such as “heart” and 19,264 human gene names, then trained our literature embedding model using the collected abstracts. For the intrinsic evaluation, we analyzed whether or not the captured words and genes were correctly identified as risk factors and associated symptoms for the input query words. For the extrinsic evaluation, we used our embedding model as feature selection and dimensionality reduction tasks on cohort data for CVD prediction. Our model accurately (average accuracy of >96 %) captured associated risk factors, symptoms, and genes for a given input query word (intrinsic evaluation). Using the selected features and reduced dimensions, our method provided better performance for CVD prediction with less computational time when compared with other popular methods (extrinsic evaluation). Our model provided outstanding results for both evaluation tasks, which enables accurate risk factor identification for CVD prediction. Our model has the potential to facilitate easier collation of multifaceted information for better data mining of vast publicly available data so that efficient and accurate risk factors and symptoms can be identified, which enables better-informed decisions for CVD prediction and treatment.

Multiple gene variants linked to Alzheimer's-type clinical dementia via GWAS are also associated with non-Alzheimer's neuropathologic entities

The classic pathologic hallmarks of Alzheimer's disease (AD) are amyloid plaques and neurofibrillary tangles (AD neuropathologic changes, or ADNC). However, brains from individuals clinically diagnosed with “AD-type” (amnestic) dementia usually harbor heterogeneous neuropathologies in addition to, or other than, ADNC. We hypothesized that some AD-type dementia associated genetic single nucleotide variants (SNVs) identified from large genomewide association studies (GWAS) were associated with non-ADNC neuropathologies. To test this hypothesis, we analyzed data from multiple studies with available genotype and neuropathologic phenotype information. Clinical AD/dementia risk alleles of interest were derived from the very large GWAS by Bellenguez et al. (2022) who reported 83 clinical AD/dementia-linked SNVs in addition to the APOE risk alleles. To query the pathologic phenotypes associated with variation of those SNVs, National Alzheimer's disease Coordinating Center (NACC) neuropathologic data were linked to AD Sequencing Project (ADSP) and AD Genomics Consortium (ADGC) data. Separate data were obtained from the harmonized Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). A total of 4811 European participants had at least ADNC neuropathology data and also genotype data available; data were meta-analyzed across cohorts. As expected, a subset of dementia-associated SNVs were associated with ADNC risk in Europeans—e.g., BIN1, PICALM, CR1, MME, and COX7C. Other gene variants linked to (clinical) AD dementia were associated with non-ADNC pathologies. For example, the associations of GRN and TMEM106B SNVs with limbic-predominant age-related TDP-43 neuropathologic changes (LATE-NC) were replicated. In addition, SNVs in TNIP1 and WNT3 previously reported as AD-related were instead associated with hippocampal sclerosis pathology. Some genotype/neuropathology association trends were not statistically significant at P < 0.05 after correcting for multiple testing, but were intriguing. For example, variants in SORL1 and TPCN1 showed trends for association with LATE-NC whereas Lewy body pathology trended toward association with USP6NL and BIN1 gene variants. A smaller cohort of non-European subjects (n = 273, approximately one-half of whom were African-Americans) provided the basis for additional exploratory analyses. Overall, these findings were consistent with the hypothesis that some genetic variants linked to AD dementia risk exert their affect by influencing non-ADNC neuropathologies.

Buprenorphine Initiation in the Era of High-potency Synthetic Opioids: A Call for Community-based Participatory Research to Help Learning Health Systems Provide Precision Medicine for Opioid Use Disorder.

High-potency synthetic opioids and adulterants have saturated the US opioid supply, heightening risk to people who use drugs and creating challenges to buprenorphine initiation. As buprenorphine is an essential component of a response to the opioid crisis, a robust evidence base is urgently needed. Rapid research, in partnership with people who use drugs and using community-based participatory research practices to ensure trust, shared goals, and an absence of adverse consequences, should be conducted to help identify the factors associated with successful and failed inductions. Among factors to be considered include detailed assessment of substances used, amount of substances used, time between last use and buprenorphine induction, withdrawal severity at time of buprenorphine induction, dose of buprenorphine, impact of buprenorphine on withdrawal severity, and perhaps even genotypic information. Successful conduct of these efforts in learning health systems can help us move toward precision medicine for opioid use disorder.

Association analyses of rare variants identify two genes associated with refractive error.

PurposeGenetic variants identified through population-based genome-wide studies are generally of high frequency, exerting their action in the central part of the refractive error spectrum. However, the power to identify associations with variants of lower minor allele frequency is greatly reduced, requiring considerable sample sizes. Here we aim to assess the impact of rare variants on genetic variation of refractive errors in a very large general population cohort.MethodsGenetic association analyses of non-cyclopaedic autorefraction calculated as mean spherical equivalent (SPHE) used whole-exome sequence genotypic information from 50,893 unrelated participants in the UK Biobank of European ancestry. Gene-based analyses tested for association with SPHE using an optimised SNP-set kernel association test (SKAT-O) restricted to rare variants (minor allele frequency < 1%) within protein-coding regions of the genome. All models were adjusted for age, sex and common lead variants within the same locus reported by previous genome-wide association studies. Potentially causal markers driving association at significant loci were elucidated using sensitivity analyses by sequentially dropping the most associated variants from gene-based analyses.ResultsWe found strong statistical evidence for association of SPHE with the SIX6 (p-value = 2.15 x 10−10, or Bonferroni-Corrected p = 4.41x10-06) and the CRX gene (p-value = 6.65 x 10−08, or Bonferroni-Corrected p = 0.001). The SIX6 gene codes for a transcription factor believed to be critical to the eye, retina and optic disc development and morphology, while CRX regulates photoreceptor specification and expression of over 700 genes in the retina. These novel associations suggest an important role of genes involved in eye morphogenesis in refractive error.ConclusionThe results of our study support previous research highlighting the importance of rare variants to the genetic risk of refractive error. We explain some of the origins of the genetic signals seen in GWAS but also report for the first time a completely novel association with the CRX gene.

Genetic diversity and population structure of locally adapted Brazilian horse breeds assessed using genome-wide single nucleotide polymorphisms

Animals first introduced in Brazil with the discovery were submitted to various selective pressures over five centuries, developing adaptive traits specific to the hostile environments found in the country. A high quantity of genotypic information can be generated quickly and with high repeatability and reliability using genome-wide Single Nucleotide Polymorphism (SNP) panels. Blood samples from 285 horses belonging to eight Brazilian breeds (Baixadeiro, Lavradeiro, Marajoara, Puruca, Campeiro, Crioulo, Mangalarga Marchador and Pantaneiro) and two commercial breeds sampled in Brazil (Arabian and English Thoroughbred) were genotyped focusing on elucidating the genetic structure and composition within and among Brazilian horse breeds and some of their possible founders. Data from the GGP Equine BeadChip (GeneSeek® Genomic Profiler™ Equine, 65.157 SNPs) was used to assess basic diversity parameters, genetic distance (pairwise FST and Nei), Principal Component Analysis (PCA), Analysis of Molecular Variance (AMOVA) and population structure (ADMIXTURE). Results showed that Crioulo (few animals), Pantaneiro, Mangalarga Marchador, and Campeiro breeds possess distinct genetic bases from the others and each other. The Lavradeiro, Baixadeiro, Puruca, and Marajoara breeds have a shared genetic basis, hindering their differentiation. The results also reflected a geographic division of the Brazilian equine breeds, with those originally from the North and Northeast regions (Lavradeiro, Marajora, Puruca and Baixadeiro) grouped. On the other hand, animals from the South and Southeast regions (Crioulo, Campeiro and Mangalarga Marchador) were genetically closer although distinguished. The Pantaneiro breed originating from Pantanal Matogrossense in the Midwest region of Brazil, separated from all the others. Overall, high values of genetic variability and low values of FIS were observed for the Brazilian naturalized breeds. Both Arabian and English Thoroughbred sampled in Brazil presented a similar genetic variability to the Brazilian breeds but were genetically differentiated. This study aims to understand better the genetic variability and relation among Brazilian naturalized horse breeds and their possible origins. This information helps establish and maintain conservation nuclei and programs for each horse breed. Results found here can be used as a base for backup decisions regarding management to optimize conservation programs and other support sampling strategies for the National Animal Germplasm Program.

Cobas HPV Genotyping of FNA Sample Supernatant and Frozen Section Scrapings of Suspected Head and Neck Cancer.

The human papillomavirus (HPV) status of squamous cell carcinomas (SCCs) of the head and neck is relevant for therapy planning, staging, and follow-up. Immunohistochemistry (IHC) for p16 is a surrogate marker of HPV status in oropharyngeal SCC, but not at other head and neck sites. We tested if the cobas HPV test was feasible and superior to p16-IHC on fine-needle aspiration (FNA) supernatants and frozen section (FS) scrapings of suspected SCC. A 500μL aliquots of postcentrifugation supernatant CytoRich Red media of FNA cellblock specimens and scrapings of FS suspended in SurePath media vials were tested with the cobas HPV test and compared with p16-IHC and/or HPV in situ hybridization (ISH) performed on cellblock and/or resections. Twenty-nine (n=29) FNAs were tested for a cobas HPV test, p16, and/or HPV-ISH. The mean collection to testing time was 6.3 days (range: 0 to 24d). Cobas yielded valid results in all cases; p16-IHC could not be interpreted in 4 (13%) cellblocks; correlation was performed on subsequent resections. Cohen κ correlation for cobas versus p16-IHC/HPV-ISH on FNA samples was 0.9, perfect agreement, sensitivity 100%, specificity 92.3%, positive predictive value 94.1%, negative predictive value 100%. Thirty-four (n=34) scrapings from FS were tested for cobas, p16, and/or HPV-ISH. The mean collection to testing time was 10.4 days (range: 1 to 28d). Cohen κ correlation for cobas versus p16-IHC/HPV-ISH on FS scrapings was 1, perfect agreement. Sensitivity, specificity, positive predictive value, and negative predictive value was 100%. Cobas genotype was HPV-16 in 87%, HPV-18 in 3%, and HPV-other in 10%. Cobas HPV test in FNA supernatant and FS scrapings outperformed or was equivalent to p16-IHC and provided genotyping information.

Susceptibility to a sexually transmitted disease in a wild koala population shows heritable genetic variance but no inbreeding depression.

The koala, one of the most iconic Australian wildlife species, is facing several concomitant threats that are driving population declines. Some threats are well known and have clear methods of prevention (e.g., habitat loss can be reduced with stronger land-clearing control), whereas others are less easily addressed. One of the major current threats to koalas is chlamydial disease, which can have major impacts on individual survival and reproduction rates and can translate into population declines. Effective management strategies for the disease in the wild are currently lacking, and, to date, we know little about the determinants of individual susceptibility to disease. Here, we investigated the genetic basis of variation in susceptibility to chlamydia using one of the most intensively studied wild koala populations. We combined data from veterinary examinations, chlamydia testing, genetic sampling and movement monitoring. Out of our sample of 342 wild koalas, 60 were found to have chlamydia. Using genotype information on 5007 SNPs to investigate the role of genetic variation in determining disease status, we found no evidence of inbreeding depression, but a heritability of 0.11 (95% CI: 0.06-0.23) for the probability that koalas had chlamydia. Heritability of susceptibility to chlamydia could be relevant for future disease management, as it suggests adaptive potential for the population.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

Alanyl-tRNA Synthetase 1 Gene Variants in Hereditary Neuropathy: Genotype and Phenotype Overview.

Background and ObjectivesOur objective was to report 2 novel variants and to reclassify previously reported alanyl-tRNA synthetase 1 (AARS1) variants associated with hereditary neuropathy and to summarize the clinical features of a previously published cohort of patients.MethodsWe performed detailed neurologic and electrophysiologic assessments and segregation analysis of 2 unrelated families with Charcot-Marie-Tooth (CMT) disease with novel variants in the AARS1 gene. Via literature search, we found studies that included neuropathy cases with AARS1 variants; we then reviewed and reclassified these variants.ResultsWe identified 2 CMT families harboring previously unreported likely pathogenic AARS1 variants: c.1823C>A p.(Thr608Lys) and c.1815C>G p.(His605Gln). In addition, we reinterpreted a total of 35 different AARS1 variants reported in cases with neuropathy from the literature: 9 variants fulfilled the current criteria for being (likely) pathogenic. We compiled and summarized standardized clinical and genotypic information for 90 affected individuals from 32 families with (likely) pathogenic AARS1 variants. Most experienced motor weakness and sensory loss in the lower limbs.DiscussionIn total, 11 AARS1 variants can currently be classified as pathogenic or likely pathogenic and are associated with sensorimotor axonal or intermediate, slowly progressive polyneuropathy with common asymmetry and variable age of symptom onset with no apparent involvement of other organ systems.

Clinical, microbiological, and genomic characteristics of clade-III Candida auris colonization and infection in southern California, 2019-2022.

Candida auris is an emerging fungal pathogen causing outbreaks in healthcare facilities. Five distinctive genomic clades exhibit clade-unique characteristics, highlighting the importance of real-time genomic surveillance and incorporating genotypic information to inform infection prevention practices and treatment algorithms. Both active and passive surveillance were used to screen hospitalized patients. C. auris polymerase chain reaction (PCR) assay on inguinal-axillary swabs was performed on high-risk patients upon admission. All clinical yeast isolates were identified to the species level. C. auris isolates were characterized by both phenotypic antifungal susceptibility tests and whole-genome sequencing. From late 2019 to early 2022, we identified 45 patients with C. auris. Most had a tracheostomy or were from a facility with a known outbreak. Moreover, 7 patients (15%) were only identified through passive surveillance. Also, 8 (18%) of the patients had a history of severe COVID-19. The overall mortality was 18%. Invasive C. auris infections were identified in 13 patients (29%), 9 (69%) of whom had bloodstream infections. Patients with invasive infection were more likely to have a central line. All C. auris isolates were resistant to fluconazole but susceptible to echinocandins. Genomic analysis showed that 1 dominant clade-III lineage is circulating in Los Angeles, with very limited intrahost and interhost genetic diversity. We have demonstrated that a robust C. auris surveillance program can be established using both active and passive surveillance, with multidisciplinary efforts involving the microbiology laboratory and the hospital epidemiology team. In Los Angeles County, C. auris strains are highly related and echinocandins should be used for empiric therapy.

VIVID: A Web Application for Variant Interpretation and Visualization in Multi-dimensional Analyses.

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.

G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data.

Maintaining duplicate germplasms in genebanks hampers effective conservation and utilization of genebank resources. The redundant germplasm adds to the cost of germplasm conservation by requiring a large proportion of the genebank financial resources towards conservation rather than enriching the diversity. Besides, genome-wide-association analysis using an association panel with over-represented germplasms can be biased resulting in spurious marker-trait associations. The conventional methods of germplasm duplicate removal using passport information suffer from incomplete or missing passport information and data handling errors at various stages of germplasm enrichment. This limitation is less likely in the case of genotypic data. Therefore, we developed a web-based tool, Germplasm Duplicate Identification and Removal Tool (G-DIRT), which allows germplasm duplicate identification based on identity-by-state analysis using single-nucleotide polymorphism genotyping information along with pre-processing of genotypic data. A homozygous genotypic difference threshold of 0.1% for germplasm duplicates has been determined using tetraploid wheat genotypic data with 94.97% of accuracy. Based on the genotypic difference, the tool also builds a dendrogram that can visually depict the relationship between genotypes. To overcome the constraint of high-dimensional genotypic data, an offline version of G-DIRT in the interface of R has also been developed. The G-DIRT is expected to help genebank curators, breeders and other researchers across the world in identifying germplasm duplicates from the global genebank collections by only using the easily sharable genotypic data instead of physically exchanging the seeds or propagating materials. The web server will complement the existing methods of germplasm duplicate identification based on passport or phenotypic information being freely accessible at http://webtools.nbpgr.ernet.in/gdirt/.

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection.

Targeted NGS-Based Analysis of Pneumocystis jirovecii Reveals Novel Genotypes.

Pneumocystis jirovecii is an important etiological agent of pneumonia that is underdiagnosed due to the inability to culture the organism. The 2019 PERCH study identified Pneumocystis as the top fungal cause of pneumonia in HIV-negative children using a PCR cutoff of 104 copies of Pneumocystis per mL of sample in nasopharyngeal/oropharyngeal (NP/OP) specimens. Given that Pneumocystis consists of an environmental ascus form and a trophic from (the latter is the form that attaches to the lung epithelium), it is possible that life-form-specific molecular assays may be useful for diagnosis. However, to accomplish this goal, these assays require genotypic information, as the current fungal genomic data are largely from the US and Europe. To genotype Pneumocystis across the globe, we developed an NGS-based genotyping assay focused on genes expressed in asci as well as trophs using PERCH throat swabs from Africa, Bangladesh, and Thailand, as well as North American samples. The NGS panel reliably detected 21 fungal targets in these samples and revealed unique genotypes in genes expressed in trophs, including Meu10, an ascospore assembly gene; two in mitochondrial gene ATP8, and the intergenic region between COX1 and ATP8. This assay can be used for enhanced Pneumocystis epidemiology to study outbreaks but also permits more accurate RT-CPR- or CRISPR-based assays to be performed to improve the non-bronchoscopic diagnosis of this under-reported fungal pathogen.