Hypothesis-free Approach Research Articles

Protocol for the Extraction and Characterization of Trabecular Meshwork Cell Cytoskeleton Fraction.

Among various cellular attributes modulating aqueous humor (AH) outflow through the trabecular pathway and eventually intraocular pressure, the involvement of actomyosin regulated cellular contraction and relaxation, cell-extracellular matrix adhesion and cell-cell junctions, and mechanotransduction are well recognized. Although various biological and pharmacological agent-modulated AH outflows were associated with altered actin cytoskeletal organization and cell adhesive interactions of trabecular meshwork (TM) cells, these changes were analyzed largely with a biased approach to examine the specific proteins, but there were very few efforts in examining them with hypothesis-free unbiased approach in their native state. Therefore, in this chapter, we describe a protocol tailored to characterize the cytoskeleton of TM cells. This simple protocol can be applied to identifying the differentially regulated TM cell cytoskeleton proteins under any given treatment condition in conjunction with quantitative proteomic analysis.

Gene-based association analysis of a large patient cohort provides insights into genetics of atypical femur fractures.

Several small genetic association studies have been conducted for atypical femur fracture (AFF) without replication of results. We assessed previously implicated and novel genes associated with AFFs in a larger set of unrelated AFF cases using whole exome sequencing (WES). We performed gene-based association analysis on 139 European AFF cases and 196 controls matched for bisphosphonate use. We tested all rare, protein-altering variants using both candidate gene and hypothesis-free approaches. In the latter, genes suggestively associated with AFFs (uncorrected p-values <.01) were investigated in a Swedish whole-genome sequencing replication study and assessed in 46 non-European cases. In the candidate gene analysis, PLOD2 showed a suggestive signal. The hypothesis-free approach revealed 10 tentative associations, with XRN2, SORD, and PLOD2 being the most likely candidates for AFF. XRN2 and PLOD2 showed consistent direction of effect estimates in the replication analysis, albeit not statistically significant. Three SNPs associated with SORD expression according to the GTEx portal were in linkage disequilibrium (R2 ≥ 0.2) with an SNP previously reported in a genome-wide association study of AFF. The prevalence of carriers of variants for both PLOD2 and SORD was higher in Asian versus European cases. While we did not identify genes enriched for damaging variants, we found suggestive evidence of a role for XRN2, PLOD2, and SORD, which requires further investigation. Our findings indicate that genetic factors responsible for AFFs are not widely shared among AFF cases. The study provides a stepping-stone for future larger genetic studies of AFF.

A Hypothesis-Free Approach to Identifying Potential Effects of Relative Age in School Year: an Instrumental Variable Phenome-Wide Association Study in the UK Biobank.

A child's relative age within their school year ('relative age') is associated with educational attainment and mental health. However, hypothesis driven studies often re-examine the same outcomes and exposure, potentially leading to confirmation and reporting biases, and missing unknown effects. Hypothesis-free outcome-wide analyses can potentially overcome these limitations. We conducted a hypothesis-free investigation of the effects of relative age within school year. We used an instrumental variable (IV)-pheWAS in the UK Biobank (participants aged 40-69 years at baseline), using the PHESANT software package. We created two IVs for relative age: being born in September vs. August (n=64 075) and week of birth (n=383 309). Outcomes passing the Bonferroni-corrected P value threshold for either instrument were plotted to identify a discontinuity at the school year transition. 13 traits associated with at least one of the instruments showed a discontinuity. Previously identified effects included those with a younger relative age being less likely to have educational qualifications and more likely to have started smoking at a younger age. We detected a few associations not explored by previous studies. For example, those with younger relative age had better lung function as adults. Hypothesis-free approaches could help address confirmation and reporting biases in epidemiology.

Quantifying variant contributions in cystic kidney disease using national-scale whole-genome sequencing.

BACKGROUNDCystic kidney disease (CyKD) is a predominantly familial disease in which gene discovery has been led by family-based and candidate gene studies, an approach that is susceptible to ascertainment and other biases.METHODSUsing whole-genome sequencing data from 1,209 cases and 26,096 ancestry-matched controls participating in the 100,000 Genomes Project, we adopted hypothesis-free approaches to generate quantitative estimates of disease risk for each genetic contributor to CyKD, across genes, variant types and allelic frequencies.RESULTSIn 82.3% of cases, a qualifying potentially disease-causing rare variant in an established gene was found. There was an enrichment of rare coding, splicing, and structural variants in known CyKD genes, with statistically significant gene-based signals in COL4A3 and (monoallelic) PKHD1. Quantification of disease risk for each gene (with replication in the separate UK Biobank study) revealed substantially lower risk associated with genes more recently associated with autosomal dominant polycystic kidney disease, with odds ratios for some below what might usually be regarded as necessary for classical Mendelian inheritance. Meta-analysis of common variants did not reveal significant associations, but suggested this category of variation contributes 3%-9% to the heritability of CyKD across European ancestries.CONCLUSIONBy providing unbiased quantification of risk effects per gene, this research suggests that not all rare variant genetic contributors to CyKD are equally likely to manifest as a Mendelian trait in families. This information may inform genetic testing and counseling in the clinic.

A novel hypothesis-generating approach for detecting phenotypic associations using epigenetic data.

Aim: Hypotheses about what phenotypes to include in causal analyses, that in turn can have clinical and policy implications, can be guided by hypothesis-free approaches leveraging the epigenome, for example.Materials & methods: Minimally adjusted epigenome-wide association studies (EWAS) using ALSPAC data were performed for example conditions, dysmenorrhea and heavy menstrual bleeding (HMB). Differentially methylated CpGs were searched in the EWAS Catalog and associated traits identified. Traits were compared between those with and without the example conditions in ALSPAC.Results: Seven CpG sites were associated with dysmenorrhea and two with HMB. Smoking and adverse childhood experience score were associated with both conditions in the hypothesis-testing phase.Conclusion: Hypothesis-generating EWAS can help identify associations for future analyses.

Genetic Findings in Short Turkish Children Born to Consanguineous Parents.

The diagnostic yield of genetic analysis in the evaluation of children with short stature depends on associated clinical characteristics, but the additional effect of parental consanguinity has not been well documented. This observational case series of 42 short children from 34 consanguineous families was collected by six referral centres of paediatric endocrinology (inclusion criteria: short stature and parental consanguinity). In 18 patients (12 families, group 1), the clinical features suggested a specific genetic defect in the growth hormone (GH) insulin-like growth factor I (IGF-I) axis, and a candidate gene approach was used. In others (group 2), a hypothesis-free approach was chosen (gene panels, microarray analysis, and whole exome sequencing) and further subdivided into 11 patients with severe short stature (height &lt;-3.5 standard deviation score [SDS]) and microcephaly (head circumference &lt;-3.0 SDS) (group 2a), 10 patients with syndromic short stature (group 2b), and 3 patients with nonspecific isolated GH deficiency (group 2c). In all 12 families from group 1, (likely) pathogenic variants were identified in GHR, IGFALS, GH1, and STAT5B. In 9/12 families from group 2a, variants were detected in PCNT, SMARCAL1, SRCAP, WDR4, and GHSR. In 5/9 families from group 2b, variants were found in TTC37, SCUBE3, NSD2, RABGAP1, and 17p13.3 microdeletions. In group 2c, no genetic cause was found. Homozygous, compound heterozygous, and heterozygous variants were found in 21, 1, and 4 patients, respectively. Genetic testing in short children from consanguineous parents has a high diagnostic yield, especially in cases of severe GH deficiency or insensitivity, microcephaly, and syndromic short stature.

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5–11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals

Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes. We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N≤2,619,939), Latin American (N≤446,646) and African American (N≤146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses. The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N=232, European; N=29, Latin American; N=7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort. Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine. MVJ, NCK, SBB, SSR and AAP were supported by T32IR5226 and 28IR-0070. SSR was also supported by NIDA DP1DA054394. NCK and RBC were also supported by R25MH081482. ASH was supported by funds from NIAAA K01AA030083. JLMO was supported by VA 1IK2CX002095. JLMO and JJMM were also supported by NIDA R21DA050160. JJMM was also supported by the Kavli Postdoctoral Award for Academic Diversity. EGA was supported by K01MH121659 from the NIMH/NIH, the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. MSA was supported by the Instituto de Salud Carlos III and co-funded by the European Union Found: Fondo Social Europeo Plus (FSE+) (P19/01224, PI22/00464 and CP22/00128).

Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank

Introduction: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world’s largest brain imaging study. Methods: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2,891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2–13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate-adjusted p value <0.05 was used to declare statistical significance. Results: Older age, male sex, greater height, and whole-body fat-free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected p < 0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. Conclusion: Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings.

Screening of Environmental Chemicals to Characterize Exposures in Participants With Systemic Lupus Erythematosus.

There is a need to characterize exposures associated with the pathogenesis of systemic lupus erythematosus (SLE). In this pilot study, we explore a hypothesis-free approach that can measure thousands of exogenous chemicals in blood ("exposome") in patients with SLE and unaffected controls. This cross-sectional study analyzed a cohort of patients with prevalent SLE (n=285) and controls (n=106). Plasma was analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). Mass spectrometry features present in at least 25% of all samples were selected for association analysis (n=2,737). Features were matched to potential chemicals using available databases. Association analysis of abundances of features with SLE status was performed, adjusting for age and sex. We also explored features associated with SLE phenotypes, sociodemographic factors, and current medication use. We found 30 features significantly associated with SLE status (Bonferroni P < 0.05). Of these, seven matched chemical names based on databases. These seven features included phthalate metabolites, a formetanate metabolite, and eugenol. The abundance of acid pesticides differed between patients with SLE and controls (Bonferroni P < 0.05). Two unmatched features were associated with a history of lupus nephritis, and one with anti-double-stranded DNA antibody production (Bonferroni P < 0.05). Seventeen features varied by self-reported race and ethnicity, including a polyfluoroalkyl substance (analysis of variance P < 1.69 × 10-5). Eleven features correlated with antimalarials, 6 with mycophenolate mofetil, and 29 with prednisone use. This proof-of-concept study demonstrates that LC-QTOF/MS is a powerful tool that agnostically detects circulating exogenous compounds. These analyses can generate hypotheses of disease-related exposures for future prospective, longitudinal studies.

Non-targeted metabolomics for the identification of plasma metabolites associated with organic anion transporting polypeptide 1B1 function.

Our aim was to evaluate biomarkers for organic anion transporting polypeptide 1B1 (OATP1B1) function using a hypothesis-free metabolomics approach. We analyzed fasting plasma samples from 356 healthy volunteers using non-targeted metabolite profiling by liquid chromatography high-resolution mass spectrometry. Based on SLCO1B1 genotypes, we stratified the volunteers to poor, decreased, normal, increased, and highly increased OATP1B1 function groups. Linear regression analysis, and random forest (RF) and gradient boosted decision tree (GBDT) regressors were used to investigate associations of plasma metabolite features with OATP1B1 function. Of the 9152 molecular features found, 39 associated with OATP1B1 function either in the linear regression analysis (p < 10-5) or the RF or GBDT regressors (Gini impurity decrease > 0.01). Linear regression analysis showed the strongest associations with two features identified as glycodeoxycholate 3-O-glucuronide (GDCA-3G; p = 1.2 × 10-20 for negative and p = 1.7 × 10-19 for positive electrospray ionization) and one identified as glycochenodeoxycholate 3-O-glucuronide (GCDCA-3G; p = 2.7 × 10-16). In both the RF and GBDT models, the GCDCA-3G feature showed the strongest association with OATP1B1 function, with Gini impurity decreases of 0.40 and 0.17. In RF, this was followed by one GDCA-3G feature, an unidentified feature with a molecular weight of 809.3521, and the second GDCA-3G feature. In GBDT, the second and third strongest associations were observed with the GDCA-3G features. Of the other associated features, we identified with confidence two representing lysophosphatidylethanolamine 22:5. In addition, one feature was putatively identified as pregnanolone sulfate and one as pregnenolone sulfate. These results confirm GCDCA-3G and GDCA-3G as robust OATP1B1 biomarkers in human plasma.

OR21-02 Diagnostic Yield Of A Multigene Approach In Children With Short Stature Of Unknown Cause

Abstract Disclosure: N.L. Menezes De Andrade: None. M.F. Funari: None. A. Malaquias: Speaker; Self; Novo Nordisk. N.C. Dantas: None. R. Rezende: None. L.D. Cellin: None. T.K. Homma: None. A.M. Lerario: None. I.J. Arnhold: None. G.A. Vasques: Employee; Self; Eli Lilly &amp; Company. A.A. Jorge: Advisory Board Member; Self; Novo Nordisk. Consulting Fee; Self; Novo Nordisk. Research Investigator; Self; Novo Nordisk. Speaker; Self; Pfizer Global R&amp;D. Introduction: The genetic factors are frequently implicated in the growth impairment of short stature (SS) children, whether they are syndromic or apparently healthy. However, the investigation recommended by the consensus fails to establish the etiology in most cases. The difficulty to search for candidate genes increases the importance of using a hypothesis-free approach as whole exome sequencing (WES) for these children. Objective: To determine the diagnostic yield of a multigene gene analysis in a cohort of children with SS of unknown cause. Patients and methods: We enrolled children with syndromic (n= 143) and isolated SS (n=380) of unknown cause after traditional investigation. The genetic analysis was applied to all patients [WES =306, panel = 217, including copy number variants (CNVs) analysis]. A chromosomal microarray analysis was also used for 79 syndromic SS. Results: A genetic defect was identified in 74 and 70 children with syndromic and isolated SS, respectively. Variants associated with syndromic SS were widely distributed in several rare conditions, affecting 63 different genes: COL2A1 in 3 patients; ANKRD11; BCL11B; LTBP3; CHD7; PTPN11; PUF60; SRCAP; STAT5B, GNAS in two patients each; SCN1A; SETD5; PTHLH; GINS1; ZMYM2; TERT; G6PC3; KIF11; SCUBE3; TBC1D32; PDHA1; RPS6KA3; SMARCA4; DNM2; KMT2A ; IHH; BRCA1; NF1; INPP5K; KRAS; FGFR2; DPH1; RPL5; KDM5C; DYRK1A; MVK; FBN1; AFF4; ACTB; POC1A; IGF1R; CREBBP; POLD1; POLR3B; PCNA; CUL7; RUNX2; EXT1; GH1; FGD1; BLM; CHRNG in one patient each. In children with isolated SS, 5 children had more than one causative finding. There were 39 defects associated with the growth plate [SHOX (n=13); NPR2 (n=7); IHH (n=10); ACAN (n=5); FGFR3 (n=3); COL2A1 (n=1)], 6 involved in the GH-IGF-1 axis [GHSR (n=3); IGF1R (n=2); GH1 (n=1)], 9 related to the RAS/MAPK pathway [PTPN11 (n=4); NF1 (n=3); BRAF (n=1); CBL (n=1)]; and 17 other genes [LTBP3 (n=2); THRA (n=2); TYMP; PIK3CA; CDKN1C; ANKRD11; HMGA2; SRCAP; KMT2C; GDF5; OBSL1; MC4R; FBN1; PRKG2; ERF (one patient each)]. Additionally, a pathogenic CNVs were identified in 11 and 3 patients with syndromic and isolated SS, respectively. The diagnostic rate was 51.7% for patients with syndromic features vs 18,4% in patients with isolated SS (p &amp;lt; 0.001). Patients with or without a genetic diagnosis cannot be clinically differentiated into either group. Conclusion: The diagnostic yield of a multigene sequencing approach was three time higher in patients with syndromic SS than those with isolated SS. Nevertheless, the identification of a monogenic condition has a clear impact on patients’ follow-up and treatment for both groups. Maximum of 2,500 characters, including punctuation (not spaces). Presentation: Saturday, June 17, 2023

Genetic Polymorphisms Associated with the Efficiency of Weight Loss: A Systematic Review

Interindividual differences are associated not only with body weight but also determine the body’s response to diet and physical activity. This systematic review aimed to identify the genetic markers associated with diet-induced and exercise-induced weight loss. The eligible intervention studies for this systematic review contained all the necessary parameters for genetic polymorphism, diet, exercise, and changes in anthropometric or body composition parameters. In total, 91 articles met the criteria and were included in this systematic review. The vast majority of studies (n = 88) are used of candidate gene approach, and only three studies were performed using hypothesis-free approach (GWAS). A total of 98 genetic variants were found, of which 72 markers are associated with the efficiency of diet therapy, and 26 – with individual response to physical activity. It should be noted that the significance of markers was replicated for only 10 of 98 genetic variants. In the near future, we should expect progress in this area, which will result in the development of a method for individual selection for each patient of the type of diet and exercise for the prevention and treatment of obesity.

14th century Yersinia pestis genomes support emergence of pestis secunda within Europe.

Pestis secunda (1356-1366 CE) is the first of a series of plague outbreaks in Europe that followed the Black Death (1346-1353 CE). Collectively this period is called the Second Pandemic. From a genomic perspective, the majority of post-Black Death strains of Yersinia pestis thus far identified in Europe display diversity accumulated over a period of centuries that form a terminal sub-branch of the Y. pestis phylogeny. It has been debated if these strains arose from local evolution of Y. pestis or if the disease was repeatedly reintroduced from an external source. Plague lineages descended from the pestis secunda, however, are thought to have persisted in non-human reservoirs outside Europe, where they eventually gave rise to the Third Pandemic (19th and 20th centuries). Resolution of competing hypotheses on the origins of the many post-Black Death outbreaks has been hindered in part by the low representation of Y. pestis genomes in archaeological specimens, especially for the pestis secunda. Here we report on five individuals from Germany that were infected with lineages of plague associated with the pestis secunda. For the two genomes of high coverage, one groups within the known diversity of genotypes associated with the pestis secunda, while the second carries an ancestral genotype that places it earlier. Through consideration of historical sources that explore first documentation of the pandemic in today's Central Germany, we argue that these data provide robust evidence to support a post-Black Death evolution of the pathogen within Europe rather than a re-introduction from outside. Additionally, we demonstrate retrievability of Y. pestis DNA in post-cranial remains and highlight the importance of hypothesis-free pathogen screening approaches in evaluations of archaeological samples.

Advantage of precision metagenomics for urinary tract infection diagnostics.

Urinary tract infections (UTIs) remain a diagnostic challenge and often promote antibiotic overuse. Despite urine culture being the gold standard for UTI diagnosis, some uropathogens may lead to false-negative or inconclusive results. Although PCR testing is fast and highly sensitive, its diagnostic yield is limited to targeted microorganisms. Metagenomic next-generation sequencing (mNGS) is a hypothesis-free approach with potential of deciphering the urobiome. However, clinically relevant information is often buried in the enormous amount of sequencing data. Precision metagenomics (PM) is a hybridization capture-based method with potential of enhanced discovery power and better diagnostic yield without diluting clinically relevant information. We collected 47 urine samples of clinically suspected UTI and in parallel tested each sample by microbial culture, PCR, and PM; then, we comparatively analyzed the results. Next, we phenotypically classified the cumulative microbial population using the Explify® data analysis platform for potential pathogenicity. Results revealed 100% positive predictive agreement (PPA) with culture results, which identified only 13 different microorganisms, compared to 19 and 62 organisms identified by PCR and PM, respectively. All identified organisms were classified into phenotypic groups (0-3) with increasing pathogenic potential and clinical relevance. This PM can simultaneously quantify and phenotypically classify the organisms readily through bioinformatic platforms like Explify®, essentially providing dissected and quantitative results for timely and accurate empiric UTI treatment. PM offers potential for building effective diagnostic models beyond usual care testing in complex UTI diseases. Future studies should assess the impact of PM-guided UTI management on clinical outcomes.

No Association between the Plasmodium vivax crt-o MS334 or In9pvcrt Polymorphisms and Chloroquine Failure in a Pre-Elimination Clinical Cohort from Malaysia with a Large Clonal Expansion.

Increasing reports of resistance to a frontline malaria blood-stage treatment, chloroquine (CQ), raises concerns for the elimination of Plasmodium vivax. The absence of an effective molecular marker of CQ resistance in P. vivax greatly constrains surveillance of this emerging threat. A recent genetic cross between CQ sensitive (CQS) and CQ resistant (CQR) NIH-1993 strains of P. vivax linked a moderate CQR phenotype with two candidate markers in P. vivax CQ resistance transporter gene (pvcrt-o): MS334 and In9pvcrt. Longer TGAAGH motif lengths at MS334 were associated with CQ resistance, as were shorter motifs at the In9pvcrt locus. In this study, high-grade CQR clinical isolates of P. vivax from a low endemic setting in Malaysia were used to investigate the association between the MS334 and In9pvcrt variants and treatment efficacy. Among a total of 49 independent monoclonal P. vivax isolates assessed, high-quality MS334 and In9pvcrt sequences could be derived from 30 (61%) and 23 (47%), respectively. Five MS334 and six In9pvcrt alleles were observed, with allele frequencies ranging from 2 to 76% and 3 to 71%, respectively. None of the clinical isolates had the same variant as the NIH-1993 CQR strain, and none of the variants were associated with CQ treatment failure (all P > 0.05). Multi-locus genotypes (MLGs) at 9 neutral microsatellites revealed a predominant P. vivax strain (MLG6) accounting for 52% of Day 0 infections. The MLG6 strain comprised equal proportions of CQS and CQR infections. Our study reveals complexity in the genetic basis of CQ resistance in the Malaysian P. vivax pre-elimination setting and suggests that the proposed pvcrt-o MS334 and In9pvcrt markers are not reliable markers of CQ treatment efficacy in this setting. Further studies are needed in other endemic settings, applying hypothesis-free genome-wide approaches, and functional approaches to understand the biological impact of the TGAAGH repeats linked to CQ response in a cross are warranted to comprehend and track CQR P. vivax.

Whole genome sequencing in the 100,000 Genomes project identifies a RYR2 variant associating with dilated cardiomyopathy and sudden unexpected death in the young

Abstract Funding Acknowledgements Type of funding sources: None. Background Previous studies have shown genetic testing in sudden unexpected death in the young (SUDY) cases identify a pathogenic variant in 13-27% of cases. This diagnostic yield increased up to 39% when genetic testing was combined with family evaluation. The 100,000 Genomes Project was launched to investigate the role of whole genome sequencing (WGS) as a diagnostic tool for cancers and rare diseases in a national health care system. Young sudden death victims with no finding on postmortem testing were enrolled to the SUDY cohort. Purpose We aimed to analyse WGS data in the SUDY cohort of 100,000 Genomes Project with panel analysis and trio studies in order to examine the yield of WGS in this population. Methods Sudden death victims, aged 40 or younger, without pre-existing condition that would explain the event and with no diagnosis established on postmortem examination were enrolled between 2017 and 2019 at different Genomic Medicine Centres (GMCs) across the UK. Parents were recruited in all paediatric cases. DNA sequencing was performed by Illumina. The platform's automated pipeline was used for variant calling. Panel based and hypothesis free approaches were used for data analysis. Variant analysis was performed with focus on variants with combined annotation-dependent depletion (CADD) score &amp;gt;15 and American College of Medical Genetics and Genomics (ACMG) classification of pathogenic (P), likely pathogenic (LP), or variant of unknown significance (VUS). Results A total of 15 participants, including 6 trios, were included in the study. Participants had a median age of 18 [IQR 1-29.5] and two thirds were male. Following assessment of all protein coding and intronic variants identified, only the missense variant RYR2:c.10046C&amp;gt;T (p.Ser3349Leu) was thought to be suitable for investigation for upgrade to likely pathogenic, giving a diagnostic yield of 6.67% for WGS in SUDY cases. This variant was detected in a 24-year-old sudden death victim. Family evaluation led to the diagnosis of dilated cardiomyopathy (DCM) in four family members and segregation testing confirmed affected family members harboured the variant. Further investigation in 100,000 Genomes Project, laboratory data, and our inherited cardiac conditions database as well as previous publications, identified additional cases of this variant in association with DCM and sudden cardiac death (see table and figure). Conclusion WGS with targeted gene analysis can be an effective diagnostic tool for SUDY. The study’s findings show the importance of large population databases and collaboration in the research community, allowing reclassification of a RYR2 VUS as a LP variant, the strongest genetic data so far to associate a missense variant with familial DCM and the risk for SCD. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project.

Brain subnetworks most sensitive to alterations of functional connectivity in Schizophrenia: a data-driven approach.

Functional connectivity (FC) of the brain changes in various brain disorders. Its complexity, however, makes it difficult to obtain a systematic understanding of these alterations, especially when they are found individually and through hypothesis-based methods. It would be easier if the variety of brain connectivity alterations is extracted through data-driven approaches and expressed as variation modules (subnetworks). In the present study, we modified a blind approach to determine inter-group brain variations at the network level and applied it specifically to schizophrenia (SZ) disorder. The analysis is based on the application of independent component analysis (ICA) over the subject's dimension of the FC matrices, obtained from resting-state functional magnetic resonance imaging (rs-fMRI). The dataset included 27 SZ people and 27 completely matched healthy controls (HC). This hypothesis-free approach led to the finding of three brain subnetworks significantly discriminating SZ from HC. The area associated with these subnetworks mostly covers regions in visual, ventral attention, and somatomotor areas, which are in line with previous studies. Moreover, from the graph perspective, significant differences were observed between SZ and HC for these subnetworks, while there was no significant difference when the same parameters (path length, network strength, global/local efficiency, and clustering coefficient) across the same limited data were calculated for the whole brain network. The increased sensitivity of those subnetworks to SZ-induced alterations of connectivity suggested whether an individual scoring method based on their connectivity values can be applied to classify subjects. A simple scoring classifier was then suggested based on two of these subnetworks and resulted in acceptable sensitivity and specificity with an area under the ROC curve of 77.5%. The third subnetwork was found to be a less specific building block (module) for describing SZ alterations. It projected a wider range of inter-individual variations and, therefore, had a lower chance to be considered as a SZ biomarker. These findings confirmed that investigating brain variations from a modular viewpoint can help to find subnetworks that are more sensitive to SZ-induced alterations. Altogether, our study results illustrated the developed method's ability to systematically find brain alterations caused by SZ disorder from a network perspective.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond.

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Polygenic score: An anchor holding the whole life course.

Polygenic score: An anchor holding the whole life course.