BACKGROUND: Multilocus pathogenic variants (MPVs) are genetic changes that affect multiple gene loci or regions of the genome, collectively leading to multiple molecular diagnoses. MPVs may also contribute to intrafamilial phenotypic variability between affected individuals within a nuclear family. In this study, we aim to gain further insights into the influence of MPVs on a disease manifestation in individual research subjects and explore the complexities of the human genome within a familial context.
METHODS: We conducted a systematic reanalysis of exome sequencing data and runs of homozygosity (ROH) regions of 47 sibling pairs previously diagnosed with various neurodevelopmental disorders (NDD).
RESULTS: We found siblings with MPVs driven by long ROH regions in 8.5% of families (4/47). The patients with MPVs exhibited significantly higher FROH values (p-value = 1.4e-2) and larger total ROH length (p-value = 1.8e-2). Long ROH regions mainly contribute to this pattern; the siblings with MPVs have a larger total size of long ROH regions than their siblings in all families (p-value = 6.9e-3). Whereas the short ROH regions in the siblings with MPVs are lower in total size compared to their sibling pairs with single locus pathogenic variants (p-value = 0.029), and there are no statistically significant differences in medium ROH regions between sibling pairs (p-value = 0.52).
CONCLUSIONS: This study sheds light on the significance of considering MPVs in families with affected sibling pairs and the role of ROH as an adjuvant tool in explaining clinical variability within families. Identifying individuals carrying MPVs may have implications for disease management, identification of possible disease risks to different family members, genetic counseling and exploring personalized treatment approaches.

UNASSIGNED: Becker muscular dystrophy (BMD) is a dystrophinopathy due to in-frame mutations in the dystrophin gene (DMD) which determines a reduction of dystrophin at muscle level. BMD has a wide spectrum of clinical variability with different degrees of disability. Studies of natural history are needed also in view of up-coming clinical trials.
UNASSIGNED: From an initial cohort of 32 BMD adult subjects, we present a detailed phenotypic characterization of 28 patients, then providing a description of their clinical natural history over the course of 12 months for 18 and 24 months for 13 of them.
UNASSIGNED: Each patient has been genetically characterized. Baseline, and 1-year and 2 years assessments included North Star Ambulatory Assessment (NSAA), timed function tests (time to climb and descend four stairs), 6-minute walk test (6MWT), Walton and Gardner-Medwin Scale and Medical Research Council (MRC) scale. Muscle magnetic resonance imaging (MRI) was acquired at baseline and in a subgroup of 9 patients after 24 months. Data on cardiac function (electrocardiogram, echocardiogram, and cardiac MRI) were also collected.
UNASSIGNED: Among the clinical heterogeneity, a more severe involvement is often observed in patients with 45-X del, with a disease progression over two years. The 6MWT appears sensitive to detect modification from baseline during follow up while no variation was observed by MRC testing. Muscle MRI of the lower limbs correlates with clinical parameters.Our study further highlights how the phenotypic variability of BMD adult patients makes it difficult to describe an uniform course and substantiates the need to identify predictive parameters and biomarkers to stratify patients.

Additive genetic variance, VA, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g. increased close-relative inbreeding) can alter VA, but how they do so depends on the (typically unknown) gene action and allele frequencies across many loci. For example, VA increases proportionally with the inbreeding coefficient when allelic effects are additive, but smaller (or larger) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for dominance effects to deflate VA under inbreeding. Applying a powerful paired pedigree design in Drosophila serrata, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small VA, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of VA, providing insight into historical selection and future evolutionary potential.

BACKGROUND: Given the specificity of bacteriophage attachment receptors, a single bacterial isolate is currently utilized to match to a bacteriophage therapeutic, thereby extrapolating activity to all bacteria in vivo. Consistently, the main bacteriophage attachment receptor for Staphylococcus aureus is teichoic acid, and it is known that this receptor has phenotypic variations in different in vivo environments. Consequently, the aim of this study was to determine whether bacteriophage activity is similar across all in vivo prosthetic joint infection environments.
METHODS: Three patients with prosthetic joint infections who had S. aureus grow from arthrocentesis cultures and at least three deep tissue cultures were analyzed for growth inhibition with a library of 56 bacteriophages.
RESULTS: Discordant bacteriophage activity was seen across the different in vivo environments. Furthermore, bacteriophages with the most robust lytic potential to the arthrocentesis isolates usually did not demonstrate activity corresponding to all the deep tissue clinical isolates.
CONCLUSIONS: Variations of bacteriophage activity can occur between the different in vivo clinical environments, which is likely secondary to different glycosylation patterns of teichoic acid. Consequently, if discordant activity is present, retreating with bacteriophages that have activity is likely needed for effective, reproducible outcomes.

Background  Patients with atrial fibrillation (AF) are frequently treated with apixaban 2.5-mg twice daily (BID) off-label, presumably to reduce the bleeding risk. However, this approach has the potential to increase the risk of ischemic stroke. If a single measurement could reliably identify patients with high drug levels, the increased stroke risk may be mitigated by confining off-label dose reduction to such patients. Objectives  This study aimed to determine whether a single high apixaban level is predictive of a similarly high level when the test is repeated in 2 months. Methods  In this prospective cohort study of clinic patients receiving apixaban 5-mg BID for AF or venous thromboembolism, peak and trough apixaban levels were measured using the STA-Liquid anti-Xa assay at baseline and 2 months. We calculated the proportions of patients with levels that remained in the upper quintile. Results  Of 100 enrolled patients, 82 came for a second visit, 55 of whom were treated with apixaban 5-mg BID. Seven (63.6%, 95% confidence interval [CI]: 35.4-84.8%) and nine (81.8%, 95% CI: 52.3-94.9%) of 11 patients with a baseline trough and peak level in the upper quintile, respectively, had a subsequent level that remained within this range. Only one (9.1%, 95% CI: 1.6-37.7%) patient had a subsequent level that fell just lower than the median. Conclusion  The trough and peak levels of apixaban in patients who have a high level on a single occasion, usually remain high when the assay is repeated in 2 months. Accordingly, the finding of a high apixaban level in patients deemed to be at high risk of bleeding, allows physicians contemplating off-label use of the 2.5-mg BID dose to limit its use to selected patients who are less likely to be exposed to an increased risk of thrombosis.

COVID-19-related restrictions impacted weight and weight-related factors during the initial months of the pandemic. However, longitudinal analyses are scarce. An online, longitudinal study was conducted among self-selected UK adults (n = 1818), involving three surveys (May-June, August-September, November-December 2020), covering anthropometric, sociodemographic, COVID-19-related and behavioural measures. Data were analysed using generalised estimating equations. Self-reported average weight/body mass index (BMI) significantly increased between the May-June period and the August-September period (74.95 to 75.33 kg/26.22 kg/m2 to 26.36kg/m2, p < 0.001, respectively), and then significantly decreased to November-December (to 75.06 kg/26.27 kg/m2, p < 0.01), comparable to May-June levels (p = 0.274/0.204). However, there was great interindividual variation, 37.0%/26.7% increased (average 3.64 kg (95% confidence interval: 3.32, 3.97)/1.64 kg/m2 (1.49, 1.79)), and 34.5%/26.3% decreased (average 3.59 kg (3.34, 3.85)/1.53 kg/m2 (1.42, 1.63)) weight/BMI between May-June and November-December. Weight/BMI increase was significantly negatively associated with initial BMI, and positively associated with monthly high fat, salt and sugar (HFSS) snacks intake and alcohol consumption, and for BMI only, older age. Associations were time-varying; lower initial BMI, higher HFSS snacks intake and high-risk alcohol consumption were associated with maintaining weight/BMI increases between August-September and November-December. The average weight/BMI of UK adults fluctuated between May-June and November-December 2020. However, the substantial interindividual variation in weight/BMI trajectories indicates long-term health impacts from the pandemic, associated with food and alcohol consumption.

Parkinson\'s disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson\'s disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson\'s disease that accounts for intra-individual and inter-individual variability and medication effects.
In this longitudinal data study, data were collected for up to 7-years on 423 patients with early Parkinson\'s disease and 196 healthy controls from the Parkinson\'s Progression Markers Initiative (PPMI) longitudinal observational study. A contrastive latent variable model was applied followed by a novel personalised input-output hidden Markov model to define disease states. Clinical significance of the states was assessed using statistical tests on seven key motor or cognitive outcomes (mild cognitive impairment, dementia, dyskinesia, presence of motor fluctuations, functional impairment from motor fluctuations, Hoehn and Yahr score, and death) not used in the learning phase. The results were validated in an independent sample of 610 patients with Parkinson\'s disease from the National Institute of Neurological Disorders and Stroke Parkinson\'s Disease Biomarker Program (PDBP).
PPMI data were download July 25, 2018, medication information was downloaded on Sept 24, 2018, and PDBP data were downloaded between June 15 and June 24, 2020. The model discovered eight disease states, which are primarily differentiated by functional impairment, tremor, bradykinesia, and neuropsychiatric measures. State 8, the terminal state, had the highest prevalence of key clinical outcomes including 18 (95%) of 19 recorded instances of dementia. At study outset 4 (1%) of 333 patients were in state 8 and 138 (41%) of 333 patients reached stage 8 by year 5. However, the ranking of the starting state did not match the ranking of reaching state 8 within 5 years. Overall, patients starting in state 5 had the shortest time to terminal state (median 2·75 [95% CI 1·75-4·25] years).
We developed a statistical progression model of early Parkinson\'s disease that accounts for intra-individual and inter-individual variability and medication effects. Our predictive model discovered non-sequential, overlapping disease progression trajectories, supporting the use of non-deterministic disease progression models, and suggesting static subtype assignment might be ineffective at capturing the full spectrum of Parkinson\'s disease progression.
Michael J Fox Foundation.

The combination of substance use and psychiatric disorders is one of the most common comorbidities. The objective of this study was to perform a genome-wide association study of this comorbidity (Com), substance use alone (Subs), and psychiatric symptomatology alone (Psych) in the Mexican population. The study included 3914 individuals of Mexican descent. Genotyping was carried out using the PsychArray microarray and genome-wide correlations were calculated. Genome-wide associations were analyzed using multiple logistic models, polygenic risk scores (PRSs) were evaluated using multinomial models, and vertical pleiotropy was evaluated by generalized summary-data-based Mendelian randomization. Brain DNA methylation quantitative loci (brain meQTL) were also evaluated in the prefrontal cortex. Genome-wide correlation and vertical pleiotropy were found between all traits. No genome-wide association signals were found, but 64 single-nucleotide polymorphism (SNPs) reached nominal associations (p < 5.00e-05). The SNPs associated with each trait were independent, and the individuals with high PRSs had a higher prevalence of tobacco and alcohol use. In the multinomial models all of the PRSs (Subs-PRS, Com-PRS, and Psych-PRS) were associated with all of the traits. Brain meQTL of the Subs-associated SNPs had an effect on the genes enriched in insulin signaling pathway, and that of the Psych-associated SNPs had an effect on the Fc gamma receptor phagocytosis pathway.

BACKGROUND: The main objectives of this study were to find the possible structural association between the activity of enzymatic antioxidants and the grain yield of triticale plants as well as identifying the genotypic variability which might be effective on this association. Accordingly, expression levels of superoxide dismutase (SOD) isozymes (Mn-SOD, Cu/Zn-SOD, and Fe-SOD) were appraised to distinguish any possible relationship between SOD expression and drought resistance of triticale. A novel analytical method for distinguishing elite genotypes based on measured features was proposed. Additionally, a new programing based on SAS-language (IML) was introduced to estimate the genetic parameters rooted from combined ANOVA model (linear mixed model), which is capable of being used in any field study other than the current one.
METHODS: Thirty genotypes of triticale were studied under normal and drought stress conditions during 6 years (three different locations). Accordingly, based on the results of genetic variability, heatmap analysis, biplot graph, and clustering technique, two genotypes with the highest genetic distance were selected to appraise the differential expression profiling of three SOD isozyme in shoot and root organs.
RESULTS: Field experiments and bioinformatics results showed that superoxide dismutase (SOD) was the most influential antioxidant in resistance of triticale to drought stress; therefore, it could be used as an indirect selection index in early stages to distinguish resistant genotypes to drought stress. Additionally, Mn-SOD and Fe-SOD showed roughly similar expression levels for both genotypes under drought stress. However, Cu/Zn-SOD expression level was higher in root and shoot of the tolerant genotype than the susceptible genotype.
CONCLUSIONS: Heatmap analysis that is applied for the first time to screen suitable genotypes, showed to be highly capable of distinguishing elite genotypes and pointing out the proper features for selection criteria. Bioinformatics results indicated that SOD is more important than other enzymatic antioxidant for being considered as selection criteria or candidate gene for transgenic purposes. Based on expressional results, Mn-SOD announced as a general isozyme that is probably highly expressed in most of the species, while, Cu/Zn-SOD was introduced as a genotype specific isozyme that is likely more expressed in tolerant genotypes.

OBJECTIVE: Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.
METHODS: Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.
RESULTS: The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar\'s test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.
CONCLUSIONS: The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.