Occurrence and genetic characterization of Echinococcus granulosus sensu lato from domestic animals in Central Iran

Background The species complex of Echinococcus granulosus sensu lato (s.l.) causes cystic echinococcosis distributed worldwide. There is no genotype information from hydatid cysts in the intermediate hosts in Central Iran. Therefore, in this study, we analyzed the hydatid cysts in livestock slaughtered in an abattoir in this region. Six hundred fifty-seven hydatid cysts were isolated from 97 animals, including sheep, cattle, camels, and goats slaughtered in Yazd abattoir from September 2018 to January 2020. The demographic data was collected as well as cyst location, fertility, and viability. Out of 657 samples, 164 samples were genotyped. Then, phylogenetic analysis was performed using MEGAX. Statistical analyses were done using SPSS version 16.0 by chi-square with a significant difference of less than 0.05. Results Out of 164 samples, the G1-G3 complex genotype had the most frequency in samples, with 135 cases recognized. The G6/G7 was observed in 19 isolates and G5 was reported in nine samples. One sample was detected as Taenia hydatigena. Conclusions This study showed that G1-G3 and G6/G7 genotypes were presented in all animals, but G5 was reported only in cattle, goats, and camels. It is the first molecular identification of cystic echinococcosis in Central Iran. Hence, reporting G5 in livestock in this area should be considered due to transmission to humans.

A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer’s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

A Review of Genome-Based Precision Medicine Efforts Within the Department of Defense

ABSTRACT Introduction Providing patient-specific clinical care is an expanding focus for medical professionals and researchers, more commonly referred to as personalized or precision medicine. The goal of using a patient-centric approach is to provide safer care while also increasing the probability of therapeutic success through careful consideration of the influence of certain extrinsic and intrinsic human factors in developing the patient care plan. Of increasing influence on patient care is the phenotype and genotype information gathered from employing various next-generation sequencing methods. Guided by and partnered with our civilian colleagues, clinical components within the DoD are embracing and advancing genomic medicine in many facets—from the bench to the bedside—and in many therapeutic areas, from Psychiatry to Oncology. In this PubMed-based review, we describe published clinical research and interventions within the DoD using genome-informed data and emphasize precision medicine efforts in earlier stages of development with the potential to revolutionize the approach to therapeutics. Materials and Methods The new PubMed database was searched for articles published between 2015 and 2020 with the following key search terms: precision medicine, genomic, pharmacogenetic, pharmacogenomic, US military, and Department of Defense. Results Eighty-one articles were retrieved in our initial search. After screening the abstracts for studies that only involved direct testing of (or clinical interaction with) active duty, Reserve, National Guard, or civilian personnel working within the DoD and excluding any epidemiological or microbial isolation studies, seven were included in this review. Conclusion There are several programs and studies within the DoD, which investigate or use gene-based biomarkers or gene variants to deliver more precise clinical assessment and treatment. These genome-based precision medicine efforts aim to optimize the clinical care of DoD beneficiaries, particularly service members in the operational environment.

Urine as a non-invasive alternative to blood for germline and somatic mutation detection in hepatocellular carcinoma

Cell-free DNA (cfDNA) from blood has become a promising analyte for cancer genetic liquid biopsy. Urinary cfDNA has been shown to contain mutations associated with non-genitourologic cancers including hepatocellular carcinoma (HCC). In this study, we evaluate urine as a noninvasive alternative to blood-based liquid biopsy in both germline and circulating tumor DNA (ctDNA) genotyping in HCC. Using quantitative PCR (qPCR), whole-genome sequencing (WGS), and targeted NGS, DNA isolated from blood or urine of patients with HCC was analyzed for overall genome coverage, HCC hotspot coverage, and germline or somatic mutation concordance. Targeted NGS of plasma and urine cfDNA was also performed for detection of somatic variants. We found urine cfDNA, similar to plasma cfDNA, showed a major mononucleosomal species of 150-180 bp in both healthy individuals and patients with HCC. By WGS, overall genome coverage breadth was similar between urine and plasma cfDNA, with higher fraction of covered cancer-associated mutation hotspots in urine cfDNA. qPCR analyses of HCC-associated mutations (TP53, CTNNB1, and TERT) in 101 patients with HCC revealed 78% overall concordance between plasma and urine. Targeted NGS of HCC-associated gene regions in additional 15 HCC patients showed a 97% overall position-level concordance between plasma and urine cfDNA. Collectively, urine DNA can potentially be used as a completely noninvasive liquid biopsy for HCC.Significance StatementHepatocellular carcinoma (HCC) is the most common liver cancer worldwide and the fastest growing gastrointestinal cancer in the U.S. Cell-free DNA (cfDNA) which originates from various cells undergoing apoptosis or necrosis including tumor cells, is present in all body fluids levels including urine. Urinary cfDNA isolated from patients with HCC showed a similar fragment size distribution, overall genome coverage, and comparable sensitivity for detecting HCC-associated variants compared to plasma cfDNA. Urine was also determined to be a reliable source of germline genotype information, similar to peripheral blood mononuclear cells in blood-based liquid biopsies. Urine cfDNA can be used as a completely non-invasive liquid biopsy in HCC.

Probabilistic Genotyping of Single Cell Replicates from Complex DNA Mixtures Recovers Higher Contributor LRs than Standard Analysis

DNA mixtures are a common source of crime scene evidence and are often one of the more difficult sources of biological evidence to interpret. With the implementation of probabilistic genotyping (PG), mixture analysis has been revolutionized allowing previously unresolvable mixed profiles to be analyzed and probative genotype information from contributors to be recovered. However, due to allele overlap, artifacts, or low-level minor contributors, genotype information loss inevitably occurs. In order to reduce the potential loss of significant DNA information from donors in complex mixtures, an alternative approach is to physically separate individual cells from mixtures prior to performing DNA typing thus obtaining single source profiles from contributors. In the present work, a simplified micro-manipulation technique combined with enhanced single-cell DNA typing was used to collect one or few cells, referred to as direct single-cell subsampling (DSCS). Using this approach, single and 2-cell subsamples were collected from 2-6 person mixtures. Single-cell subsamples resulted in single source DNA profiles while the 2-cell subsamples returned either single source DNA profiles or new mini-mixtures that are less complex than the original mixture due to the presence of fewer contributors. PG (STRmixTM) was implemented, after appropriate validation, to analyze the original bulk mixtures, single source cell subsamples, and the 2-cell mini mixture subsamples from the original 2-6-person mixtures. PG further allowed replicate analysis to be employed which, in many instances, resulted in a significant gain of genotype information such that the returned donor likelihood ratios (LRs) were comparable to that seen in their single source reference profiles (i.e., the reciprocal of their random match probabilities). In every mixture, the DSCS approach gave improved results for each donor compared to standard bulk mixture analysis. With the 5- and 6- person complex mixtures, DSCS recovered highly probative LRs (> 1020) from donors that had returned non-probative LRs (<103) by standard methods.

Smoking and subfertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study

Objective. To investigate the association between smoking-related traits and subfertility. Design. Prospective study. Setting. Nationwide cohort in Norway. Patients. 28,606 women (average age 30) and 27,096 men (average age 33) with questionnaire and genotype information from the Norwegian Mother, Father and Child Cohort Study. Intervention. Self-reported information on smoking (having ever smoked [both sexes], age at smoking initiation [women only], smoking cessation [women only], and cigarettes smoked per week in current smokers [both sexes]) was gathered. Genetically predetermined levels or likelihood of presenting the mentioned traits were estimated for Mendelian randomization (MR) analyses. Main outcome measure. Subfertility, defined as time-to-pregnancy >=12 months. Results. A total of 10% of couples were subfertile. In multivariable regression accounting for age, years of education, body mass index, and number of previous pregnancies, having ever smoked was not linked to subfertility in women or men. A higher intensity of tobacco use in women who were current smokers was related to greater odds of subfertility (+ 1 standard deviation [SD, 48 cigarettes/week]: odds ratio [OR] 1.12, 95% confidence interval [CI] 1.03 to 1.21), also after adjusting for the partner's tobacco use. Later smoking initiation (+ 1 SD [3.2 years]: OR 0.89, 95% CI 0.84 to 0.95) and smoking cessation (relative to not quitting: OR 0.83, 95% CI 0.75 to 0.93) were linked to decreased subfertility in women who had ever smoked. Nevertheless, MR results were not directionally consistent for smoking intensity and cessation and were imprecisely estimated in two-sample MR, with wide confidence intervals that overlapped with the multivariable regression results. In men, greater smoking intensity was marginally linked to greater odds of subfertility in multivariable analyses, but this association was attenuated when adjusting for the partner's smoking intensity (+ 1 SD [54 cigarettes/week]: OR 1.05, 95% CI 0.96 to 1.15). MR estimates were directionally consistent but again imprecisely estimated. Conclusions. We did not find robust evidence of an effect of smoking on subfertility. This may be due to a true lack of effect, weak genetic instruments, or other kinds of confounding. The relevant limitations across all methods highlights the need for larger studies with information on subfertility.

Comparative Signatures of Selection Analyses Identify Loci Under Positive Selection in the Murrah Buffalo of India

India is home to a large and diverse buffalo population. The Murrah breed of North India is known for its milk production, and it has been used in breeding programs in several countries. Selection signature analysis yield valuable information about how the natural and artificial selective pressures have shaped the genomic landscape of modern-day livestock species. Genotype information was generated on six buffalo breeds of India, namely, Murrah, Bhadawari, Mehsana, Pandharpuri, Surti, and Toda using ddRAD sequencing protocol. Initially, the genotypes were used to carry out population diversity and structure analysis among the six breeds, followed by pair-wise comparisons of Murrah with the other five breeds through XP-EHH and FST methodologies to identify regions under selection in Murrah. Admixture results showed significant levels of Murrah inheritance in all the breeds except Pandharpuri. The selection signature analysis revealed six regions in Murrah, which were identified in more than one pair-wise comparison through both XP-EHH and FST analyses. The significant regions overlapped with QTLs for milk production, immunity, and body development traits. Genes present in these regions included SLC37A1, PDE9A, PPBP, CXCL6, RASSF6, AFM, AFP, ALB, ANKRD17, CNTNAP2, GPC5, MYLK3, and GPT2. These genes emerged as candidates for future polymorphism studies of adaptability and performance traits in buffaloes. The results also suggested ddRAD sequencing as a useful cost-effective alternative for whole-genome sequencing to carry out diversity analysis and discover selection signatures in Indian buffalo breeds.

A Propensity-Matched Nested Case-Control Study of Acute Coronary Syndrome Patients Genotyped for CYP2C19

Introduction Ticagrelor is widely considered superior to clopidogrel however a pharmacogenetic substudy of PLATO indicated that the majority of this difference is due to genetic nonresponders to clopidogrel. We evaluated patient outcomes following genotyping for CYP2C19 in a propensity matched acute coronary syndrome cohort treated with either clopidogrel, ticagrelor or aspirin monotherapy. Methods ICD10 coding identified 6,985 acute coronary syndrome patients at Waitemata District Health Board over a five year period (2012-2016). Ticagrelor was subsidised by The Pharmaceutical Management Agency of New Zealand in July 2013. Patients were genotyped for CYP2C19 *2, *3 and *17 alleles using the Nanosphere Verigene analyser and treatment was tailored accordingly. Logistic regression and nearest neighbour propensity matching was employed in a 1:3 fashion with each treatment group to balance patient characteristics. Results A total of 146 patients were genotyped and compared with 438 matched patients taking either clopidogrel, ticagrelor or aspirin monotherapy. Post July 2013 clopidogrel was prescribed more often in responders than in those without genotype information (68 vs 39%, X2 9, 95% CI 4 to 34, p=0.003). Conversely, ticagrelor was used more frequently in clopidogrel nonresponders. Mortality with personalised treatment was equivalent to ticagrelor (HR 0.8, 95% CI 0.3 to 1.8) but higher in those treated with clopidogrel (HR 2.3, 95 % CI 1 to 5.3). Readmissions with ACS were higher in nonresponders treated with clopidogrel versus those treated with genotype appropriate dual antiplatelet therapy (HR 3.9, 95% CI 0.8 to 18, p =0.03). Conclusion Personalised antiplatelet management was equivalent to ticagrelor with respect to all-cause mortality and ACS readmissions. It also led to more appropriate use of both clopidogrel and ticagrelor and potential cost savings.

Predictive Prevalence of Risk Phenotypes Associated With rs2285666 Single Nucleotide Polymorphism of ACE2 in the World Population Susceptible to SARS-CoV-2 Infection

Variability of ACE2 expression encoded by the ACE2 gene may be important for susceptibility and clinical outcomes of SARS-CoV-2 infection. This study was aimed to identify potential single nucleotide polymorphisms (SNPs) of ACE2 relevant to SARS-CoV-2 infection and predictively assigned risk phenotypes. Allele and genotype information of rs2285666 SNP of ACE2 was obtained from the 1000 Genomes project Phase III in line with Fort Lauderdale principles. About 16 SNPs of ACE2 as potential venture for susceptibility to SARS-CoV-2 infection was identified from the literature. Predicted high-risk phenotypes of ACE2 expressor due to carrying rs2285666 SNP of ACE2 was highly prevalent in East Asia (40.7%; 95% CI 36%-45%), followed by South Asia (36.8%; 95% CI 33%-41%), America (22.8%; 95% CI 18%-27%), Europe (14.5%; 95% CI 11%-18%) and Africa (12.3%; 95% CI 10%-15%), respectively. In total, ~25% of the world populations were predictively identified as being at high-risk for SARS-CoV-2 infection due to carrying rs2285666 ACE2 genetic polymorphism. Identification of high-risk phenotypes for SARS-CoV-2 infection through screening of ACE2 genetic polymorphisms may be valuable for SARS-CoV-2- related COVID-19 prevention and treatment in the population. Customized DNA microarray techniques or next generation sequencing may holistically advance this newly evolving research area of infection genetics.

Mendelian randomization analysis of arsenic metabolism and pulmonary function within the Hispanic Community Health Study/Study of Latinos

Arsenic exposure has been linked to poor pulmonary function, and inefficient arsenic metabolizers may be at increased risk. Dietary rice has recently been identified as a possible substantial route of exposure to arsenic, and it remains unknown whether it can provide a sufficient level of exposure to affect pulmonary function in inefficient metabolizers. Within 12,609 participants of HCHS/SOL, asthma diagnoses and spirometry-based measures of pulmonary function were assessed, and rice consumption was inferred from grain intake via a food frequency questionnaire. After stratifying by smoking history, the relationship between arsenic metabolism efficiency [percentages of inorganic arsenic (%iAs), monomethylarsenate (%MMA), and dimethylarsinate (%DMA) species in urine] and the measures of pulmonary function were estimated in a two-sample Mendelian randomization approach (genotype information from an Illumina HumanOmni2.5-8v1-1 array), focusing on participants with high inferred rice consumption. Among never-smoking high inferred consumers of rice (n = 1395), inefficient metabolism was associated with past asthma diagnosis and forced vital capacity below the lower limit of normal (LLN) (OR 1.40, p = 0.0212 and OR 1.42, p = 0.0072, respectively, for each percentage-point increase in %iAs; OR 1.26, p = 0.0240 and OR 1.24, p = 0.0193 for %MMA; OR 0.87, p = 0.0209 and OR 0.87, p = 0.0123 for the marker of efficient metabolism, %DMA). Among ever-smoking high inferred consumers of rice (n = 1127), inefficient metabolism was associated with peak expiratory flow below LLN (OR 1.54, p = 0.0108/percentage-point increase in %iAs, OR 1.37, p = 0.0097 for %MMA, and OR 0.83, p = 0.0093 for %DMA). Less efficient arsenic metabolism was associated with indicators of pulmonary dysfunction among those with high inferred rice consumption, suggesting that reductions in dietary arsenic could improve respiratory health.