Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models.

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or LD-based summary statistics perform well in estimating selection.

BRCA 1/BRCA 2 Pathogenic/Likely Pathogenic Variant Patients with Breast, Ovarian, and Other Cancers.

The demographic and clinical characteristics of patients who have BRCA 1/BRCA 2 pathogenic/likely pathogenic variants may differ from their relatives who had BRCA-related cancer. In this study, we aimed to demonstrate the clinical and demographic findings of patients who had BRCA-related cancer and to assess the differences comparing their relatives who had BRCA-related cancer with breast, genital tract, prostate, and pancreas cancers as well. The results of sequencing analysis of 200 cancer patients (190 women, 10 men) who have been directed to genetic counseling with an indication of BRCA1/BRCA2 testing from different regions across 9 medical oncology centers were retrospectively analyzed. A total of 200 consecutive cancer patients who harbored the BRCA1/BRCA2 pathogenic/likely pathogenic variant (130 (65%) patients harbored BRCA 1 pathogenic/likely pathogenic variant, and 70 harbored BRCA 2 pathogenic/likely pathogenic variant) were included. Of these, 64.0% had breast cancer (43.8% of them had the triple-negative disease, and about 2.3% had only the HER-2 mutant), 31.5% had genital cancers (92.1% of them had ovarian cancer, 3.2% had endometrium, and 1.6% had peritoneum cancer as the primary site and mostly serous adenocarcinoma was the most common histopathology and 14.3% of the patients had endometrioid adenocarcinoma), 3.5% had prostate (median time from metastasis to castration-resistant status was 28 months) and 1.0% had pancreas cancer. Newly diagnosed cancer (breast and ovary) patients who had BRCA 1/BRCA 2 pathogenic/ likely pathogenic variant were younger than their previous cancer diagnosed (breast, ovary, and pancreas) parents who harbored BRCA pathogenic/likely pathogenic variant. We suggest that the genetic screening of BRCA 1/ BRCA 2 pathogenic/likely pathogenic variant is needed as a routine screening for those with a personal or family history of breast, ovarian, tubal, or peritoneal cancer. In addition, once BRCA 1 or BRCA 2 germline pathogenic variant has been identified in a family, testing of at-risk next-generation relatives earlier can identify those family members who also have the familial pathogenic variant, and thus need increased surveillance.

The logical structure of experiments lays the foundation for a theory of reproducibility.

The scientific reform movement has proposed openness as a potential remedy to the putative reproducibility or replication crisis. However, the conceptual relationship among openness, replication experiments and results reproducibility has been obscure. We analyse the logical structure of experiments, define the mathematical notion of idealized experiment and use this notion to advance a theory of reproducibility. Idealized experiments clearly delineate the concepts of replication and results reproducibility, and capture key differences with precision, allowing us to study the relationship among them. We show how results reproducibility varies as a function of the elements of an idealized experiment, the true data-generating mechanism, and the closeness of the replication experiment to an original experiment. We clarify how openness of experiments is related to designing informative replication experiments and to obtaining reproducible results. With formal backing and evidence, we argue that the current 'crisis' reflects inadequate attention to a theoretical understanding of results reproducibility.

Rigorous exploration in a model-centric science via epistemic iteration.

Urgent attention is needed to address generalizability problems in psychology. However, the current dominant paradigm centered on dichotomous results and rapid discoveries cannot provide the solution because of its theoretical inadequacies. We propose a paradigm shift towards a model-centric science, which provides the sophistication to understanding the sources of generalizability and promote systematic exploration. In a model-centric paradigm, scientific activity involves iteratively building and refining theoretical, empirical, and statistical models that communicate with each other. This approach is transparent, and efficient in addressing generalizability issues. We illustrate the nature of scientific activity in a model-centric system and its potential for advancing the field of psychology.

Acute effect of thymoquinone on action potential and ionic currents of rat cardiac myocytes.

OBJECTIVES: This study aims to investigate the acute effects of thymoquinone (TQ) which has been suggested to be a cardioprotective agent, on ventricular myocytes. METHODS: Freshly isolated rat ventricular myocytes were exposed to TQ and using standard whole-cell patch-clamp technique action potential (AP), sodium current (INa), L-type calcium current (ICaL) and transient outward potassium current (Ito) were measured. RESULTS: TQ prolonged the duration of AP and decreased the peak value compared to that of control myocytes. Consistently, it inhibited INa in a concentration-dependent manner and shifted the channel kinetics to more hyperpolarized voltages. Besides, TQ not only inhibited Ito and ICaL but also significantly attenuated the isoproterenol-induced increase in ICaL. CONCLUSION: The effect of TQ on cardiomyocytes has been demonstrated for the first time. TQ changes AP morphology along with ionic currents and alleviates ß-adrenergic response in adult ventricular myocytes. These results indicate that TQ may be considered as a therapeutic agent in cases such as diabetic cardiomyopathy and cardiac hypertrophy, wherein the ß-adrenergic system is over-activated (Tab. 2, Fig. 6, Ref. 30).

Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps.

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.

AABC: approximate approximate Bayesian computation for inference in population-genetic models.

Approximate Bayesian computation (ABC) methods perform inference on model-specific parameters of mechanistically motivated parametric models when evaluating likelihoods is difficult. Central to the success of ABC methods, which have been used frequently in biology, is computationally inexpensive simulation of data sets from the parametric model of interest. However, when simulating data sets from a model is so computationally expensive that the posterior distribution of parameters cannot be adequately sampled by ABC, inference is not straightforward. We present "approximate approximate Bayesian computation" (AABC), a class of computationally fast inference methods that extends ABC to models in which simulating data is expensive. In AABC, we first simulate a number of data sets small enough to be computationally feasible to simulate from the parametric model. Conditional on these data sets, we use a statistical model that approximates the correct parametric model and enables efficient simulation of a large number of data sets. We show that under mild assumptions, the posterior distribution obtained by AABC converges to the posterior distribution obtained by ABC, as the number of data sets simulated from the parametric model and the sample size of the observed data set increase. We demonstrate the performance of AABC on a population-genetic model of natural selection, as well as on a model of the admixture history of hybrid populations. This latter example illustrates how, in population genetics, AABC is of particular utility in scenarios that rely on conceptually straightforward but potentially slow forward-in-time simulations.

Evaluation of intrarater and interrater reliability of the Wisconsin Gait Scale with using the video taped stroke patients in a Turkish sample.

OBJECTIVE: To establish the intrarater and interrater reliability of Wisconsin Gait Scale (WGS) in hemiplegic patients. DESIGN: Repeated-measures reliability study using video data of stroke patients. SETTING: Rehabilitation department of the university hospital. PARTICIPITANTS: Nineteen hemiplegic patients with 3-9 months stroke history and two physiatrists and two physical therapists. INTERVENTIONS: Video recordings were assessed twice, at an interval of 2 days, by the two physiatrists and two physical therapists. MAIN OUTCOME MEASURE: Wisconsin Gait Scale. RESULTS: Internal consistency coefficients for the WGS were excellent; Cronbach scores were 0.91 and 0.94 for the first and third days. Coefficient of Repeatability (CR) for observers' WGS assessments were ranged between 4.23-5.76 and intraclass correlation coefficients for total WGS score were indicated very high interrater reliability at the begining and end, respectively 0.91 and 0.96. Intraclass correlation coefficients for fourteen items of WGS ranged from 0.81 to 1. "Hip hiking at mid-swing", "Circumduction at mid-swing" and "Hip extension of the affected leg" were the items with lowest correlation coefficients. Intrarater reliability for total WGS scores ranged from 0.75 to 0.90. CONCLUSION: WGS was found excellent in reliability and may provide an objective means to document the findings from observational gait analysis, which is frequently used in clinical practice by rehabilitation teams.

Genotype imputation in a coalescent model with infinitely-many-sites mutation.

Empirical studies have identified population-genetic factors as important determinants of the properties of genotype-imputation accuracy in imputation-based disease association studies. Here, we develop a simple coalescent model of three sequences that we use to explore the theoretical basis for the influence of these factors on genotype-imputation accuracy, under the assumption of infinitely-many-sites mutation. Employing a demographic model in which two populations diverged at a given time in the past, we derive the approximate expectation and variance of imputation accuracy in a study sequence sampled from one of the two populations, choosing between two reference sequences, one sampled from the same population as the study sequence and the other sampled from the other population. We show that, under this model, imputation accuracy-as measured by the proportion of polymorphic sites that are imputed correctly in the study sequence-increases in expectation with the mutation rate, the proportion of the markers in a chromosomal region that are genotyped, and the time to divergence between the study and reference populations. Each of these effects derives largely from an increase in information available for determining the reference sequence that is genetically most similar to the sequence targeted for imputation. We analyze as a function of divergence time the expected gain in imputation accuracy in the target using a reference sequence from the same population as the target rather than from the other population. Together with a growing body of empirical investigations of genotype imputation in diverse human populations, our modeling framework lays a foundation for extending imputation techniques to novel populations that have not yet been extensively examined.

A pilot study of 2 years of interferon treatment in patients with chronic delta hepatitis.

High dose interferon treatment for 1 year is the only established treatment for chronic hepatitis D, but it is associated with a high relapse rate after treatment discontinuation. In this study, patients were treated with 10 MU interferon alpha 2b, thrice weekly for 2 years. Twenty-three patients were recruited and 15 completed the 2-year treatment and 6 months follow-up periods. Treatment response was assessed biochemically [normal alanine aminotransferase (ALT)], virologically (undetectable hepatitis D virus RNA) and histologically (at least 2 point decrease in the Knodell score) at the end of treatment (EOT) and at the end of follow-up. Out of 15 patients who finished the 2-year treatment period, seven patients (47%) had a biochemical response but only two (13%) had a normal ALT after follow-up. ALT decreased from the baseline value of 143.1 +/- 121.7 (mean +/- SD) to 39.7 +/- 20.6 (P < 0.01) at EOT. Virological response was observed in six patients at EOT and in two patients at follow-up. Two patients lost hepatitis B surface antigen. Of the 12 patients with paired liver biopsies, a histological improvement was observed in eight patients. Interferon treatment leads to a complete or partial response in a substantial number of patients but 2 years of treatment does not appear to increase sustained response rates over 1 year treatment.

Lamivudine vs lamivudine and interferon combination treatment of HBeAg(-) chronic hepatitis B.

To determine whether combination treatment of HBeAg(-) chronic hepatitis B is beneficial we studied 78 patients with HBeAg(-), HBV DNA-positive chronic hepatitis B who were randomized to lamivudine, 100 mg, qd, for 12 months or lamivudine-interferon (9 MU, t.i.w.) in combination. In the combination arm, 2 months of lamivudine treatment preceded 10 months of combination treatment. Biochemical, virologic and histologic responses were assessed at the end of treatment, after six and a median 27 months of drug-free follow-up (short- and long-term follow-up, respectively). Virologic response was defined as undetectable HBV DNA with a hybridization assay and biochemical response as normal alanine aminotransferase (ALT). Change in HBV DNA was also assessed by real-time polymerase chain reaction (PCR). Presence of YMDD mutants at the end of treatment was investigated with a line probe assay. Both treatment regimes led to a median 2 log decline in HBV DNA levels. Virologic end of treatment responses were 90 and 92% with mono- and combination treatment, respectively. Corresponding virologic responses at short- and long-term follow-up were 59 and 54%, and 27 and 25%, respectively. Patients having a baseline HBV DNA value > or =200 pg/mL were more likely to relapse within 6 months off therapy than those patients with a baseline HBV DNA level <200 pg/mL (P = 0.041). YMDD mutants were observed in 53% of patients receiving lamivudine compared with 24% of patients receiving the combination regime (P = 0.017). In conclusion, efficacy of combination treatment is similar to lamivudine monotherapy. However, combination treatment decreases the development of YMDD mutant strains compared with lamivudine monotherapy.

Molecular epidemiology of hepatitis B, C and D viruses in Turkish patients.

Different genotypes of the hepatitis viruses may influence the clinical outcome of the disease. The distribution of genotypes may vary according to geographical regions. The aim of this study was to evaluate hepatitis B virus (HBV), hepatitis C virus (HCV) and hepatitis D virus (HDV) genotypes in Turkish patients with chronic hepatitis in a large cohort of patients. Genotyping was performed in 41, 59 and 365 patients with chronic hepatitis B, D and C, respectively, and 36 hemodialysis patients with chronic hepatitis C. Genotypes were determined by direct sequencing in hepatitis B and by polymerase chain reaction-restriction fragment length polymorphism in hepatitis C and D patients. In addition, HBV subtyping by multiplex PCR and subtype specific ELISA were performed in 83 and 71 HBsAg (+) blood donors, respectively. All hepatitis B (100%) and hepatitis D (100%) patients had genotype D and type I, respectively. HBsAg subtyping by two methods yielded that 99% of the patients were subtype ayw. S gene amino acid sequence in the 41 patients included for HBV genotyping revealed the ayw2 subtype. Genotype distribution of 365 patients with chronic C hepatitis were as follows: 306 (84%) patients genotype 1b, 43 (11%) patients genotype 1a, 10 (3%) patients genotype 2, 3 (1%) patients genotype 3, 3 (1%) patients genotype 4. Among 36 patients receiving hemodialysis, 28 (78%) patients had genotype 1b and 8 (22%) patients had genotype 1a. The study indicates that Turkish patients with chronic viral hepatitis show very little genotypic heterogeneity. Subtype ayw and the genotype D of HBV DNA, and the type I of HDV RNA represent almost 100% of related infections. The genotype 1b of HCV RNA was found to be significantly dominant in Turkish patients.