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In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimates linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

IntroductionEnd-stage renal disease (ESRD) patients with a paradoxical increase in blood pressure (BP) during hemodialysis (HD), termed intradialytic hypertension (ID-HTN), are at significantly increased risk for mortality and adverse cardiovascular events. ID-HTN affects up to 15% of all HD patients, and the pathophysiologic mechanisms remain unknown. We hypothesized that ESRD patients prone to ID-HTN have heightened volume-sensitive cardiopulmonary baroreflex sensitivity (BRS) that leads to exaggerated increases in sympathetic nervous system (SNS) activation during HD.MethodsWe studied ESRD patients on maintenance HD with ID-HTN (n = 10) and without ID-HTN (controls, n = 12) on an interdialytic day, 24 to 30 hours after their last HD session. We measured continuous muscle sympathetic nerve activity (MSNA), beat-to-beat arterial BP, and electrocardiography (ECG) at baseline, and during graded lower body negative pressure (LBNP). Low-dose LBNP isolates cardiopulmonary BRS, whereas higher doses allow assessment of physiologic responses to orthostatic stress.ResultsThe ID-HTN patients had significantly higher pre- and post-HD BP, and greater interdialytic fluid weight gain compared to controls. There was a significantly greater increase in MSNA burst incidence (P = 0.044) during graded LBNP in the ID-HTN group, suggesting heightened cardiopulmonary BRS. The ID-HTN group also had a trend toward increased diastolic BP response during LBNP, and had significantly greater increases in BP during the cold pressor test.ConclusionPatients with ID-HTN have augmented cardiopulmonary BRS that may contribute to increased SNS activation and BP response during HD.

Introduction: According to the common disease/rare variant hypothesis, it is important to study the role of rare variants in complex diseases. The association of rare variants with psoriasis has been demonstrated, but the association between rare variants and specific clinical subtypes of psoriasis has not been investigated. Methods: Gene-based and gene-level meta-analyses were performed on data extracted from our previous study data sets (2,483 patients with guttate psoriasis and 8,292 patients with non-guttate psoriasis) for genotyping. Then, haplotype analysis was performed for rare loss-of-function variants located in MED12L, and protein function prediction was performed for MED12L. Gene-based analysis at each stage had a moderate significance threshold (p < 0.05). A χ2 test was then conducted on the three potential genes, and the merged gene-based analysis was used to confirm the results. We also conducted association analysis and meta-analysis for functional variants located on the identified gene. Results: Through these gene-level analyses, we determined that MED12L is a guttate psoriasis susceptibility gene (p = 9.99 × 10−5), and the single-nucleotide polymorphism with the strongest association was rs199780529 (p_combine = 1 × 10−3, p_meta = 2 × 10−3). Conclusions: In our study, a guttate psoriasis-specific subtype-associated susceptibility gene was confirmed in a Chinese Han population. These findings contribute to a better genetic understanding of different subtypes of psoriasis. [ABSTRACT FROM AUTHOR]

The use of Monte‐Carlo (MC) p$$ p $$‐values when testing the significance of a large number of hypotheses is now commonplace. In large‐scale hypothesis testing, we will typically encounter at least somep$$ p $$‐values near the threshold of significance, which require a larger number of MC replicates than p$$ p $$‐values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 106$$ 1{0}^6 $$) of MC replicates may not be enough to guarantee conclusions reached using MC p$$ p $$‐values. Gandy and Hahn (GH)6‐8 have developed the only method that directly addresses this problem. They defined a Monte‐Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p$$ p $$‐values are different from decisions based on idealp$$ p $$‐values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large‐scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p$$ p $$‐values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study. [ABSTRACT FROM AUTHOR]