Dating rare mutations from small samples with dense marker data.
Ontology highlight
ABSTRACT:
SUBMITTER: Gandolfo LC
PROVIDER: S-EPMC4125402 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Publications
Dating rare mutations from small samples with dense marker data.
Gandolfo Luke C LC Bahlo Melanie M Speed Terence P TP
Genetics 20140530 4
We present a method for estimating the age of a mutation based on the genetic length of ancestral haplotypes shared between individuals carrying the mutation. The method can be reliably applied to small samples, typical of situations involving rare mutations, and makes effective use of modern high-density SNP data, thus overcoming two of the limitations with existing methods. The method provides age estimates and confidence intervals without the use of asymptotic theory and is applicable to gene ...[more]
Similar Datasets
Project description:Genome wide association studies have been usually analyzed in a univariate manner. The commonly used univariate tests have one degree of freedom and assume an additive mode of inheritance. The experiment-wise significance of these univariate statistics is obtained by adjusting for multiple testing. Next generation sequencing studies, which assay 10-20 million variants, are beginning to come online. For these studies, the strategy of additive univariate testing and multiple testing adjustment is likely to result in a loss of power due to (1) the substantial multiple testing burden and (2) the possibility of a non-additive causal mode of inheritance. To reduce the power loss we propose: a new method (1) to summarize in a single statistic the strength of the association signals coming from all not-very-rare variants in a linkage disequilibrium block and (2) to incorporate, in any linkage disequilibrium block statistic, the strength of the association signals under multiple modes of inheritance. The proposed linkage disequilibrium block test consists of the sum of squares of nominally significant univariate statistics. We compare the performance of this method to the performance of existing linkage disequilibrium block/gene-based methods. Simulations show that (1) extending methods to combine testing for multiple modes of inheritance leads to substantial power gains, especially for a recessive mode of inheritance, and (2) the proposed method has a good overall performance. Based on simulation results, we provide practical advice on choosing suitable methods for applied analyses.
| S-EPMC3827222 | biostudies-literature
Project description:The impetus behind this study is to understand the sedimentological dynamics of very young fluvial systems in the Amazon River catchment and relate these to land use change and modern analogue studies of tidal rhythmites in the geologic record. Initial quartz optically stimulated luminescence (OSL) dating feasibility studies have concentrated on spit and bar deposits in the Rio Tapajós. Many of these features have an appearance of freshly deposited pristine sand, and these observations and information from anecdotal evidence and LandSat imagery suggest an apparent decadal stability. The characteristics of OSL from small (~5 cm) sub-samples from ~65 cm by ~2 cm diameter vertical cores are quite remarkable. Signals from medium-sized aliquots (5 mm diameter) exhibit very high specific luminescence sensitivity, have excellent dose recovery and recycling, essentially independent of preheat, and show minimal heat transfer even at the highest preheats. These characteristics enable measurement of very small signals with reasonable precision and, using modified single-aliquot regenerative-dose (SAR) approaches, equivalent doses as low as ~4 mGy can be obtained. Significant recuperation is observed for samples from two of the study sites and, in these instances, either the acceptance threshold was increased or growth curves were forced through the origin; recuperation is considered most likely to be a measurement artefact given the very small size of natural signals. Dose rates calculated from combined inductively coupled plasma mass spectrometry/inductively coupled plasma optical emission spectrometry (ICP-MS/ICP-OES) and high-resolution gamma spectrometry range from ~0.3 to 0.5 mGya-1, and OSL ages for features so far investigated range from 13 to 34 years to several 100 years. Sampled sands are rich in quartz and yields of 212-250 μm or 250-310 μm grains indicate high-resolution sampling at 1-2 cm intervals is possible. Despite the use of medium-sized aliquots to ensure the recovery of very dim natural OSL signals, these results demonstrate the potential of OSL for studying very young active fluvial processes in these settings.
| S-EPMC6961124 | biostudies-literature
Project description:Ocean ventilation is the integrated effect of various processes that exchange surface properties with the ocean interior and is essential for oxygen supply, storage of anthropogenic carbon and the heat budget of the ocean, for instance. Current observational methods utilise transient tracers, e.g. tritium, SF6, CFCs and 14C. However, their dating ranges are not ideal to resolve the centennial-dynamics of the deep ocean, a gap filled by the noble gas isotope 39Ar with a half-life of 269 years. Its broad application has been hindered by its very low abundance, requiring 1000 L of water for dating. Here we show successful 39Ar dating with 5 L of water based on the atom-optical technique Atom Trap Trace Analysis. Our data reveal previously not quantifiable ventilation patterns in the Tropical Atlantic, where we find that advection is more important for the ventilation of the intermediate depth range than previously assumed. Now, the demonstrated analytical capabilities allow for a global collection of 39Ar data, which will have significant impact on our ability to quantify ocean ventilation.
| S-EPMC6261945 | biostudies-literature
Project description:Chemical mutagenesis is routinely used to create large numbers of rare mutations in plant and animal populations, which can be subsequently subjected to selection for beneficial traits and phenotypes that enable the characterization of gene functions. Several next-generation sequencing (NGS)-based target enrichment methods have been developed for the detection of mutations in target DNA regions. However, most of these methods aim to sequence a large number of target regions from a small number of individuals. Here, we demonstrate an effective and affordable strategy for the discovery of rare mutations in a large sodium azide-induced mutant rice population (F2 ). The integration of multiplex, semi-nested PCR combined with NGS library construction allowed for the amplification of multiple target DNA fragments for sequencing. The 8 × 8 × 8 tridimensional DNA sample pooling strategy enabled us to obtain DNA sequences of 512 individuals while only sequencing 24 samples. A stepwise filtering procedure was then elaborated to eliminate most of the false positives expected to arise through sequencing error, and the application of a simple Student's t-test against position-prone error allowed for the discovery of 16 mutations from 36 enriched targeted DNA fragments of 1024 mutagenized rice plants, all without any false calls.
| S-EPMC4265296 | biostudies-literature
Project description:The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM).In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18).We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.
| S-EPMC4940283 | biostudies-literature
Project description:Antiretroviral hair levels objectively quantify drug exposure over time and predict virologic responses. We assessed the acceptability and feasibility of collecting small hair samples in a rural Kenyan cohort. Ninety-five percentage of participants (354/373) donated hair. Although median self-reported adherence was 100% (interquartile range, 96%-100%), a wide range of hair concentrations likely indicates overestimation of self-reported adherence and the advantages of a pharmacologic adherence measure. Higher nevirapine hair concentrations observed in women and older adults require further study to unravel behavioral versus pharmacokinetic contributors. In resource-limited settings, hair antiretroviral levels may serve as a low-cost quantitative biomarker of adherence.
| S-EPMC4146734 | biostudies-literature
Project description:Information about the geological timeframe during which animals radiated into their major subclades is crucial to understanding early animal ecology and evolution. Unfortunately, the pre-Cambrian fossil record is sparse and its interpretation controversial. Relaxed molecular-clock methods provide an alternative means of estimating the timing of cladogenesis deep in the metazoan tree of life. So far, thorough molecular clock studies focusing specifically on Metazoa as a whole have been based on relatively small datasets or incomplete representation of the main non-bilaterian lineages (such as sponges and ctenophores), which are fundamental for understanding early metazoan evolution. Here, we use a previously published phylogenomic dataset that includes a fair sampling of all relevant groups to estimate the timing of early animal evolution with Bayesian relaxed-clock methods. According to our results, all non-bilaterian phyla, as well as total-group Bilateria, evolved in an ancient radiation during a geologically relatively short time span, before the onset of long-term global glaciations ("Snowball Earth"; ~720-635 Ma). Importantly, this result appears robust to alterations of a number of important analytical variables, such as models of among-lineage rate variation and sets of fossil calibrations used.
| S-EPMC5472626 | biostudies-literature
Project description:Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.
| S-EPMC7969809 | biostudies-literature
Project description:Epidermal growth factor receptor (EGFR) mutations are the second most common oncogenic driver event in non-small cell lung cancer (NSCLC). Classical activating mutations (exon 19 deletions and the L858R point mutation) comprise the vast majority of EGFR mutations and are well defined as strong predictors for good clinical response to EGFR tyrosine kinase inhibitors (EGFRi). However, low frequency mutations including point mutations, deletions, insertions and duplications occur within exons 18-25 of the EGFR gene in NSCLC and are associated with poorer responses to EGFRi. Despite an increased uptake of more sensitive detection methods to identify rare EGFR mutations in patients, our understanding of the biology of these rare EGFR mutations is poor compared to classical mutations. In particular, clinical data focused on these mutations is lacking due to their rarity and challenges in trial recruitment, resulting in an absence of effective treatment strategies for many low frequency EGFR mutations. In this review, we describe the structural and mechanistic features of rare EGFR mutations in NSCLC and discuss the preclinical and clinical evidence for EGFRi response for individual rare EGFR mutations. We also discuss EGFRi sensitivity for complex EGFR mutations, and conclude by offering a perspective on the outstanding questions and future steps required to make advances in the treatment of NSCLC patients that harbour rare EGFR mutations.
| S-EPMC7083237 | biostudies-literature
Project description:Leveraging a massive dataset of over 421 million potential matches between single users on a leading mobile dating application, we were able to identify numerous characteristics of effective matching. Effective matching is defined as the exchange of contact information with the likely intent to meet in person. The characteristics of effective match include alignment of psychological traits (i.e., extroversion), physical traits (i.e., height), personal choices (i.e., desiring the same relationship type), and shared experiences. For nearly all characteristics, the more similar the individuals were, the higher the likelihood was of them finding each other desirable and opting to meet in person. The only exception was introversion, where introverts rarely had an effective match with other introverts. When investigating the preliminary stages of the choice process we looked at the consistency between the choice of men/women, the time it took users to make these binary choices, and the tendency of yes/no decisions. We used a biologically inspired choice model to estimate the decision process and could predict the selection and response time with nearly 60% accuracy. Given that people make their initial selection in no more than 11 s, and ultimately prefer a partner who shares numerous attributes with them, we suggest that users are less selective in their early preferences and gradually, during their conversation, converge onto clusters that share a high degree of similarity in characteristics.
| S-EPMC6743509 | biostudies-literature