scholarly journals freqpcr: Estimation of population allele frequency using qPCR ΔΔCq measures from bulk samples

2021 ◽  
Author(s):  
Masaaki Sudo ◽  
Masahiro Osakabe
Keyword(s):  
Allele Frequency ◽  
Population Allele Frequency

scholarly journals Mutation Rate Model Used in the DNA VIEW Program

2020 ◽  
Vol 10 (10) ◽  
pp. 3585 ◽  
Author(s):  
Tomasz Krajka
Keyword(s):  
Computer Simulation ◽  
Allele Frequency ◽  
Mutation Rate ◽  
Allele Frequencies ◽  
Rate Model ◽  
Dna Database ◽  
Mutation Model ◽  
Population Allele Frequency ◽  
Frequency Changes

The first problem considered in this paper is the problem of correctness of a mutation model used in the DNA VIEW program. To this end, we theoretically predict population allele frequency changes in time according to this and similar models (we determine the limit frequencies of alleles—they are uniformly distributed). Furthermore, we evaluate the speed of the above changes using computer simulation applied to our DNA database. Comparing uniformly distributed allele frequencies with these existing in the population (for example, using entropy), we conclude that this mutation model is not correct. The evolution does not follow this direction (direction of uniformly distributed frequencies). The second problem relates to the determination of the extent to which an incorrect mutation model can disturb DNA VIEW program results. We show that in typical computations (simple paternity testing without maternal mutation) this influence is negligible, but in the case of maternal mutation, this should be taken into account. Furthermore, we show that this model is inconsistent from a theoretical viewpoint. Equivalent methods result in different error levels.

scholarly journals freqpcr: estimation of population allele frequency using qPCR ΔΔCq measures from bulk samples

2021 ◽  
Author(s):  
Masaaki Sudo ◽  
Masahiro Osakabe
Keyword(s):  
Allele Frequency ◽  
Measurement Errors ◽  
Interval Estimation ◽  
R Package ◽  
Bulk Sample ◽  
Dna Yield ◽  
Rare Mutations ◽  
Population Allele Frequency ◽  
The Individual ◽  
Pcr Techniques

AbstractPCR techniques, both quantitative (qPCR) and non-quantitative, have been used to estimate allele frequency in a population. However, the labor required to sample numerous individuals, and subsequently handle each sample, makes quantification of rare mutations, including pesticide resistance genes at the early stages of resistance development, challenging. Meanwhile, pooling DNA from multiple individuals as a “bulk sample” may reduce handling costs. The qPCR output for a bulk sample, however, contains uncertainty owing to variations in DNA yields from each individual, in addition to measurement errors. In this study, we developed a statistical model for the interval estimation of allele frequency using ΔΔCq-based qPCR analyses of multiple bulk samples collected from a population. We assumed a gamma distribution as the individual DNA yield and developed an R package for parameter estimation, which was verified with real DNA samples from acaricide-resistant spider mites, as well as a numerical simulation. Our model resulted in unbiased point estimates of the allele frequency compared with simple averaging of the ΔΔCq values, while their confidence intervals suggest that collecting and pooling additional samples from individuals may produce higher precision than individual PCR tests with moderate sample sizes.

scholarly journals . Identification of CYP2C9, VKORC1 genotypes and recommendation of warfarin dose for Vietnamese cardiovascular patients

2020 ◽  
Vol 17 (4) ◽  
pp. 589-594
Author(s):  
Nguyen Dang Ton ◽  
Nguyen Thi Thanh Hoa ◽  
Nguyen Phan Anh ◽  
Vu Phuong Nhung ◽  
Le Thi Bich Thao ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Allele Frequency ◽  
Vitamin K ◽  
Warfarin Dose ◽  
Clotting Factors ◽  
Allelic Frequencies ◽  
Cardiovascular Patients ◽  
Population Allele Frequency ◽  
Pcr Rflp ◽  
Study Population

Warfarin is a well-known anticoagulant that capable of reducing the activity of vitamin K-dependent clotting factors. It has been widely used for cardiovascular patients. However, patient’s genotype of CYP2C9 and VKORC1 remarkably affects the metabolism of warfarin. This study aims to identify the CYP2C9 and VKORC1 genotypes of 96 Vietnamese patients suffering cardiodynia or myocardial infarction and establish their daily warfarin dose. The PCR-RFLP method was used to identified the CYP2C9*2, CYP2C9*3 and VKORC1 alleles. The result indicated that allelic frequencies for CYP2C9*3 were observed at 3% of investigated patients while CYP2C9*2 alleles and genotypes were not detected in this study population. Allele frequency of VKORC1 (c. -1639G>A) was observed at 84%. Base on the CYP2C9 and VKORC1 genotypes, we recommended the daily warfarin dose for of these patients.

scholarly journals Testing for ancient selection using cross-population allele frequency differentiation

2015 ◽  
Author(s):  
Fernando Racimo
Keyword(s):  
Allele Frequency ◽  
Modern Human ◽  
Composite Likelihood ◽  
Likelihood Method ◽  
Population Allele Frequency ◽  
Long Time ◽  
Ancient Times ◽  
Frequency Changes ◽  
Beneficial Allele ◽  
Two Populations

A powerful way to detect selection in a population is by modeling local allele frequency changes in a particular region of the genome under scenarios of selection and neutrality, and finding which model is most compatible with the data. Chen et al. (2010) developed a composite likelihood method called XP-CLR that uses an outgroup population to detect departures from neutrality which could be compatible with hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive to recent selection and may miss selective events that happened a long time ago. To overcome this, we developed an extension of XP-CLR that jointly models the behavior of a selected allele in a three-population tree. Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that occurred before two populations split from each other, and can distinguish between those events and events that occurred specifically in each of the populations after the split. We applied our new test to population genomic data from the 1000 Genomes Project, to search for selective sweeps that occurred before the split of Yoruba and Eurasians, but after their split from Neanderthals, and that could have led to the spread of modern-human-specific phenotypes. We also searched for sweep events that occurred in East Asians, Europeans and the ancestors of both populations, after their split from Yoruba. In both cases, we are able to confirm a number of regions identified by previous methods, and find several new candidates for selection in recent and ancient times. For some of these, we also find suggestive functional mutations that may have driven the selective events.

scholarly journals A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wendell Jones ◽  
Binsheng Gong ◽  
Natalia Novoradovskaya ◽  
Dan Li ◽  
Rebecca Kusko ◽  
...  
Keyword(s):  
Quality Control ◽  
Allele Frequency ◽  
Liquid Biopsy ◽  
Limit Of Detection ◽  
Reference Sample ◽  
Digital Pcr ◽  
Detection Sensitivity ◽  
Analytical Accuracy ◽  
And Performance ◽  
Reference Samples

Abstract Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

18 New method of assessing tumor heterogeneity utilizing both circulating tumor DNA and tissue DNA to predict the response to immunotherapy

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A18-A18
Author(s):  
Jaeyoun Choi ◽  
Myungwoo Nam ◽  
Stanislav Fridland ◽  
Jinyoung Hwang ◽  
Chan Mi Jung ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Response ◽  
Allele Frequency ◽  
Scoring System ◽  
Tumor Heterogeneity ◽  
Metastatic Breast ◽  
Circulating Tumor Dna ◽  
List Type ◽  
Multiple Biopsies ◽  
Tumor Dna

BackgroundTumor heterogeneity assessment may help predict response to immunotherapy. In melanoma mouse models, tumor heterogeneity impaired immune response.1 In addition, among lung cancer patients receiving immunotherapy, the high clonal neoantigen group had favorable survival and outcomes.2 Ideal methods of quantifying tumor heterogeneity are multiple biopsies or autopsy. However, these are not feasible in routine clinical practice. Circulating tumor DNA (ctDNA) is emerging as an alternative. Here, we reviewed the current state of tumor heterogeneity quantification from ctDNA. Furthermore, we propose a new tumor heterogeneity index(THI) based on our own scoring system, utilizing both ctDNA and tissue DNA.MethodsSystematic literature search on Pubmed was conducted up to August 18, 2020. A scoring system and THI were theoretically derived.ResultsTwo studies suggested their own methods of assessing tumor heterogeneity. One suggested clustering mutations with Pyclone,3 and the other suggested using the ratio of allele frequency (AF) to the maximum somatic allele frequency (MSAF).4 According to the former, the mutations in the highest cellular prevalence cluster can be defined as clonal mutations. According to the latter, the mutations with AF/MSAF<10% can be defined as subclonal mutations. To date, there have been no studies on utilizing both ctDNA and tissue DNA simultaneously to quantify tumor heterogeneity. We hypothesize that a mutation found in only one of either ctDNA or tissue DNA has a higher chance of being subclonal.We suggest a scoring system based on the previously mentioned methods to estimate the probability for a mutant allele to be subclonal. Adding up the points that correspond to the conditions results in a subclonality score (table 1). In a given ctDNA, the number of alleles with a subclonality score greater than or equal to 2 divided by the total number of alleles is defined as blood THI (bTHI) (figure 1). We can repeat the same calculation in a given tissue DNA for tissue THI (tTHI) (figure 2). Finally, we define composite THI (cTHI) as the mean of bTHI and tTHI.Abstract 18 Table 1Subclonality scoreAbstract 18 Figure 1Hypothetical distribution of all alleles found in ctDNA bTHI = the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in ctDNA = 10/20 =50%Abstract 18 Figure 2Hypothetical distribution of all alleles found in tissue DNA tTHI= the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in tissue DNA = 16/40 = 40% cTHI= (bTHI + tTHI)/2 = 45%ConclusionsTumor heterogeneity is becoming an important biomarker for predicting response to immunotherapy. Because autopsy and multiple biopsies are not feasible, utilizing both ctDNA and tissue DNA is the most comprehensive and practical approach. Therefore, we propose cTHI, for the first time, as a quantification measure of tumor heterogeneity.ReferencesWolf Y, Bartok O. UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell 2019;179:219–235.McGranahan N, Swanton C. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–1469.Ma F, Guan Y. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020;146:1359–1368.Liu Z, Xie Z. Presence of allele frequency heterogeneity defined by ctDNA profiling predicts unfavorable overall survival of NSCLC. Transl Lung Cancer Res 2019;8:1045–1050.

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