537 research outputs found

    Oligonucleotide properties determination and primer designing: a critical examination of predictions

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    Motivation: Precise prediction of melting temperature (Tm), secondary structures and design of oligonucleotides determine the efficiency and success of experimentation in molecular biology. Availability of a plethora of software and the users unawareness about their limitations compromises the accuracy and reliability of the predictions. Results: Comparative analysis of 56 modules was done for Tm prediction using a large set of oligonucleotide sequences spanning the whole range of GC-content and length. Allawi module of the calculator ‘MELTING’, Nearest Neighbor (NN) of oligo calculator (McLab), NN of Tm Calculation for Oligos (Biomath Calculator, Promega) and HYTHER provided the most precise Tm predictions. A model has also been proposed to calculate the optimum annealing temperature integrating the already reported formulations. Secondary structure predictions of oligonucleotides reveal a large number of structures in contrast to the experimental observations. Of the 11 primer designing tools evaluated, Primer 3 and WebPrimer performed the best for the AT-rich templates, Exon Primer for AT = GC templates, and Primer Design Assistant, Primer3 and Primer Quest for GC-rich templates. This study provides optimal choice for application to the user, increasing the success of a variety of experimentations, especially those that have high-throughput and complex assay designs

    Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

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    Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts

    The genetic architecture of type 2 diabetes

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    The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

    Estimation of the population mean using paired ranked set sampling

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    In the situation where the sampling units in a study can be easily ranked than quantified, the ranked set sampling methods are found to be more efficient and cost effective as compared to SRS. In this paper we propose an estimator of the population mean using paired ranked set sampling (RSS) method. The proposed estimator is an unbiased estimator of the population mean when the set size is even. In case of odd set size the estimator is unbiased when the underlying distribution is symmetric. It is shown that the proposed estimator is more efficient than its counterpart SRS method for all distributions considered in this study

    Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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    To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (\u3e80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D

    Common Variants in CRP and LEPR Influence High Sensitivity C-Reactive Protein Levels in North Indians

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    BACKGROUND: High sensitivity C-reactive protein (hsCRP) levels are shown to be influenced by genetic variants in Europeans; however, little is explored in Indian population. METHODS: Herein, we comprehensively evaluated association of all previously reported genetic determinants of hsCRP levels, including 18 cis (proximal to CRP gene) and 73 trans-acting (distal to CRP gene) variants in 4,200 North Indians of Indo-European ethnicity. First, we evaluated association of 91 variants from 12 candidate loci with hsCRP levels in 2,115 North Indians (1,042 non-diabetic subjects and 1,073 patients with type 2 diabetes). Then, cis and trans-acting variants contributing maximally to hsCRP level variation were further replicated in an independent 2,085 North Indians (1,047 patients with type 2 diabetes and 1,038 non-diabetic subjects). RESULTS: We found association of 12 variants from CRP, LEPR, IL1A, IL6, and IL6R with hsCRP levels in non-diabetic subjects. However, only rs3093059-CRP [β = 0.33, P = 9.6×10⁻⁵] and the haplotype harboring rs3093059 risk allele [β = 0.32 µg/mL, P = 1.4×10⁻⁴/P(perm) = 9.0×10⁻⁴] retained significance after correcting for multiple testing. The cis-acting variant rs3093059-CRP had maximum contribution to the variance in hsCRP levels (1.14%). Among, trans-acting variants, rs1892534-LEPR was observed to contribute maximally to hsCRP level variance (0.59%). Associations of rs3093059-CRP and rs1892534-LEPR were confirmed by replication and attained higher significance after meta-analysis [β(meta) = 0.26/0.22; P(meta) = 4.3×10⁻⁷/7.4×10⁻³ and β(meta) = -0.15/-0.12; P(meta) = 2.0×10⁻⁶/1.6×10⁻⁶ for rs3093059 and rs1892534, respectively in non-diabetic subjects and all subjects taken together]. CONCLUSION: In conclusion, we identified rs3093059 in CRP and rs1892534 in LEPR as major cis and trans-acting contributor respectively, to the variance in hsCRP levels in North Indian population