scholarly journals Rapid Acceleration of the Permutation Test via Transpositions

Author(s):  
Moo K. Chung ◽  
Linhui Xie ◽  
Shih-Gu Huang ◽  
Yixian Wang ◽  
Jingwen Yan ◽  
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2021 ◽  
Vol 9 (1) ◽  
pp. e001443
Author(s):  
Jingjing Zuo ◽  
Yuan Lan ◽  
Honglin Hu ◽  
Xiangqing Hou ◽  
Jushuang Li ◽  
...  

IntroductionDespite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.Research design and methodsIn this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.ResultsWe detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.ConclusionsThis study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings.


2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.


2021 ◽  
Vol 3 ◽  
Author(s):  
Alexei S. Kassian ◽  
George Starostin ◽  
Ilya M. Egorov ◽  
Ekaterina S. Logunova ◽  
Anna V. Dybo
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Abstract


Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1640
Author(s):  
Cecilia Jiménez-Sánchez ◽  
Fabián Pedregosa ◽  
Isabel Borrás-Linares ◽  
Jesús Lozano-Sánchez ◽  
Antonio Segura-Carretero

In this study, we determined the phytochemical profile of the Spanish “triguero” asparagus landrace “verde-morado” (Asparagus officinalis L.), a wild traditional landrace, and the improved “triguero” HT-801, together with two commercial green asparagus varieties. For comparison, we used reverse-phase high-performance liquid chromatography coupled with diode array electrospray time-of-flight mass spectrometry (RP-HPLC-DAD-ESI-TOF/MS) followed by a permutation test applied using a resampling methodology valid under a relaxed set of assumptions, such as i.i.d. errors (not necessarily normal) that are exchangeable under the null hypothesis. As a result, we postulate that “triguero” varieties (the improved HT-801 followed by its parent “verde-morado”) have a significantly different phytochemical profile from that of the other two commercial hybrid green varieties. In particular, we found compounds specific to the “triguero” varieties, such as feruloylhexosylhexose isomers, or isorhamnetin-3-O-glucoside, which was found only in the “triguero” variety HT-801. Although studies relating the phytochemical content of “triguero” asparagus varieties to its health-promoting effects are required, this characteristic phytochemical profile can be used for differentiating and revalorizating these asparagus cultivars.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
James M. Kunert-Graf ◽  
Nikita A. Sakhanenko ◽  
David J. Galas

Abstract Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. Results In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples. Conclusions The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.


Author(s):  
Markus Ekvall ◽  
Michael Höhle ◽  
Lukas Käll

Abstract Motivation Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. Results Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up—by orders of magnitude—is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test. Availabilityand implementation In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. Supplementary information Supplementary data are available at Bioinformatics online.


2009 ◽  
Vol 36 (1) ◽  
pp. 137-140 ◽  
Author(s):  
PROTON RAHMAN ◽  
ROBERT D. INMAN ◽  
WALTER P. MAKSYMOWYCH ◽  
JEFF P. REEVE ◽  
LYNETTE PEDDLE ◽  
...  

Objective.A recent genome-wide pooling study noted a significant association of interleukin 23 receptor (IL-23R) and psoriasis. Overxpression of IL-23 has been detected in lesional psoriatic skin, and induces epidermal proliferation. Given the interplay between psoriasis and PsA, we examined the association of IL-23R variants in 2 independent Canadian Caucasian cohorts of patients with psoriatic arthritis (PsA).Methods.We examined 496 PsA probands and 476 controls. Cases and controls were genotyped for a panel of 11 single-nucleotide polymorphisms (SNP) in IL-23R. Allele and haplotype associations were calculated using WHAP software. P values for haplotype associations were calculated using a permutation test.Results.The 381Gln allele of the coding SNP Arg381Gln (rs11209026) was found to be protective in the Canadian population (p = 0.004; corrected p = 0.044).A 2-marker haplotype from SNP rs7530511 and rs11209026 was associated with PsA (p = 0.011). All 3-marker sliding windows containing SNP rs11209026 were associated with PsA (p = 0.02 for all 3 windows). The magnitude of effect of IL-23R association in PsA appears to be similar to that reported in uncomplicated psoriasis.Conclusion.Significant associations between Arg381Gln SNP and haplotypes encoding this variant were noted in PsA. It remains to be determined what contribution of this association, if any, is specifically due to the inflammatory arthritis (PsA) rather than psoriasis.


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