scholarly journals Correction to: Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
William R. P. Denault ◽  
Astanand Jugessur
Keyword(s):  
Association Analysis ◽  
Differentially Methylated Regions ◽  
Functional Association ◽  
Fast Wavelet

A Correction to this paper has been published: https://doi.org/10.1186/s12859-021-03979-y

scholarly journals Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
William R. P. Denault ◽  
Astanand Jugessur
Keyword(s):  
Type I Error ◽  
R Package ◽  
Type I ◽  
Differentially Methylated Regions ◽  
Stat Assoc ◽  
Link Type ◽  
Fast Wavelet ◽  
Computational Speed ◽  
Recent Method ◽  
Public Repositories

Abstract Background We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665–686, 2015. 10.1214/14-AOAS776) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL). Results WaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362–1371, 2017. 10.1080/01621459.2017.1328361) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach “fast functional wavelet” (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions. Conclusions Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw.

ROPO Effect in Gender- Generational Characteristics

2020 ◽  
Vol 11 (1) ◽  
pp. 43-43
Author(s):  
Radovan Bačík ◽  
Mária Oleárová ◽  
Martin Rigelský
Keyword(s):  
Association Analysis ◽  
Search Engines ◽  
Product Information ◽  
Customer Behavior ◽  
Internet Research ◽  
The Internet ◽  
Significant Progress ◽  
Customer Reviews ◽  
Online Purchase ◽  
Brick And Mortar

The development of the Internet and the current technologies have contributed to a significant progress in the consumer shopping process. Today, shopping decisions are more intuitive and much easier to make. E-shops, search engines, customer reviews and other similar tools reduce costs of searching for products or product information, thus boosting the habit of searching for information on the Internet - "Research Shopper Phenomenon" (Verhoef et al. 2007). According to Verhoef et al. (2015), this phenomenon leads to a phenomenon where consumers search for product information using one channel (Internet) and then make a purchase through another channel (brick-and-mortar shop). Heinrich and Thalmair (2013) refer to this effect as the "research online, purchase offline" or "ROPO" effect for short. This phenomenon can also be observed in reverse. Keywords: customer behavior, research online – purchase offline, association analysis

A Caution in Association of ABO Blood Group with COVID-19

2020 ◽  
Keyword(s):  
Gene Cluster ◽  
Clinical Outcomes ◽  
Blood Group ◽  
Association Analysis ◽  
Blood Type ◽  
Genome Wide Association ◽  
Chromosome 3 ◽  
Abo Blood Group ◽  
Genome Wide Association Analysis ◽  
Genome Wide

A comment on Zhao J, Yang Y, Huang H, Li D, Gu D, Lu X, et al. Association of ABO blood group and Covid19 susceptability. medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.03.11.20031096. Zeng X, Fan H, Lu D, Huang F, Meng X, Li Z, et al. Association between ABO blood group and clinical outcomes of Covid19. medRxiv[PREPRINT].2020; https://doi.org/10.1101/2020.04.15.20063107. Zietz M, Tatonetti N. Testing the association between blood type and COVID-19 infection, intubation, and death medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.04.08.20058073. Ellinghaus D, Degenhardt F, Bujanda L, al. e. The ABO blood group and a chromosome 3 gene cluster associate with SRAS-CoV2 respitarory failure in an Italy-Spain genome-wide association analysis. medRxiv. 2020; https://doi.org/10.1101/2020.05.31.20114991.

Correlation and Association Analysis between Biomass and Yield Components in Soybean

2014 ◽  
Vol 40 (1) ◽  
pp. 7 ◽  
Author(s):  
Mao-Ni CHAO ◽  
De-Rong HAO ◽  
Zhi-Tong YIN ◽  
Jin-Yu ZHANG ◽  
Hai-Na SONG ◽  
...  
Keyword(s):  
Association Analysis ◽  
Yield Components

Genome-wide Association Analysis of Kernel Row Number in Maize

2014 ◽  
Vol 40 (1) ◽  
pp. 1 ◽  
Author(s):  
Huan-Xin ZHANG ◽  
Jian-Feng WENG ◽  
Xiao-Cong ZHANG ◽  
Chang-Lin LIU ◽  
Hong-Jun YONG ◽  
...  
Keyword(s):  
Association Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
Kernel Row Number
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