Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.5
Journals
Genes
Special Issues
Genome-Wide Identification: Recent Trends in Genomic Studies, Volume II
share announcement

Genome-Wide Identification: Recent Trends in Genomic Studies, Volume II

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1140

Special Issue Editors

Dr. Xiaowei Wu

E-Mail Website
Guest Editor
Department of Statistics, Virginia Tech, 250 Drillfield Dr., Blacksburg, VA 24061, USA
Interests: statistical genetics; bioinformatics; Bayesian and computational statistics; branching processes
Dr. Hailan Liu

E-Mail Website
Guest Editor
Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
Interests: statistical genetics; bioinformatics; genomic prediction; QTL mapping
Dr. Lide Han

E-Mail Website
Guest Editor
Vanderbilt University Medical Center, Nashville, TN 37232, USA
Interests: statistical genomics; bioinformatics; identity by descent; CNV association

Special Issue Information

Dear Colleagues,

With the advances in high-throughput sequencing in recent years, rich sources of genotype and phenotype data have been produced, providing unprecedented opportunities for genome-wide identification of complex traits and disease-related biomarkers in humans, animals, and even plants and posing statistical and computational challenges to the paradigm of traditional GWAS. New methodological research continues to emerge at an impressive pace, breaking through the limitations of classical association mapping theory and expanding the scope of more sophisticated genomic applications. We invite you to contribute original articles, new methods, and thought-provoking reviews addressing recent trends in genome-wide identification to this Special Issue. If you would like more information about this Special Issue, or have any other questions, please feel free to contact us.

Dr. Xiaowei Wu
Dr. Hailan Liu
Dr. Lide Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GWAS
  • linkage mapping
  • complex disease
  • complex traits
  • association testing
  • linear mixed model
  • rare-variant association
  • related individuals
  • longitudinal traits
  • functional regression
  • gene association analysis
  • pathway association analysis
  • gene–environment interactions
  • variable selection
  • genetic relationship matrix
  • population stratification
  • CNV association
  • multi-ancestry meta-analysis
  • transcriptome-wide association
  • phenome-wide association
  • proteome-wide association

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 1175 KiB  
Article
Integrating External Controls by Regression Calibration for Genome-Wide Association Study
by Lirong Zhu, Shijia Yan, Xuewei Cao, Shuanglin Zhang and Qiuying Sha
Genes 2024, 15(1), 67; https://doi.org/10.3390/genes15010067 - 03 Jan 2024
Viewed by 834
Abstract
Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is [...] Read more.
Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study. Full article
(This article belongs to the Special Issue Genome-Wide Identification: Recent Trends in Genomic Studies, Volume II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop