Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.8
Journals
Genes
Special Issues
Statistical Approaches for the Analysis of Genomic Data
share announcement

Statistical Approaches for the Analysis of Genomic Data

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

Deadline for manuscript submissions: closed (10 September 2022) | Viewed by 5587

Special Issue Editors

Prof. Dr. Xiaolei Liu

E-Mail Website
Guest Editor
College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: genome-wide association studies (GWAS); genomic selection; statistical genetics; algorithms; phenomics; artificial intelligence
Dr. Yunlong Ma

E-Mail Website
Guest Editor
College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: selective sweep; parallel selection; genotype imputation; genomics; bioinformatics
Dr. Yuhua Fu

E-Mail Website
Guest Editor
College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: multi-omics integration; machine learning; big omics data; bioinformatics; database; animal breeding
Dr. Lilin Yin

E-Mail Website
Guest Editor
College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: quantitative genetics; statistical genomics; genome-wide association studies (GWAS); genomic selection/prediction; animal breeding; software developing; visualization

Special Issue Information

Dear Colleagues,

Newly developed techniques have produced a wealth of multiple layers of omics data, including genomics, transcriptomics, proteomics, and phenomics, which help us in understanding the underlying biological processes of traits of interest. Therefore, the research community requires new computational approaches in order to efficiently handle big omics data.

This Special Issue is calling for submissions of original studies about computational approaches for the analysis of omics data. The topics that are welcome include but are not limited to:

  • Computational approaches and reviews for GWAS, genomic selection, selective sweep, and imputation;
  • Approaches for efficient handling of big omics data;
  • Computational approaches for integrating multi-omics data and new gene-phenotype associations identified by multi-omics integration approach;
  • Advanced phenotyping approaches and new discoveries of gene-phenotype associations.

Prof. Dr. Xiaolei Liu
Dr. Yunlong Ma
Dr. Yuhua Fu
Dr. Lilin Yin
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
  • genomic selection
  • multi-omics integration analysis
  • selective sweep

Published Papers (3 papers)

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

Research

10 pages, 1179 KiB  
Article
Study on the Association between LRRC8B Gene InDel and Sheep Body Conformation Traits
by Jiaqiang Zhang, Zhansaya Toremurat, Yilin Liang, Jie Cheng, Zhenzhen Sun, Yangming Huang, Junxia Liu, BUREN Chaogetu, Gang Ren and Hong Chen
Genes 2023, 14(2), 356; https://doi.org/10.3390/genes14020356 - 30 Jan 2023
Cited by 1 | Viewed by 1464
Abstract
Marker-assisted selection is an important method for livestock breeding. In recent years, this technology has been gradually applied to livestock breeding to improve the body conformation traits. In this study, the LRRC8B (Leucine Rich Repeat Containing 8 VRAC Subunit B) gene was selected [...] Read more.
Marker-assisted selection is an important method for livestock breeding. In recent years, this technology has been gradually applied to livestock breeding to improve the body conformation traits. In this study, the LRRC8B (Leucine Rich Repeat Containing 8 VRAC Subunit B) gene was selected to evaluate the association between its genetic variations and the body conformation traits in two native sheep breeds in China. Four body conformation traits, including withers height, body length, chest circumference, and body weight, were collected from 269 Chaka sheep. We also collected the body length, chest width, withers height, chest depth, chest circumference, cannon bone circumference, and height at hip cross of 149 Small-Tailed Han sheep. Two different genotypes, ID and DD, were detected in all sheep. Our data showed that the polymorphism of the LRRC8B gene was significantly associated with chest depth (p < 0.05) in Small-Tailed Han sheep, and it is greater in sheep with DD than those with ID. In conclusion, our data suggested that the LRRC8B gene could serve as a candidate gene for marker-assisted selection in Small-Tailed Han sheep. Full article
(This article belongs to the Special Issue Statistical Approaches for the Analysis of Genomic Data)
►▼ Show Figures

Graphical abstract

15 pages, 2084 KiB  
Article
A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits
by Gulnara R. Svishcheva, Evgeny S. Tiys, Elizaveta E. Elgaeva, Sofia G. Feoktistova, Paul R. H. J. Timmers, Sodbo Zh. Sharapov, Tatiana I. Axenovich and Yakov A. Tsepilov
Genes 2022, 13(10), 1694; https://doi.org/10.3390/genes13101694 - 21 Sep 2022
Cited by 2 | Viewed by 1820
Abstract
We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing [...] Read more.
We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast. Full article
(This article belongs to the Special Issue Statistical Approaches for the Analysis of Genomic Data)
►▼ Show Figures

Graphical abstract

12 pages, 1145 KiB  
Article
An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs
by Xingjie Hao, Aixin Liang, Graham Plastow, Chunyan Zhang, Zhiquan Wang, Jiajia Liu, Angela Salzano, Bianca Gasparrini, Giuseppe Campanile, Shujun Zhang and Liguo Yang
Genes 2022, 13(8), 1430; https://doi.org/10.3390/genes13081430 - 11 Aug 2022
Cited by 1 | Viewed by 1752
Abstract
Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research [...] Read more.
Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information (i.e., the known causal loci) was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits (FY270, MY270, PY270, and PM) when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches (GBLUP, BayesR, and pGBLUP) did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information. Full article
(This article belongs to the Special Issue Statistical Approaches for the Analysis of Genomic Data)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop