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Statistical Genetics
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Statistical Genetics

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

Deadline for manuscript submissions: closed (20 April 2021) | Viewed by 14925

Special Issue Editors

Dr. Arthur Berg

E-Mail Website
Guest Editor
Department of Public Health Sciences, Division of Biostatistics & Bioinformatics, Penn State College of Medicine, Hershey, PA 17033, USA
Interests: statistical genetics; Bayesian statistics; nonparametric statistics; health informatics
Prof. Dr. Rongling Wu

E-Mail Website
Guest Editor
Penn State College of Medicine, Hershey, PA 17033, USA
Interests: statistical/quantitative genetics in medicine; life sciences; agriculture; biology
Special Issues, Collections and Topics in MDPI journals
Dr. Dajiang Liu

E-Mail Website
Guest Editor
Penn State College of Medicine, Hershey, PA 17033, USA
Interests: statistical genetics; complex trait genetics; functional genomics

Special Issue Information

Dear Colleagues,

A team of statistical genetics researchers at Penn State are putting together this Special Issue titled “Statistical Genetics”. Statistical genetics is a rich interdisciplinary field that mixes together various aspects of genomics, quantitative genetics, computational sciences, bioinformatics, and statistics, and the editors are excited to promote this topic in Genes (2019 Impact Factor: 3.331) with this Special Issue.  

This Special Issue welcomes any contributions to the field of statistical genetics or computational genomics, and we hope you will consider contributing. We are committed to fast responses from the editors. Feel free to reach out to any of the editors directly, or you can also contact the editors through a dedicated email address—[email protected]—that has been set up to facilitate communication for this Special Issue. 

We thank you very much for supporting this Special Issue, and we look forward to your submissions.

Sincerely,

Assoc. Prof. Dr. Arthur Berg
Prof. Rongling Wu
Assoc. Prof. Dr. Dajiang Liu
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

  • Statistical genetics
  • Quantitative genetics
  • Complex trait genetics
  • Statistical genomics
  • Computational genomics
  • Functional genomics

Published Papers (3 papers)

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Research

28 pages, 617 KiB  
Article
Improved Models of Coalescence Ages of Y-DNA Haplogroups
by Iain McDonald
Genes 2021, 12(6), 862; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12060862 - 04 Jun 2021
Cited by 2 | Viewed by 8601
Abstract
Databases of commercial DNA-testing companies now contain more customers with sequenced DNA than any completed academic study, leading to growing interest from academic and forensic entities. An important result for both these entities and the test takers themselves is how closely two individuals [...] Read more.
Databases of commercial DNA-testing companies now contain more customers with sequenced DNA than any completed academic study, leading to growing interest from academic and forensic entities. An important result for both these entities and the test takers themselves is how closely two individuals are related in time, as calculated through one or more molecular clocks. For Y-DNA, existing interpretations of these clocks are insufficiently accurate to usefully measure relatedness in historic times. In this article, I update the methods used to calculate coalescence ages (times to most-recent common ancestor, or TMRCAs) using a new, probabilistic statistical model that includes Y-SNP, Y-STR and ancilliary historical data, and provide examples of its use. Full article
(This article belongs to the Special Issue Statistical Genetics)
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13 pages, 673 KiB  
Article
Secondary Genome-Wide Association Study Using Novel Analytical Strategies Disentangle Genetic Components of Cleft Lip and/or Cleft Palate in 1q32.2
by Yunju Yang, Akiko Suzuki, Junichi Iwata and Goo Jun
Genes 2020, 11(11), 1280; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11111280 - 29 Oct 2020
Cited by 4 | Viewed by 1988
Abstract
Orofacial cleft (OFC) is one of the most prevalent birth defects, leading to substantial and long-term burdens in a newborn’s quality of life. Although studies revealed several genetic variants associated with the birth defect, novel approaches may provide additional clues about its etiology. [...] Read more.
Orofacial cleft (OFC) is one of the most prevalent birth defects, leading to substantial and long-term burdens in a newborn’s quality of life. Although studies revealed several genetic variants associated with the birth defect, novel approaches may provide additional clues about its etiology. Using the Center for Craniofacial and Dental Genetics project data (n = 10,542), we performed linear mixed-model analyses to study the genetic compositions of OFC and investigated the dependence among identified loci using conditional analyses. To identify genes associated with OFC, we conducted a transcriptome-wide association study (TWAS) based on predicted expression levels. In addition to confirming the previous findings at four loci, 1q32.2, 8q24, 2p24.2 and 17p13.1, we untwined two independent loci at 1q32.2, TRAF3IP3 and IRF6. The sentinel SNP in TRAF3IP3 (rs2235370, p-value = 5.15 × 10−9) was independent of the sentinel SNP at IRF6 (rs2235373, r2 < 0.3). We found that the IRF6 effect became nonsignificant once the 8q24 effect was conditioned, while the TRAF3IP3 effect remained significant. Furthermore, we identified nine genes associated with OFC in TWAS, implicating a glutathione synthesis and drug detoxification pathway. We identified some meaningful additions to the OFC etiology using novel statistical methods in the existing data. Full article
(This article belongs to the Special Issue Statistical Genetics)
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16 pages, 295 KiB  
Article
Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data
by Yu Jiang, Sai Chen, Xingyan Wang, Mengzhen Liu, William G. Iacono, John K. Hewitt, John E. Hokanson, Kenneth Krauter, Markku Laakso, Kevin W. Li, Sharon M. Lutz, Matthew McGue, Anita Pandit, Gregory J.M. Zajac, Michael Boehnke, Goncalo R. Abecasis, Scott I. Vrieze, Bibo Jiang, Xiaowei Zhan and Dajiang J. Liu
Genes 2020, 11(5), 586; https://0-doi-org.brum.beds.ac.uk/10.3390/genes11050586 - 25 May 2020
Cited by 4 | Viewed by 3464
Abstract
There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown [...] Read more.
There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online. Full article
(This article belongs to the Special Issue Statistical Genetics)
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