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Wang Y, Dong W, Liang Y, Lin W, Chen J, Henry R, Chen F. PhyloForge: Unifying Micro- and Macroevolution With Comprehensive Genomic Signals. Mol Ecol Resour 2025; 25:e14050. [PMID: 39588658 DOI: 10.1111/1755-0998.14050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 10/31/2024] [Accepted: 11/15/2024] [Indexed: 11/27/2024]
Abstract
The dimensions of phylogenetic research have expanded to encompass the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge, a robust tool designed to seamlessly integrate the demands of both micro- and macroevolution, comprehensively utilising diverse phylogenomic signals, such as genes, SNPs, and structural variations, as well as mitochondrial and chloroplast genomes. PhyloForge's innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling the unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customisable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants and fungi, attests to its broad applicability in the field of phylogenetics. In summary, PhyloForge has significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life. PhyloForge codes could be found in GitHub (https://github.com/wangyayaya/PhyloForge/), and the program could be installed in Conda (https://anaconda.org/wangxiaobei/phyloforge).
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Affiliation(s)
- Ya Wang
- National Key Laboratory for Tropical Crop Breeding, College of Breeding and Multiplication, Sanya Institute of Breeding and Multiplication, Hainan University, Sanya, China
- College of Tropical Agriculture and Forestry, Hainan University, Danzhou, China
| | - Wei Dong
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Yufan Liang
- National Key Laboratory for Tropical Crop Breeding, College of Breeding and Multiplication, Sanya Institute of Breeding and Multiplication, Hainan University, Sanya, China
- College of Tropical Agriculture and Forestry, Hainan University, Danzhou, China
| | - Weiwei Lin
- National Key Laboratory for Tropical Crop Breeding, College of Breeding and Multiplication, Sanya Institute of Breeding and Multiplication, Hainan University, Sanya, China
- College of Tropical Agriculture and Forestry, Hainan University, Danzhou, China
| | - Junhao Chen
- Department of Biology, Saint Louis University, St. Louis, Missouri, USA
| | - Robert Henry
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia
| | - Fei Chen
- National Key Laboratory for Tropical Crop Breeding, College of Breeding and Multiplication, Sanya Institute of Breeding and Multiplication, Hainan University, Sanya, China
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Zhang Y, Liu H, Wang Y, Si X, Pan Y, Guo M, Wu M, Li Y, Liu H, Zhang X, Hou J, Li T, Hao C. TaFT-D1 positively regulates grain weight by acting as a coactivator of TaFDL2 in wheat. PLANT BIOTECHNOLOGY JOURNAL 2025. [PMID: 40100647 DOI: 10.1111/pbi.70032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 01/24/2025] [Accepted: 02/18/2025] [Indexed: 03/20/2025]
Abstract
FLOWERING LOCUS T (FT), a multifunctional regulator in crops, modulates multiple key agronomic traits such as flowering time or heading date and plant height; however, its role in grain development regulation is unclear. Herein, through genome-wide association studies (GWAS), we identified TaFT-D1, which encodes a phosphatidylethanolamine-binding protein (PEBP), as a candidate gene for grain weight in wheat. A one-bp insertion/deletion (InDel) (G/-) in the third exon of TaFT-D1, resulting in different protein lengths, was significantly associated with grain weight. TaFT-D1 knockout via the CRISPR-Cas9 system reduced grain size and weight, and TaFT-D1 increased grain size by promoting cell proliferation and starch synthesis. Transcriptome analysis revealed a significant decrease in the expression of cell cycle- and starch synthesis-related genes, including TaNAC019-3A, TaSWEET15-like-7B, TaCYCD4;1 and TaCYCD3;2, in the taft-d1 knockout line. TaFT-D1 interacted with the bZIP transcription factor TaFDL2, and the tafdl2 mutant presented relatively small grains, suggesting that TaFDL2 is a positive regulator of grain size. Moreover, TaFDL2 bound to the promoters of downstream cell cycle- and starch synthesis-related genes, activating their expression, whereas TaFT-D1 increased this activation via TaFDL2. Interaction assays demonstrated that TaFT-D1, Ta14-3-3A and TaFDL2 formed a regulatory complex. Furthermore, the TaFT-D1(G) allele was significantly correlated with greater thousand-grain weight and earlier heading. This favourable allele has undergone strong positive selection during wheat breeding in China. Our findings provide novel insights into how TaFT-D1 regulates grain weight and highlight its potential application for yield improvement in wheat.
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Affiliation(s)
- Yinhui Zhang
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Haixia Liu
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yaojia Wang
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xuemei Si
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuxue Pan
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjiao Guo
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Meijuan Wu
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuanhao Li
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongxia Liu
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xueyong Zhang
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jian Hou
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tian Li
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chenyang Hao
- State Key Laboratory of Crop Gene Resources and Breeding/National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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Xiong X, Yu C, Qiu M, Zhang Z, Hu C, Zhu S, Yang L, Peng H, Song X, Chen J, Xia B, Wang J, Qing Y, Yang C. Genomic and Gut Microbiome Evaluations of Growth and Feed Efficiency Traits in Broilers. Animals (Basel) 2024; 14:3615. [PMID: 39765519 PMCID: PMC11672845 DOI: 10.3390/ani14243615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 12/10/2024] [Accepted: 12/13/2024] [Indexed: 01/11/2025] Open
Abstract
In this study, we combined genomic and gut microbiome data to evaluate 13 economically important growth and feed efficiency traits in 407 Dahen broilers, including body weight (BW) at four, six, nine, and ten weeks of age (BW4, BW6, BW9, and BW10), as well as the average daily gain (ADG6, ADG9, and ADG10), feed conversion ratio (FCR6, FCR9, and FCR10), and residual feed intake (RFI6, RFI9, and RFI10) for the three growing ages. The highest ADG and lowest FCR were observed at nine and six weeks of age, respectively. We obtained 47,872 high-quality genomic single-nucleotide polymorphisms (SNPs) by sequencing the genomes and 702 amplicon sequence variants (ASVs) of the gut microbiome by sequencing the 16S rRNA gene, both of which were used for analyses of linear mixed models. The heritability estimates (± standard error, SE) ranged from 0.103 ± 0.072 to 0.156 ± 0.079 for BW, 0.154 ± 0.074 to 0.276 ± 0.079 for the ADG, 0.311 ± 0.076 to 0.454 ± 0.076 for the FCR, and 0.413 ± 0.077 to 0.609 ± 0.076 for the RFI traits. We consistently observed moderate and low negative genetic correlations between the BW traits and the FCR and RFI traits (r = -0.562 to -0.038), whereas strong positive correlations were observed between the FCR and RFI traits (r = 0.564 to 0.979). For the FCR and RFI traits, strong positive correlations were found between the measures at the three ages. In contrast to the genomic contribution, we did not detect a gut microbial contribution to all of these traits, as the estimated microbiabilities did not confidently deviate from zero. We systematically evaluated the contributions of host genetics and gut microbes to several growth and feed efficiency traits in Dahen broilers, and the results show that only the host genetics had significant effects on the phenotypic variations in a flock. The parameters obtained in this study, based on the combined use of genomic and gut microbiota data, may facilitate the implementation of efficient breeding schemes in Dahen broilers.
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Affiliation(s)
- Xia Xiong
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Chunlin Yu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Mohan Qiu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Zengrong Zhang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Chenming Hu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Shiliang Zhu
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Li Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Han Peng
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Xiaoyan Song
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Jialei Chen
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Bo Xia
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Jiangxian Wang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
| | - Yi Qing
- Chengdu Livestock and Poultry Genetic Resources Protection Center, Chengdu 610081, China
| | - Chaowu Yang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu 610066, China; (X.X.); (C.Y.); (M.Q.); (Z.Z.); (C.H.); (S.Z.); (L.Y.); (H.P.); (X.S.); (J.C.); (B.X.); (J.W.)
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Liu H, Xu J, Wang X, Wang H, Wang L, Shen Y. Efficient large-scale genomic prediction in approximate genome-based kernel model. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 138:6. [PMID: 39666050 DOI: 10.1007/s00122-024-04793-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 11/23/2024] [Indexed: 12/13/2024]
Abstract
KEY MESSAGE Three computationally efficient algorithms of GP including RHBK, RHDK, and RHPK were developed in approximate genome-based kernel model. The drastically growing amount of genomic information contributes to increasing computational burden of genomic prediction (GP). In this study, we developed three computationally efficient algorithms of GP including RHBK, RHDK, and RHPK in approximate genome-based kernel model, which reduces dimension of genomic data via Nyström approximation and decreases the computational cost significantly thereby. According to the simulation study and real datasets, our three methods demonstrated predictive accuracy similar to or better than RHAPY, GBLUP, and rrBLUP in most cases. They also demonstrated a substantial reduction in computational time compared to GBLUP and rrBLUP in simulation. Due to their advanced computing efficiency, our three methods can be used in a wide range of application scenarios in the future.
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Affiliation(s)
- Hailan Liu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.
| | - Jinqing Xu
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Laboratory for Research and Utilization of Qinghai Tibetan Plateau Germplasm Resources, Qinghai Provincial Key Laboratory of Crop Molecular Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China
| | - Xuesong Wang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Handong Wang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Laboratory for Research and Utilization of Qinghai Tibetan Plateau Germplasm Resources, Qinghai Provincial Key Laboratory of Crop Molecular Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lei Wang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Laboratory for Research and Utilization of Qinghai Tibetan Plateau Germplasm Resources, Qinghai Provincial Key Laboratory of Crop Molecular Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China
| | - Yuhu Shen
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Laboratory for Research and Utilization of Qinghai Tibetan Plateau Germplasm Resources, Qinghai Provincial Key Laboratory of Crop Molecular Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China.
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Shang J, Xu A, Bi M, Zhang Y, Li F, Liu JX. A review: simulation tools for genome-wide interaction studies. Brief Funct Genomics 2024; 23:745-753. [PMID: 39173096 DOI: 10.1093/bfgp/elae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/25/2024] [Accepted: 08/10/2024] [Indexed: 08/24/2024] Open
Abstract
Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.
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Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Anqi Xu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Mingyuan Bi
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266114, China
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Sun L, Lai M, Ghouri F, Nawaz MA, Ali F, Baloch FS, Nadeem MA, Aasim M, Shahid MQ. Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:2676. [PMID: 39409546 PMCID: PMC11478383 DOI: 10.3390/plants13192676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 09/08/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024]
Abstract
With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops' audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research.
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Affiliation(s)
- Lixia Sun
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou 510642, China; (L.S.); (M.L.); (F.G.)
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Mingyu Lai
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou 510642, China; (L.S.); (M.L.); (F.G.)
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Fozia Ghouri
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou 510642, China; (L.S.); (M.L.); (F.G.)
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Muhammad Amjad Nawaz
- Education Scientific Center of Nanotechnology, Far Eastern Federal University, 690091 Vladivostok, Russia;
| | - Fawad Ali
- School of Tropical Agriculture and Forestry, Hainan University, Sanya 572025, China;
| | - Faheem Shehzad Baloch
- Dapartment of Biotechnology, Faculty of Science, Mersin University, Mersin 33343, Türkiye;
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58140, Türkiye; (M.A.N.); (M.A.)
| | - Muhammad Aasim
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58140, Türkiye; (M.A.N.); (M.A.)
| | - Muhammad Qasim Shahid
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou 510642, China; (L.S.); (M.L.); (F.G.)
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
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Kontou PI, Bagos PG. The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Min 2024; 17:31. [PMID: 39238044 PMCID: PMC11375927 DOI: 10.1186/s13040-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 08/27/2024] [Indexed: 09/07/2024] Open
Abstract
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.
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Affiliation(s)
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece.
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Li J, Liu L, Luo Q, Zhou W, Zhu Y, Jiang W. Exploring the causal relationship between immune cell and all-cause heart failure: a Mendelian randomization study. Front Cardiovasc Med 2024; 11:1363200. [PMID: 38938655 PMCID: PMC11210391 DOI: 10.3389/fcvm.2024.1363200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 06/06/2024] [Indexed: 06/29/2024] Open
Abstract
Background and objectives Heart failure (HF) is a disease with numerous genetic and environmental factors that affect it. The results of previous studies indicated that immune phenotypes are associated with HF, but there have been inconclusive studies regarding a causal relationship. Therefore, Mendelian randomization (MR) analyses were undertaken to confirm the causal connections between immune phenotypes and HF, providing genetic evidence supporting the association of immune cell factors with HF risk. Methods We selected instrumental variables that met the criteria based on data from the results of genome-wide association studies (GWAS) of immune phenotype and all-cause HF. An evaluation of the causal association between 731 immune cell factors and HF risk was carried out using the inverse variance weighted (IVW), MR-Egger regression (MR-Egger), and weighted median (WM) analysis methods. To determine the horizontal pleiotropy, heterogeneity, and stability of the genetic variants, the MR-Egger intercept test, Cochran's Q test, MR-PRESSO, and leave-one-out sensitivity analysis were performed. Results MR principal method (IVW) analysis showed that a total of 38 immune cell-related factors were significantly causally associated with HF. Further analyses combining three methods (IVW, MR-Egger and WME) showed that six exposure factors significantly associated with heart failure, as shown below. The effect of Dendritic cell Absolute Count, CD62l- CD86+ myeloid Dendritic cell Absolute Count, CD62l- CD86+ myeloid Dendritic cell% Dendritic cell, CD39+ CD8+ T cell% CD8+ T cell, CD3 on Central Memory CD4+ T cell on heart failure was positive. Whereas, a reverse effect was observed for CD14+ CD16+ monocyte% monocyte. Conclusion We investigated the causal relationship between immune phenotypes and all-cause HF. According to the results, Dendritic cell Absolute Count, CD62l- CD86+ myeloid Dendritic cell Absolute Count, CD62l- CD86+ myeloid Dendritic cell% Dendritic cell, CD39+ CD8+ T cell% CD8+ T cell, CD3 on Central Memory CD4+ T cell aggravate HF, and the risk of HF is decreased by CD14+ CD16+ monocyte% monocyte. These phenotypes may serve as new biomarkers, providing new therapeutic insights for the prevention and treatment of all-cause HF.
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Affiliation(s)
| | | | | | | | - Yao Zhu
- Department of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Weimin Jiang
- Department of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
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Reyes-Pérez P, Hernández-Ledesma AL, Román-López TV, García-Vilchis B, Ramírez-González D, Lázaro-Figueroa A, Martinez D, Flores-Ocampo V, Espinosa-Méndez IM, Tinajero-Nieto L, Peña-Ayala A, Morelos-Figaredo E, Guerra-Galicia CM, Torres-Valdez E, Gordillo-Huerta MV, Gandarilla-Martínez NA, Salinas-Barboza K, Félix-Rodríguez G, Frontana-Vázquez G, Matuk-Pérez Y, Estrada-Bellmann I, Alpizar-Rodríguez D, Rodríguez-Violante M, Rentería ME, Ruíz-Contreras AE, Alcauter S, Medina-Rivera A. Building national patient registries in Mexico: insights from the MexOMICS Consortium. Front Digit Health 2024; 6:1344103. [PMID: 38895515 PMCID: PMC11183280 DOI: 10.3389/fdgth.2024.1344103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/01/2024] [Indexed: 06/21/2024] Open
Abstract
Objective To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population. Methods Since 2019, the MexOMICS Consortium has established three electronic-based registries: the Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX), and the Mexican Parkinson's Research Network (MEX-PD), designed and implemented using the Research Electronic Data Capture web-based application. Participants were enrolled through voluntary participation and on-site engagement with medical specialists. We also acquired DNA samples and Magnetic Resonance Imaging scans in subsets of participants. Results The registries have successfully enrolled a large number of participants from a variety of regions within Mexico: TwinsMX (n = 2,915), LupusRGMX (n = 1,761) and MEX-PD (n = 750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected DNA samples to study the genetic biomarkers across the three registries. Cognitive function has been assessed with the Montreal Cognitive Assessment in a subset of 376 MEX-PD participants. Furthermore, a subset of 267 twins have participated in cognitive evaluations with the Creyos platform and in MRI sessions acquiring structural, functional, and spectroscopy brain imaging; comparable evaluations are planned for LupusRGMX and MEX-PD. Conclusions The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.
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Affiliation(s)
- Paula Reyes-Pérez
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Ana Laura Hernández-Ledesma
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Talía V. Román-López
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Brisa García-Vilchis
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Diego Ramírez-González
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Alejandra Lázaro-Figueroa
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Domingo Martinez
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
- Unidad de Genómica Avanzada, Langebio, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Irapuato, Mexico
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Victor Flores-Ocampo
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Ian M. Espinosa-Méndez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Lizbet Tinajero-Nieto
- Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Santiago de Querétaro, Mexico
| | - Angélica Peña-Ayala
- Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Santiago de Querétaro, Mexico
- Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Ciudad de México, Mexico
| | - Eugenia Morelos-Figaredo
- Hospital Regional, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Morelia, Mexico
| | | | | | - María Vanessa Gordillo-Huerta
- Hospital General Querétaro, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Santiago de Querétaro, Mexico
| | | | | | | | | | - Yamil Matuk-Pérez
- Facultad de Medicina, Universidad Autónoma de Querétaro. Unidad de Neurociencias, Hospital Angeles Centro Sur, Santiago de Querétaro, Mexico
| | - Ingrid Estrada-Bellmann
- Movement Disorders Clinic, Neurology Division, Internal Medicine Department, University Hospital “Dr. José E. González”, Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | | | - Mayela Rodríguez-Violante
- Laboratorio Clínico de Enfermedades Neurodegenerativas, Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez, Mexico City, Mexico
| | - Miguel E. Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alejandra E. Ruíz-Contreras
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Sarael Alcauter
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
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Xiang B, Li J, Deng Y, Wang J. Causal relationship between immune cells and aortic aneurysms: a Mendelian randomization study. Eur J Cardiothorac Surg 2024; 65:ezae229. [PMID: 38833686 DOI: 10.1093/ejcts/ezae229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/06/2024] [Accepted: 06/03/2024] [Indexed: 06/06/2024] Open
Abstract
OBJECTIVES The causal association between immune cell traits and aortic aneurysm remains unknown. METHODS We performed a bidirectional two-sample Mendelian randomization analysis to explore the causality between 731 immune cell characteristics and the risk of abdominal aortic aneurysm and thoracic aortic aneurysms through publicly available genetic data, respectively. To examine heterogeneity and horizontal pleiotropy, Cochran's Q test and MR-Egger intercept were utilized. Additionally, multivariable Mendelian randomization analysis and meta-analysis were performed in further analysis. RESULTS We found that 20 immune phenotypes had a suggestive causality on abdominal aortic aneurysm, and 15 immune phenotypes had a suggestive causal effect on thoracic aortic aneurysm. After further false discovery rate adjustment (q value <0.1), CD20 on IgD+ CD38- B cell (q = 0.053) and CD127 on CD28+ CD4+ T cell (q = 0.096) were associated with an increased risk of abdominal aortic aneurysm, respectively, indicating a significant causality between them. After adjusting for smoking, there is still statistical significance between CD127 on CD28+ CD4+ T cell and abdominal aortic aneurysm. However, after adjusting for lipids, no statistical significance can be observed between CD127 on CD28+ CD4+ T cells and abdominal aortic aneurysm. Furthermore, there is still statistical significance between CD20 on IgD+ CD38- B cells and abdominal aortic aneurysm after adjusting for lipids and smoking, which was further identified by meta-analysis. CONCLUSIONS We found a causal association between immune cell traits and aortic aneurysm by genetic methods, thus providing new avenues for future mechanism studies.
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Affiliation(s)
- Bitao Xiang
- Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jia Li
- Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yao Deng
- Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Junjie Wang
- Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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11
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He C, Wu X, Lin L, Liu C, Li M, Jiang C, Xu Z, Fang B. Causal relationship between atrial fibrillation and stroke risk: a Mendelian randomization. J Stroke Cerebrovasc Dis 2023; 32:107446. [PMID: 38442074 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/10/2023] [Accepted: 10/24/2023] [Indexed: 03/07/2024] Open
Abstract
OBJECTIVES This study aimed to investigate the causal relationship between Atrial Fibrillation (AF) and the risk of Stroke using a Mendelian randomization (MR) approach. METHODS A two-sample MR analysis was conducted using publicly available genome-wide association study (GWAS) summary statistics data. In this analysis, genetic variants associated with AF were used as instrumental variables to estimate the causal effect. The inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were employed for estimation. Additionally, sensitivity analysis was performed using the leave-one-out method. RESULTS The analysis included 87 single nucleotide polymorphisms (SNPs) associated with AF. The results from the IVW method indicated a positive association between genetic predisposition to AF and the risk of stroke (OR 1.002, 95 % CI 1.001-1.003, P < 0.001). The weighted median and MR-Egger methods showed consistent results (weighted median: OR 1.001, 95 % CI 1.000-1.002, P = 0.034; MR-Egger: OR 1.001, 95 % CI 1.000-1.003, P = 0.086). Sensitivity analysis demonstrated that no individual SNP significantly influenced the causal inference. CONCLUSIONS This study provides evidence of a causal relationship between AF and an elevated risk of stroke. These findings emphasize the significance of managing AF in order to prevent and treat strokes. Additional research is required to better understand the underlying mechanisms of this causal association.
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Affiliation(s)
- Chenming He
- Shaanxi University of Chinese Medicine, Xianyang, China
| | - Xinxin Wu
- Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ling Lin
- Huizhou Hospital of Guangzhou University of Chinese Medicine, Huizhou, China
| | - Changya Liu
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Min Li
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chao Jiang
- The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China
| | - Zhongju Xu
- Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Bangjiang Fang
- Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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Wu X, Pan J, Zhu Y, Huang H. Research progress and challenges of preimplantation genetic testing for polygenic diseases. Zhejiang Da Xue Xue Bao Yi Xue Ban 2023; 53:280-287. [PMID: 37987034 PMCID: PMC11348693 DOI: 10.3724/zdxbyxb-2023-0440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/29/2023] [Indexed: 11/22/2023]
Abstract
Preimplantation genetic testing is an important part in assisted reproductive technology, which can block the intergenerational inheritance of a single gene or chromosomal diseases. Preimplantation genetic testing for polygenic disease risk (PGT-P) is one of the latest developments in the field. With the development of artificial intelligence and genetic detection technology, PGT-P can be used to analyze genetic material, calculate polygenic risk scores and convert these into incidence probability. Embryos with relatively low incidence probability can be screened for transfer, in order to reduce the possibility that the offspring suffers from the disease in the future. This has significant clinical and social significance. At present, PGT-P has been applied clinically and made phased progress at home and abroad. But as a developing technology, PGT-P still has some technical limitations as unstable results, environmental influences and racial differences cannot be ruled out. From the ethical perspective, if the screening indications are not strictly regulated, it is likely to cause new social problems. In this paper, we review the technical details and recent progress in PGT-P, and discuss the prospects of its future development, especially how to establish a complete and suitable screening model for Chinese population.
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Affiliation(s)
- Xiaojing Wu
- Zhejiang University School of Medicine, Hangzhou 310058, China.
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Jiexue Pan
- Department of Reproductive Medicine, Obstetrics and Gynecology Hospital, Fudan University, Shanghai 200090, China
| | - Yimin Zhu
- Zhejiang University School of Medicine, Hangzhou 310058, China.
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
- Ministry of Education Key Laboratory of Reproductive Genetics, Department of Reproductive Endocrinology, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Hefeng Huang
- Zhejiang University School of Medicine, Hangzhou 310058, China.
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
- Department of Reproductive Medicine, Obstetrics and Gynecology Hospital, Fudan University, Shanghai 200090, China.
- Ministry of Education Key Laboratory of Reproductive Genetics, Department of Reproductive Endocrinology, Zhejiang University School of Medicine, Hangzhou 310006, China.
- Shanghai Key Laboratory of Embryo Original Diseases, Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences, Shanghai 200030, China.
- Institute of Reproduction and Development, Obstetrics and Gynecology Hospital, Fudan University, Shanghai 200030, China.
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Lin C, Li J, Deng Y, Li X, Li S. Effect of obesity, lipids and adipokines on allergic rhinitis risk: a Mendelian randomization study. Braz J Otorhinolaryngol 2023; 89:101306. [PMID: 37634407 PMCID: PMC10472243 DOI: 10.1016/j.bjorl.2023.101306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 08/29/2023] Open
Abstract
OBJECTIVES Observational studies suggested that obesity may promote the development of allergic rhinitis. The aim of this study was to explore the association of obesity, lipids and adipokines with this allergic disease at the genetic level using Mendelian randomization strategies. METHODS Summary data for three obesity indicators (such as body mass index), eight lipid indicators (such as triglycerides) and six adipokines (such as interleukin-6 and adipocyte fatty acid-binding protein) were collected, and suitable instrumental variables were extracted from these summary data according to the three main assumptions of Mendelian randomization. Three Mendelian randomization methods (such as inverse variance weighted) were used to detect the casual effect of the above indicators on allergic rhinitis risk. Sensitivity analyses were performed to assess heterogeneity and horizontal pleiotropy. RESULTS After Bonferroni correction, the inverse variance weighted reported that elevated levels of interleukin-6 and adipocyte fatty acid-binding protein were nominally associated with the decreased risk of allergic rhinitis (OR = 0.870, 95% CI 0.765-0.990, p = 0.035; OR = 0.732, 95% CI 0.551-0.973, p = 0.032). The other Mendelian randomization methods supported these results. Obesity, lipids and other adipokines were not related to this allergic disease. Sensitivity analyses found no heterogeneity and horizontal pleiotropy in the study. CONCLUSION The study provided some interesting, but not sufficient, evidence to suggest that interleukin-6 and adipocyte fatty acid-binding protein might play a protective role in the development of allergic rhinitis at the genetic level. These findings should be validated by more research. LEVEL OF EVIDENCE This was a Mendelian randomized study with a level of evidence second only to clinical randomized trials, and higher than cohort and case-control studies.
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Affiliation(s)
- Chenxi Lin
- Department of Otorhinolaryngology, Meizhou People's Hospital, Meizhou City, Guangdong Province, China
| | - Jia Li
- Department of Otorhinolaryngology, Meizhou People's Hospital, Meizhou City, Guangdong Province, China.
| | - Ye Deng
- Department of Otorhinolaryngology, Meizhou People's Hospital, Meizhou City, Guangdong Province, China
| | - Xiongwen Li
- Department of Otorhinolaryngology, Meizhou People's Hospital, Meizhou City, Guangdong Province, China
| | - Shirong Li
- Department of Otorhinolaryngology, Meizhou People's Hospital, Meizhou City, Guangdong Province, China
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Zhou Y, Sun S, Ling T, Chen Y, Zhou R, You Q. The role of fibroblast growth factor 18 in cancers: functions and signaling pathways. Front Oncol 2023; 13:1124520. [PMID: 37228502 PMCID: PMC10203589 DOI: 10.3389/fonc.2023.1124520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/21/2023] [Indexed: 05/27/2023] Open
Abstract
Fibroblast growth factor 18(FGF18) is a member of the fibroblast growth factor family (FGFs). FGF18 is a class of bioactive substances that can conduct biological signals, regulate cell growth, participate in tissue repair and other functions, and can promote the occurrence and development of different types of malignant tumors through various mechanisms. In this review, we focus on recent studies of FGF18 in the diagnosis, treatment, and prognosis of tumors in digestive, reproductive, urinary, respiratory, motor, and pediatric systems. These findings suggest that FGF18 may play an increasingly important role in the clinical evaluation of these malignancies. Overall, FGF18 can function as an important oncogene at different gene and protein levels, and can be used as a potential new therapeutic target and prognostic biomarker for these tumors.
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Affiliation(s)
- Yiming Zhou
- Department of Biotherapy, Medical Center for Digestive Diseases, Second Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Sizheng Sun
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Tao Ling
- Department of Biotherapy, Medical Center for Digestive Diseases, Second Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Yongzhen Chen
- Second Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Rongzhong Zhou
- Department of Ophthalmology, Zaoyang First People’s Hosipital, Zaoyang, China
| | - Qiang You
- Department of Biotherapy, Medical Center for Digestive Diseases, Second Affiliated Hospital, Nanjing Medical University, Nanjing, China
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15
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Zimmermann E, Distl O. SNP-Based Heritability of Osteochondrosis Dissecans in Hanoverian Warmblood Horses. Animals (Basel) 2023; 13:ani13091462. [PMID: 37174498 PMCID: PMC10177438 DOI: 10.3390/ani13091462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/24/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Before the genomics era, heritability estimates were performed using pedigree data. Data collection for pedigree analysis is time consuming and holds the risk of incorrect or incomplete data. With the availability of SNP-based arrays, heritability can now be estimated based on genotyping data. We used SNP array and 1.6 million imputed genotype data with different minor allele frequency restrictions to estimate heritabilities for osteochondrosis dissecans in the fetlock, hock and stifle joints of 446 Hanoverian warmblood horses. SNP-based heritabilities were estimated using a genomic restricted maximum likelihood (GREML) method and accounting for patterns of regional linkage disequilibrium in the equine genome. In addition, we employed GREML for family data to account for different degrees of relatedness in the study population. Our results indicate that we were able to capture a larger proportion of additive genetic variance compared to pedigree-based estimates in the same population of Hanoverian horses. Heritability estimates on the linear scale for fetlock-, hock- and stifle-osteochondrosis dissecans were 0.41-0.43, 0.62-0.63, and 0.23-0.25, respectively, with standard errors of 0.11-0.14. Accounting for linkage disequilibrium patterns had an upward effect on the imputed data and a downward impact on the SNP array genotype data. GREML for family data resulted in higher heritability estimates for fetlock-osteochondrosis dissecans and slightly higher estimates for hock-osteochondrosis dissecans, but had no effect on stifle-osteochondrosis dissecans. The largest and most consistent heritability estimates were obtained when we employed GREML for family data with genomic relationship matrices weighted through patterns of regional linkage disequilibrium. Estimation of SNP-based heritability should be recommended for traits that can only be phenotyped in smaller samples or are cost-effective.
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Affiliation(s)
- Elisa Zimmermann
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany
| | - Ottmar Distl
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), 30559 Hannover, Germany
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16
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Abstract
Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability (h SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimateh SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.
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Affiliation(s)
- Amit K. Srivastava
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, USA; The Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, USA; March of Dimes Prematurity Research Center Ohio Collaborative, USA; Department of Pediatrics, University of Cincinnati College of Medicine, USA
| | - Scott M. Williams
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, USA; Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, USA; Institute of Computational Biology, Case Western Reserve University, USA
| | - Ge Zhang
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, USA; The Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, USA; March of Dimes Prematurity Research Center Ohio Collaborative, USA; Department of Pediatrics, University of Cincinnati College of Medicine, USA
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17
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Ou YN, Wu BS, Ge YJ, Zhang Y, Jiang YC, Kuo K, Yang L, Tan L, Feng JF, Cheng W, Yu JT. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl Psychiatry 2023; 13:90. [PMID: 36906575 PMCID: PMC10008562 DOI: 10.1038/s41398-023-02387-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/13/2023] Open
Abstract
The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.
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Affiliation(s)
- Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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Wu H, Yi Q, Ma W, Yan L, Guan S, Wang L, Yang G, Tan X, Ji P, Liu G. Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat ( Capra hircus) as the molecular breeding markers. Front Genet 2023; 14:1118367. [PMID: 37021000 PMCID: PMC10067595 DOI: 10.3389/fgene.2023.1118367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/28/2023] [Indexed: 04/07/2023] Open
Abstract
Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study.
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Affiliation(s)
- Hao Wu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
- Sanya Institute of China Agricultural University, Sanya, China
- Hainan Yazhou Bay Seed Laboratory, Sanya, China
| | - Qi Yi
- Sanya Institute of China Agricultural University, Sanya, China
| | - Wenkui Ma
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Laiqing Yan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shengyu Guan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Likai Wang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guang Yang
- Sanya Institute of China Agricultural University, Sanya, China
| | - Xinxing Tan
- Inner Mongolia Grassland Hongbao Food Co., Ltd., Bayannaoer, China
| | - Pengyun Ji
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guoshi Liu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
- Sanya Institute of China Agricultural University, Sanya, China
- *Correspondence: Guoshi Liu,
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19
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Yin X, Lin H, Lin L, Miao L, He J, Zhuo Z. LncRNAs and CircRNAs in cancer. MedComm (Beijing) 2022; 3:e141. [PMID: 35592755 PMCID: PMC9099016 DOI: 10.1002/mco2.141] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Xin Yin
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China
- College of Pharmacy Jinan University Guangzhou Guangdong China
| | - Huiran Lin
- Faculty of Medicine Macau University of Science and Technology Macau China
| | - Lei Lin
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China
| | - Lei Miao
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China
| | - Jing He
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China
| | - Zhenjian Zhuo
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center Guangzhou Medical University Guangzhou Guangdong China
- Laboratory Animal Center, School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen China
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