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      Impact of LDLR polymorphisms on lipid levels and atorvastatin’s efficacy in a northern Chinese adult Han cohort with dyslipidemia

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          Abstract

          Background

          Dyslipidemia, a significant risk factor for atherosclerotic cardiovascular disease (ASCVD), is influenced by genetic variations, particularly those in the low-density lipoprotein receptor (LDLR) gene. This study aimed to elucidate the effects of LDLR polymorphisms on baseline serum lipid levels and the therapeutic efficacy of atorvastatin in an adult Han population in northern China with dyslipidemia.

          Methods

          In this study, 255 Han Chinese adults receiving atorvastatin therapy were examined and followed up. The 3’ untranslated region (UTR) of the LDLR gene was sequenced to identify polymorphisms. The associations between gene polymorphisms and serum lipid levels, as well as changes in lipid levels after intervention, were evaluated using the Wilcoxon rank sum test, with a P < 0.05 indicating statistical significance. Assessment of linkage disequilibrium patterns and haplotype structures was conducted utilizing Haploview.

          Results

          Eleven distinct polymorphisms at LDLR 3’ UTR were identified. Seven polymorphisms (rs1433099, rs14158, rs2738466, rs5742911, rs17249057, rs55971831, and rs568219285) were correlated with the baseline serum lipid levels ( P < 0.05). In particular, four polymorphisms (rs14158, rs2738466, rs5742911, and rs17249057) were in strong linkage disequilibrium (r 2 = 1), and patients with the AGGC haplotype had higher TC and LDL-C levels at baseline. Three polymorphisms (rs1433099, rs2738467, and rs7254521) were correlated with the therapeutic efficacy of atorvastatin ( P < 0.05). Furthermore, carriers of the rs2738467 T allele demonstrated a significantly greater reduction in low-density lipoprotein cholesterol (LDL-C) levels post-atorvastatin treatment ( P = 0.03), indicating a potentially crucial genetic influence on therapeutic outcomes. Two polymorphisms (rs751672818 and rs566918949) were neither correlated with the baseline serum lipid levels nor atorvastatin’s efficacy.

          Conclusions

          This research outlined the complex genetic architecture surrounding LDLR 3’ UTR polymorphisms and their role in lipid metabolism and the response to atorvastatin treatment in adult Han Chinese patients with dyslipidemia, highlighting the importance of genetic profiling in enhancing tailored therapeutic strategies. Furthermore, this investigation advocates for the integration of genetic testing into the management of dyslipidemia, paving the way for customized therapeutic approaches that could significantly improve patient care.

          Trial registration

          This multicenter study was approved by the Ethics Committee of Xiangya Hospital Central South University (ethics number K22144). It was a general ethic. In addition, this study was approved by The First Hospital of Hebei Medical University (ethics number 20220418).

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12944-024-02101-4.

          Related collections

          Most cited references38

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              Fast and accurate long-read alignment with Burrows–Wheeler transform

              Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                sunnl@263.net
                husn@im.ac.cn
                cardio2004@hebmu.edu.cn
                Journal
                Lipids Health Dis
                Lipids Health Dis
                Lipids in Health and Disease
                BioMed Central (London )
                1476-511X
                14 April 2024
                14 April 2024
                2024
                : 23
                : 106
                Affiliations
                [1 ]Department of Cardiology, The First Hospital of Hebei Medical University, ( https://ror.org/04eymdx19) Shijiazhuang, Hebei China
                [2 ]Beijing E-Seq Medical Technology Co. Ltd, Beijing, China
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Microbial Resources, Institute of Microbiology, , Chinese Academy of Sciences, ; Beijing, China
                [4 ]College of Information Science and Technology, Nanjing Agricultural University, ( https://ror.org/05td3s095) Nanjing, Jiangsu Province China
                [5 ]Institute of Hypertension, People’s Hospital, Peking University, ( https://ror.org/02v51f717) Beijing, China
                [6 ]University of Chinese Academy of Sciences, ( https://ror.org/05qbk4x57) Beijing, China
                Author information
                http://orcid.org/0000-0002-1331-8515
                http://orcid.org/0000-0003-1221-3698
                Article
                2101
                10.1186/s12944-024-02101-4
                11016223
                8d8ea0e6-84dc-4d55-b280-d14aeb85a450
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 November 2023
                : 4 April 2024
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                Research
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                © BioMed Central Ltd., part of Springer Nature 2024

                Biochemistry
                pharmacogenetics,ldlr polymorphisms,atorvastatin,dyslipidemia,han chinese
                Biochemistry
                pharmacogenetics, ldlr polymorphisms, atorvastatin, dyslipidemia, han chinese

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