New genetic variants associated with major adverse cardiovascular events in patients with acute coronary syndromes and treated with clopidogrel and aspirin
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Abstract
Although a few studies have reported the effects of several polymorphisms on major
adverse cardiovascular events (MACE) in patients with acute coronary syndromes (ACS)
and those undergoing percutaneous coronary intervention (PCI), these genotypes account
for only a small fraction of the variation and evidence is insufficient. This study
aims to identify new genetic variants associated with MACE end point during the 18-month
follow-up period by a two-stage large-scale sequencing data, including high-depth
whole exome sequencing of 168 patients in the discovery cohort and high-depth targeted
sequencing of 1793 patients in the replication cohort. We discovered eight new genotypes
and their genes associated with MACE in patients with ACS, including
MYOM2 (rs17064642),
WDR24 (rs11640115),
NECAB1 (rs74569896),
EFR3A (rs4736529),
AGAP3 (rs75750968),
ZDHHC3 (rs3749187),
ECHS1 (rs140410716), and
KRTAP10-4 (rs201441480). Notably, the expressions of
MYOM2 and
ECHS1 are downregulated in both animal models and patients with phenotypes related to MACE.
Importantly, we developed the first superior classifier for predicting 18-month MACE
and achieved high predictive performance (AUC ranged between 0.92 and 0.94 for three
machine-learning methods). Our findings shed light on the pathogenesis of cardiovascular
outcomes and may help the clinician to make a decision on the therapeutic intervention
for ACS patients.
Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
[2
]GRID grid.21155.32, ISNI 0000 0001 2034 1839, China National GeneBank-Shenzhen, , BGI-Shenzhen, ; Shenzhen, 518083 China
[3
]GRID grid.79703.3a, ISNI 0000 0004 1764 3838, School of Biology and Biological Engineering, , South China University of Technology, ; Guangzhou, 510006 China
[4
]BGI-tech, BGI-Wuhan, Wuhan, 430075 Hubei China
[5
]GRID grid.410643.4, Guangdong Provincial People’s Hospital, , Guangdong Academy of Medical Sciences, ; Guangzhou, China
[6
]GRID grid.410643.4, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong
Cardiovascular Institute, Guangdong Provincial People’s Hospital, , Guangdong Academy of Medical Sciences, ; Guangzhou, Guangdong 510080 P.R. China
[7
]GRID grid.13402.34, ISNI 0000 0004 1759 700X, James D. Watson Institute of Genome Sciences, ; Hangzhou, 310058 China
[8
]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International
Graduate School, , Tsinghua University, ; Shenzhen, 518055 China
[9
]GRID grid.410643.4, Department of Pharmacy, Guangdong Provincial People’s Hospital, , Guangdong Academy of Medical Sciences, ; Guangzhou, Guangdong 510080 P.R. China
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History
Date
received
: 31
January
2020
Date
revision received
: 3
June
2021
Date
accepted
: 10
June
2021
Funding
Funded by: The research was supported by the National key R&D program (No. 2017YFC0909301, 2016YFC0905003),
National Nature Science Foundation of China (No.81872934, 81673514, 81373486),Key-Area
Research and Development Program of Guangdong Province, China (No. 2019B020229003),
Science and Technology Development Projects of Guangdong Province, China (No. 2013B021800157,
2016B090918114,2017B030314041), and Science and Technology Development Projects of
Guangzhou, Guangdong, China (No. 201510010236).
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