Common Complex Diseases Research Articles

A common transcriptional mechanism involving R-loop and RNA abasic site regulates an enhancer RNA of APOE.

RNA is modified by hundreds of chemical reactions and folds into innumerable shapes. However, the regulatory role of RNA sequence and structure and how dysregulation leads to diseases remain largely unknown. Here, we uncovered a mechanism where RNA abasic sites in R-loops regulate transcription by pausing RNA polymerase II. We found an enhancer RNA, AANCR, that regulates the transcription and expression of apolipoprotein E (APOE). In some human cells such as fibroblasts, AANCR is folded into an R-loop and modified by N-glycosidic cleavage; in this form, AANCR is a partially transcribed nonfunctional enhancer and APOE is not expressed. In contrast, in other cell types including hepatocytes and under stress, AANCR does not form a stable R-loop as its sequence is not modified, so it is transcribed into a full-length enhancer that promotes APOE expression. DNA sequence variants in AANCR are associated significantly with APOE expression and Alzheimer's Disease, thus AANCR is a modifier of Alzheimer's Disease. Besides AANCR, thousands of noncoding RNAs are regulated by abasic sites in R-loops. Together our data reveal the essentiality of the folding and modification of RNA in cellular regulation and demonstrate that dysregulation underlies common complex diseases such as Alzheimer's disease.

Abstract 13560: System Gene Regulatory Network-Based Drug Repurposing Combined With Preclinical Molecular Imaging Identifies Anti-Atherosclerotic Properties of Auranofin

Introduction: The identification of new treatments for atherosclerosis (ACVD), a common complex disease, is a daunting task because sets of genes, rather than individual genes, control cell functions. Using systems genetics, our team identified gene regulatory network (GRN42) that is active in the human atherosclerotic arterial wall and is involved in the regulation of foam cell formation. Hypothesis: A network-driven drug repurposing approach combined with rigorous preclinical validation will identify new uses for existing drugs to treat ACVD. Methods: We developed a GRN42-based computational drug repurposing pipeline to infer new uses for existing drugs. Selected compounds were screened by measuring their ability to inhibit foam cell formation in vitro using THP-1-derived macrophages. The in vivo efficacy was validated in ApoE-/- mice fed a western diet by histology and in atherosclerotic rabbits using histology and non-invasive 18 F-FDG PET-MR imaging. Results: 30 candidate compounds were predicted to influence the function of GRN42 and 5 were selected for in vitro screening based on their prediction scores (Figure 1A). As expected, vitamin D reduced cholesteryl esters accumulation in foam cells. Auranofin, and FDA-approved drug for rheumatoid arthritis reduced foam cell formation. As predicted methyl-fasudil had no effect, while nicardipine and flutamide increased foam cell formation (Figure 1B). Auranofin reduced plaque burden in ApoE-/- mice (Figure 1C) and in rabbits reduced lipid accumulation (Figure 1D) and inflammation shown as reduced 18 F-FDG uptake in the arterial wall (Figure 1E). Conclusions: Our results provide robust evidence that a GRN-based computational drug repurposing approach, when combined with robust preclinical validation using translational molecular imaging, can successfully identify new uses of existing drugs for the treatment of ACVD.

Artificial Intelligence: Use in Clinical and Genomic Diagnostics

The development of computer systems that are capable of carrying out tasks that typically require human intelligence is known as artificial intelligence (AI). Recent and quickly rising interest in medical AI applications is a result of AI software and technology improvements, especially deep learning algorithms and the graphics processing units (GPUs) that enable their training. While other AI subtypes have started to show similar promise in different diagnostic modalities, AI-based computer vision methods are poised to change image-based diagnostics in clinical diagnostics. Large and complicated genomic datasets are processed using a particular form of AI algorithm known as deep learning in various fields, such as clinical genomics. In this review, we first provide an overview of the primary categories of issues that AI systems are best adapted to address, followed by a description of the clinical diagnostic tasks that are aided by these solutions. Then, we concentrate on recently developed techniques for certain clinical genomics applications, such as variant calling, genome annotation and variant categorization, and phenotype-to-genotype correlation. We conclude by talking about the future potential of AI in individualized medicine applications, particularly for risk prediction in common complex diseases, as well as the issues, constraints, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those using data from genomics and human genetics.

The All of Us Research Program: Data quality, utility, and diversity

SummaryThe All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Clinical Implementation of Combined Monogenic and Polygenic Risk Disclosure for Coronary Artery Disease

BackgroundState-of-the-art genetic risk interpretation for a common complex disease such as coronary artery disease (CAD) requires assessment for both monogenic variants—such as those related to familial hypercholesterolemia—as well as the cumulative impact of many common variants, as quantified by a polygenic score. ObjectivesThe objective of the study was to describe a combined monogenic and polygenic CAD risk assessment program and examine its impact on patient understanding and changes to clinical management. MethodsStudy participants attended an initial visit in a preventive genomics clinic and a disclosure visit to discuss results and recommendations, primarily via telemedicine. Digital postdisclosure surveys and chart review evaluated the impact of disclosure. ResultsThere were 60 participants (mean age 51 years, 37% women, 72% with no known CAD), including 30 (50%) referred by their cardiologists and 30 (50%) self-referred. Two (3%) participants had a monogenic variant pathogenic for familial hypercholesterolemia, and 19 (32%) had a high polygenic score in the top quintile of the population distribution. In a postdisclosure survey, both the genetic test report (in 80% of participants) and the discussion with the clinician (in 89% of participants) were ranked as very or extremely helpful in understanding the result. Of the 42 participants without CAD, 17 or 40% had a change in management, including statin initiation, statin intensification, or coronary imaging. ConclusionsCombined monogenic and polygenic assessments for CAD risk provided by preventive genomics clinics are beneficial for patients and result in changes in management in a significant portion of patients.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits.

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.

Quality matters: International standards for biobanking.

Human biospecimens provide the basis for research, leading to a better understanding of human disease biology and discovery of new treatments that are tailored to individual patients with cancer or other common complex diseases. The collection, processing, preservation, storage and providing access to these resources are key activities of biobanks. Biobanks must ensure proper quality of samples and data, ethical and legal compliance as well as transparent and efficient access procedures. The standards for biobanking outlined herein are intended to be implemented in biobanks and to supply researchers with high‐quality samples fitted for an intended use.

Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study.

We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.

What are the challenges with multi-targeted drug design for complex diseases?

ABSTRACT Introduction Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account. Areas covered In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases. Expert opinion Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation.

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations

ObjectiveDeep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome. MethodsHere, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package. ResultsWe describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10−11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease. ConclusionsOur proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.

Polygenic scores in biomedical research.

Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use. In this Viewpoint, we asked six experts to give their opinions on the utility of polygenic scores, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

Polympact: exploring functional relations among common human genetic variants.

In the last years, many studies were able to identify associations between common genetic variants and complex diseases. However, the mechanistic biological links explaining these associations are still mostly unknown. Common variants are usually associated with a relatively small effect size, suggesting that interactions among multiple variants might be a major genetic component of complex diseases. Hence, elucidating the presence of functional relations among variants may be fundamental to identify putative variants’ interactions. To this aim, we developed Polympact, a web-based resource that allows to explore functional relations among human common variants by exploiting variants’ functional element landscape, their impact on transcription factor binding motifs, and their effect on transcript levels of protein-coding genes. Polympact characterizes over 18 million common variants and allows to explore putative relations by combining clustering analysis and innovative similarity and interaction network models. The properties of the network models were studied and the utility of Polympact was demonstrated by analysing the rich sets of Breast Cancer and Alzheimer's GWAS variants. We identified relations among multiple variants, suggesting putative interactions. Polympact is freely available at bcglab.cibio.unitn.it/polympact.

Transcriptome sequencing reveals circRNA expression profile in Parkinson's disease-like mice after aerobic exercise.

Parkinson's disease (PD) is a common complex neurodegenerative disease, and aerobic exercise (EX) has potential to improve motor dysfunction. This study aimed to explore whether EX acts on PD in mice mode. Mice were administered 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and subjected to a 4-week physical exercise regimen (EX-PD group) and underwent RNA-Seq. Here, MPTP caused PD, which was characterized by neuron shrinkage and behavioral deficits, whereas EX improved PD by rescuing neuronal survival and motor function in mice. Moreover, circRNA expression profiles identified a total of 142 differentially expressed circRNAs (DEcircRNAs) between PD and EX-PD group. These DEcircRNAs were mainly involved in PD, dopaminergic synapses, and calcium signaling pathways. The expression of circZfp827 and circTshz2 were significantly elevated in PD group while reduced owing to EX intervention. In contrast, EX intervention significantly restored decline in circHivep2 expression due to PD. The circRNA-miRNA-mRNA network suggested that circZfp827, circHivep2, and circTshz2 were involved in ceRNA mechanism of EX to improve PD, and their target genes were significantly decreased after interference. The directly binding regulation relationship of circTshz2-mmu-miR-326-3p-Th was verified by double luciferase reporter assay. Our research revealed that EX improved motor behavioral deficits and pathological features of PD mice, and circRNA-based signatures are potential candidates for further assessment as PD biomarkers for improvement by EX.

Abstract 13644: Depict Analysis Suggests Enrichment of Heart Valve Tissue Expression in African Rheumatic Heart Disease Patients From the RHDGen GWAS Study Cohort

Introduction: Rheumatic heart disease (RHD) is a sequela of rheumatic fever that results in permanent heart valve damage. While it remains the primary cause of cardiac surgery in Africa, its pathophysiology is poorly understood.Genome-wide association studies (GWASs) are ‘hypothesis-free’, genetic discovery tools for common complex diseases. Recently, our African GWAS of RHD susceptibility uncovered a novel locus (11q24.1; OR=1.63; 95% CI, 1.38-1.94; P=4.36x10 -8 ) which may explain the disproportionate burden in Africans.Here we further report secondary analyses investigating tissue type expression enrichment among suggestively associated loci, to elucidate the underlying biological processes involved in RHD susceptibility. Aims and Objectives: This study sought to identify tissue types enriched in gene-set analyses associated with RHD GWAS susceptibility data in Africans. Methods: A multicenter, ethnically matched case-control GWAS was performed in 2,548 RHD cases and 2,261 controls, from eight African countries. All 4,809 participants were assessed according to the 2012 World Heart Federation (WHF) criteria for echocardiographic diagnosis of RHD. After cleaning, the GWAS data were stratified by ethnicity and an association test was run via mixed linear models in GCTA that adjusted for gender, chip type and the first ten principal components. Using the resulting GWAS summary statistics suggestive loci (P<1x10 -5 ), we conducted secondary analyses in DEPICT (Data-driven Expression Prioritized Integration for Complex Traits) to test for association with RHD and its related traits and tissues, to validate the GWAS findings. Results: DEPICT indicated promising and biologically relevant results; the most enriched tissues were heart valves (p=2.47x10 -3 ; False Discovery Rate (FDR)=<0.20). Discussion and Conclusion: Our results indicate that genetic susceptibility to RHD may involve perturbed gene expression signatures characteristic of heart valves, known for their pathogenic involvement in RHD development. Future larger studies are warranted to identify the specific genes involved.

Sequence analysis of tumor necrosis factor-α (TNF-α) promoter, 5'UTR and exon1 and association of rs361525 (-238 G>A) with BMI

Obesity is among the most common complex diseases with a high rate of morbidity and mortality globally and locally in Kuwait. Tumor Necrosis Factor-α(TNF-α) is a pro-inflammatory cytokine that is primarily secreted by monocytes/macrophage. Increased expression of TNF-α has been observed in the adipodse tissue of obese subjects that could disrupt lipid metabolism and lead to and lead to sustained obese state and obesity-related diseases. The human TNF-α promoter exhibits a high number of genetic variants, mainly single nucleotide polymorphisms (SNPs) that have been shown to influence the level of transcription in association with diseases. The human TNF-α genetic variants have never been fully reported in Arabs, therefore, we aimed to identify these variants by sequencing the TNF-α promoter, 5' UTR, and exon 1 in 290 Kuwaiti Arabs. As a result, we identified 14 genetic variants, including one novel SNP. Two promoter SNPs; rs1800750 (- 376G>A) and rs361525 (-238G>A) were found to be in strong linkage disequilibrium (r2 = 0.73) and (D′ = 1, LOD = 32). To investigate the association of rs361525 (-238G>A) with obesity, we genotyped an additional 573 samples of the general Kuwaiti population by Real-time PCR (total n=863). Linear and logistic regression analysis have not shown any significant association in carriers of the A allele of rs361525 with continuous and categorical BMI, respectively. This is the first study in the Middle East and Kuwait that has sequenced and identified the common, rare and novel genetic variants of TNF-α promoter, 5'UTR and exon 1 in Arabs.

Enhancers in disease: molecular basis and emerging treatment strategies

Enhancers are genomic sequences that play a key role in regulating tissue-specific gene expression levels. An increasing number of diseases are linked to impaired enhancer function through chromosomal rearrangement, genetic variation within enhancers, or epigenetic modulation. Here, we review how these enhancer disruptions have recently been implicated in congenital disorders, cancers, and common complex diseases and address the implications for diagnosis and treatment. Although further fundamental research into enhancer function, target genes, and context is required, enhancer-targeting drugs and gene editing approaches show great therapeutic promise for a range of diseases.

Comparing Genetic and Socioenvironmental Contributions to Ethnic Differences in C-Reactive Protein.

C-reactive protein (CRP) is a routinely measured blood biomarker for inflammation. Elevated levels of circulating CRP are associated with response to infection, risk for a number of complex common diseases, and psychosocial stress. The objective of this study was to compare the contributions of genetic ancestry, socioenvironmental factors, and inflammation-related health conditions to ethnic differences in C-reactive protein levels. We used multivariable regression to compare CRP blood serum levels between Black and White ethnic groups from the United Kingdom Biobank (UKBB) prospective cohort study. CRP serum levels are significantly associated with ethnicity in an age and sex adjusted model. Study participants who identify as Black have higher average CRP than those who identify as White, CRP increases with age, and females have higher average CRP than males. Ethnicity and sex show a significant interaction effect on CRP. Black females have higher average CRP levels than White females, whereas White males have higher average CRP than Black males. Significant associations between CRP, ethnicity, and genetic ancestry are almost completely attenuated in a fully adjusted model that includes socioenvironmental factors and inflammation-related health conditions. BMI, smoking, and socioeconomic deprivation all have high relative effects on CRP. These results indicate that socioenvironmental factors contribute more to CRP ethnic differences than genetics. Differences in CRP are associated with ethnic disparities for a number of chronic diseases, including type 2 diabetes, essential hypertension, sarcoidosis, and lupus erythematosus. Our results indicate that ethnic differences in CRP are linked to both socioenvironmental factors and numerous ethnic health disparities.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The ‘sociology of expectations’ has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the ‘problematic’ found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

Genetic Variants and Functional Analyses of the ATG16L1 Gene Promoter in Acute Myocardial Infarction.

BackgroundAcute myocardial infarction (AMI), a common complex disease caused by an interaction between genetic and environmental factors, is a serious type of coronary artery disease and is also a leading cause of death worldwide. Autophagy-related 16-like 1 (ATG16L1) is a key regulatory factor of autophagy and plays an important role in induced autophagy. In the cardiovascular system, autophagy is essential to preserve the homeostasis and function of the heart and blood vessels. No studies have hitherto examined the association between AMI and ATG16L1 gene promoter.MethodsWe conducted a case-control study, using polymerase chain reaction and sequencing techniques, dual luciferase reporter assay, and electrophoretic mobility shift assay, to analyze genetic and functional variation in the ATG16L1 gene promoter between AMI and controls. A variety of statistical analyses were used to analyze the allele and genotype frequencies and the relationship between single-nucleotide polymorphisms (SNPs) and AMI.ResultsIn all, 10 SNPs and two DNA-sequence variants (DSVs) were identified in 688 subjects, and three ATG16L1 gene promoter mutations [g.233250693 T > C (rs185213911), g.233250946 G > A (rs568956599), g.233251133 C > G (rs1301744254)] that were identified in AMI patients significantly altered the transcriptional activity of ATG16L1 gene promoter in HEH2, HEK-293, and H9c2 cells (P < 0.05). Further electrophoretic mobility shift assays indicated that the SNPs affected the binding of transcription factors (P < 0.01).ConclusionATG16L1 gene promoter mutations in AMI patients may affect the binding of transcription factors and change the transcriptional activity of the ATG16L1 gene, changing the level of autophagy and contributing to the occurrence and development of AMI as rare and low-frequency risk factors.