Alzheimer’s disease (AD) is a progressive neurological disorder commonly affecting people older than 65 years of age. The neurodegenerative progression of the disorder causes the death of the brain cells which results in brain atrophy (shrinkage). This leads to the onset of dementia which causes a decline in memory and thinking. As a result, the patient’s ability to behave normally and function independently in society is severely affected. Presently there is no cure available for AD. The treatments available currently can only improve the symptoms. Understanding of the pathogenesis and the predisposing genetic factors are needed to devise effective therapeutic measures. One of the theories of AD pathogenesis suggests the buildup of abnormal amyloid protein plaques around brain cells as a possible cause of the disease. Other theories suggest the role of cholinergic deficits, a role of abnormal neurofibrillary tangles of tau proteins, and a role of synaptic damage in the neocortex and limbic system. Also, several risk factors such as old age, head injury, infections, genetic and environmental risk factors may play a role in the disease. Several genetic risk factors that may play a role in the development of AD have been discovered over the years. These include the inheritance of variants in genes such as APP, PSEN-1, PSEN-2, and ApoE. Even after multiple years of efforts the underlying cause and mechanism of pathological changes in AD have not yet been deciphered. Over the years, multiple Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer’s disease (AD). Despite these genetic and molecular studies the causal variants, regulatory elements, genes, and pathways leading to AD pathogenesis have not been uncovered. This hinders the mechanistic understanding of AD pathogenesis and thereby the possibility of devising curative strategies. In this article, the authors have utilized integrative analysis of GWAS, chromatin interactions (promoter-capture Hi–C), and eQTL datasets myeloid cells (monocytes and macrophages) and performed further analysis using Summary data-based Mendelian Randomization (SMR) method. The authors had previously shown that AD risk alleles are enriched in myeloid-specific epigenomic annotations. In this article, they have continued their work and showed that the AD risk alleles are specifically enriched in active enhancers of monocytes, macrophages, and microglia. They also identify transcription factor binding motifs that are overrepresented in these regulatory elements. Methodology: The research study employs integrative analysis of GWAS, ChIP-Seq, ATAC-Seq, promoter-capture Hi–C, and eQTL datasets from monocytes and macrophages to identify candidate causal genes. In the first step of the study, myeloid active enhancers that contain AD risk alleles (AD risk enhancers) were mapped to their target genes by integrating promoter-capture Hi–C and eQTL datasets from monocytes and macrophages. This led to the identification of candidate causal genes in eleven genome wide significant and five suggestive AD risk loci, which includes TP53INP1, APBB3, RABEP1, and SPPL2A. In the next stage of analysis, to investigate the causal relationship between chromatin activity, target gene expression, and AD risk modification they conducted SMR analysis. Specific active chromatin regions, that likely modify AD risk by regulating the expression of one or more of their target genes, were identified in 12 loci. They further fine-mapped AD risk enhancers which lead to the identification of candidate functional variants that likely affect transcription factor binding and regulate gene expression in seven loci. In the final step, they validated one of these variants in the MS4A locus in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Results: The study found that the AD risk alleles are specifically enriched in active enhancers of monocytes, macrophages, and microglia. Integration of AD GWAS signals with myeloid epigenomic annotations, chromatin interactions (promoter-capture Hi–C), and eQTL datasets identified candidate causal genes in sixteen AD risk loci. Further integration of AD GWAS signals with myeloid epigenomic annotations, chromatin activity (hQTL) and eQTL datasets identified candidate causal genes in twelve AD risk loci. Fine-mapping using myeloid epigenomic annotations identified candidate causal variants in seven AD risk loci. The study further found that a candidate causal variant in the MS4A locus disrupts an anchor CTCF binding site and that this variant is associated with reduced chromatin accessibility and increased MS4A6A gene expression in myeloid cells. This variant was validated in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Conclusion: This study uncovers a link between chromatin activity, gene expression and AD risk in myeloid cells. This suggests a molecular mechanism of action of candidate functional variants in several AD risk loci. The study also identifies specific AD risk enhancers that harbor these variants and regulate target gene expression. These identified AD risk enhancers most likely modulate disease susceptibility by altering the biology of myeloid cells. Impact of research: This study integrated AD GWAS with multiple myeloid genomic datasets to explore the mechanisms of AD risk alleles and suggested candidate functional variants, regulatory elements and genes that likely modulate AD disease susceptibility. This will help in understanding the molecular mechanism behind the AD pathogenesis.