Variant annotation methods that incorporate epigenetic data have become a standard approach to elucidating the functional consequences of genetic variants. Assessment of DNA accessibly can be caried out in multiple cell types to reveal underlying gene regulatory roles of chromatin organization in a given cell type, which in turn suggests how such activity confers its trait effects. Integrating various publicly available resources can further aid the prioritization of GWAS-identified variants. Assay for transposase-accessible chromatin sequencing (ATAC-seq) (Buenrostro et al., 2015) has proven to be an efficacious assay in assessing GWAS variants and functionality for complex diseases. A study focusing on type 2 diabetes (T2D) determined that the open chromatin landscape in human pancreatic islets cells differed between samples obtained from individuals with and without T2D (46). A total of 13 T2D associated SNPs were found in regions marked within open chromatin accessibility sites which were located near genes such as TCF7L2, ADCY5, and GCH (Bysani et al., 2019). In addition to the 13 SNPs, there were 67 SNPs that were in LD with T2D associated SNPs and further annotated to known T2D genes (such as PPARG, FTO, and KCNJ11) (Bysani et al., 2019). Another study focused on T2D used a combination of pancreatic cell expression data, chromatin accessibility, and network analysis methods to prioritize the gene RFX6 from GWAS results for the disease (Walker et al., 2023). This study took a different approach for incorporating ATAC-seq by using this technique to assess the chromatin architecture following the knockdown of the putative T2D causal gene (Walker et al., 2023). Knockdown of RFX6 in beta-pancreatic cells not only resulted in substantial variation in gene expression, but also provided evidence of dysregulation of regulatory elements harboring T2D GWAS variants via changes in genome-wide chromatin states (Walker et al., 2023).

As such, these annotations help determine a variant’s context with respect to a typically non-coding role as a potential regulatory element. Identifying whether a genetic variant resides in a regulatory element (such as promoter, enhancer, or transcription binding site) can be highly informative when determining subsequent biological consequences with respect to gene expression. Furthermore, assessing epigenetic markers in a cell type-dependent manner can help determine gene regulation specificity, adding important context to cell and tissue involvement for a given complex trait or disease. A popular resource for assessing variant annotations includes the Encyclopedia of DNA Elements (ENCODE) Consortium, which consists of histone modification, expression, and chromatin conformation data across different cell types (Luo et al., 2020).

Histone modifications are an essential aspect of gene regulation by influencing how tightly or loosely DNA is packaged, which indicates areas of the genome that are accessible and available for gene transcription. Acetylation and methylation patterns are histone modifications often leveraged to assess poised or inactive cell-specific chromatin states (Karlić et al., 2010). Histone marks used to determine overall DNA accessibility help determine whether a specific region is accessible to transcriptions factor (TF) binding, and therefore a potentially functional element. Regions with histone marks like H3K4me3 and H3K27ac are typically defined as promoter/open chromatin regions (Barski et al., 2007; Creyghton et al., 2010), while marks like H3K27me3 indicate closed chromatin states (Cai et al., 2021). Such histone modification patterns can be assessed by a number of available techniques, which are explored in greater detail elsewhere (Mansisidor and Risca, 2022). Examples of some techniques used to assess chromatin accessibility include not only ATAC-seq (discussed in previous paragraph), but also formaldehyde-Assisted Isolation of Regulatory Elements sequencing (FAIRE-seq) (Giresi et al., 2007), DNase I hypersensitive sites sequencing (DNase-seq) (Song and Crawford, 2010) and sequencing of micrococcal nuclease sensitive sites (MNase-seq) (Deng et al., 2022; Wong et al., 2023). Although many options are available, subtle nuances between the available techniques make some techniques more appealing than others depending on the specific application. Regarding experimental time and output success among the available options, ATAC-seq has become a gold standard, with an approximate three-hour protocol for preparation (Buenrostro et al., 2015; Grandi et al., 2022), where transposases are used with DNA-associated adaptors for subsequent high-throughput sequencing (Buenrostro et al., 2015).

Another significant component of the genomic landscape to consider is chromatin 3D interactions, which offer insight into physical connections between GWAS-implicated candidate causal variants and putative effector genes. Understanding which gene(s) are regulated by non-coding GWAS associated variants is essential to understanding complex traits fully. This is where the power of incorporating chromosomal capture techniques become apparent.

Chromosomal capture techniques involve crosslinking interacting genomic loci to each other, followed by high throughput sequencing (Dixon et al., 2012). The sequencing results generate a map of interacting segments from the genome, which can help nominate candidate effector genes controlled by regulatory elements harboring GWAS associated variants. Various techniques have been developed that can be used for such a purpose, and indeed TADs can be defined with chromosomal capture techniques (Zufferey et al., 2022).

Inclusion of chromosomal conformation capture techniques to determine gene regulation by GWAS loci has been successful in a multitude of studies involving complex diseases and traits. One study identified 38 candidate genes potentially involved in obesity by incorporating promoter Capture-Hi-C techniques within primary adipocytes (Pan et al., 2018). Following promoter Capture-HiC, the study implicated interaction with GWAS SNPs and three additional genes (rs8076131 to ORMDL3, rs1017546 and rs3784671 with LACTB, and rs10774569 with ACADS) (Pan et al., 2018). Use of related techniques led to the discovery of inflammatory bowel syndrome (IBD) target genes that were regulated by 92 regulatory elements previously associated with IBD (Meddens et al., 2016). Our study on systemic lupus erythematosus (SLE) evaluated putative target genes of GWAS signals by combining follicular helper T cells (TFH) (cells with SLE involvement), open chromatin sites, and three-dimensional genomic architecture as defined by Capture C (63). Incorporation of Capture C implicated genes BCL6 and CXCR5, which were previously identified as TFH regulators (Su et al., 2020). The study further assessed the putative target genes via CRISPR/CAS9 genome editing techniques, which revealed key genes important in regulating crucial cytokine involvment in B cell antibody production (Su et al., 2020). Such examples show the power of integrating chromosomal conformation techniques to determine targeted genes by non-coding variants and can lead to a more comprehensive understanding of the genomics driving specific complex traits or diseases.

Studies involving chromatin 3D interactions and genome organization have found regions with increased frequency of contact referred to as topologically associated domains (TADs) (McArthur and Capra, 2021). TADs provide a spatial framework for the genome while facilitating proper gene regulation (McArthur and Capra, 2021), and has been used by investigators to define the search space for defining an underlying effector gene at a given GWAS locus. Variants within noncoding regulatory elements, such as enhancers, localize within three dimensional TADs that are readily assessed through various chromosomal capture methods that asses chromatin 3D interactions (Dixon et al., 2012; McArthur and Capra, 2021). An overview of popular and useful resources for chromatin accessibility data integration can be found in Table 1.

Chromosomal capture techniques including Capture-C (65), ChIA-Pet (Han et al., 2018), Hi-C (59,65), chromosome conformation capture carbon copy (5C) (Dixon et al., 2012; Han et al., 2018; Dostie et al., 2006), chromosome conformation capture-on-chip (4C) (Dixon et al., 2012; Han et al., 2018; Simonis et al., 2006), and other chromosome conformation capture (3C) (Han et al., 2018) techniques are all methods that are available for genomic interaction assessment purposes (Luo et al., 2020; Karlić et al., 2010). Although there are some differences among chromosomal capture technique methods, each method includes four main steps (crosslinking, fragmenting, ligating, and sequencing steps). Each approach requires an initial step for crosslinking chromatin. The genome is subsequently fragmented with endonucleases and then ligated. The ligation step is used to join the interacting genetic loci to each other, which is reverse crosslinked in preparation for sequencing or quantitative polymerase chain reaction (PCR) to identify genomic interactions (Dostie et al., 2006; Simonis et al., 2006; Naumova et al., 2012; Lieberman-Aiden et al., 2009; Fullwood et al., 2009; Li et al., 2010; Zhao et al., 2019a; Fullwood and Ruan, 2009).

QTL analysis is another method that can aid in prioritizing variants that are involved in a disease or trait. A QTL can be used to focus on a genomic region associated with a phenotype or trait, which is beneficial when attempting to implicate a gene for GWAS V2F follow-up (Powder, 2020). QTLs will typically incorporate marker and quantitative data to connect observed trait variation to genetic variations within a given population (Powder, 2020). Various types of QTL analysis exist and rely on various combinations of genotype, RNA-seq, chromatin accessibility, and/or methylation data (Powder, 2020). QTL-based data is based on observable, quantitative traits motivated by answering the question of how genetic variation impacts said trait. QTLs can further be used to assess the influence of genetic variation diseases and response to various treatments. Some of the more commonly used QTL analyses include expression quantitative trait loci (eQTLs) (Porcu et al., 2019), protein quantitative trait loci (pQTLs) (Xu et al., 2023), and chromatin accessibility quantitative trait loci (caQTLs) (Khetan et al., 2021; Kumasaka et al., 2019).

The more well-known type of QTL is the eQTL, which is used to determine how a specific genome region impacts gene expression variation. In order to conduct an eQTL study, both genotype and RNA expression data are required. Combining such data for eQTLs can aid in the GWAS V2F follow-up process to determine which genes are influenced by genetic differences in complex diseases or traits. Studies that have overlapped eQTL and GWAS data have been able to address the link between GWAS signals and potential target genes through ‘co-localization’. One example was seen in the context of an AD study, where combined eQTL and mQTL data was used to prioritize SNPs and then to connect them to putative target effector genes (Zhao et al., 2019b). Findings from the study indicated an association between 653 SNPs and 25 genes, with 93 of the SNPs being significant for both eQTL and mQTL data (Zhao et al., 2019b). Furthermore, 10 out of the 25 genes found were previously identified in the literature as already being involved in the genetic etiology of AD (79). Another study used eQTL to assess immune related diseases and nominate 127 candidate disease genes following colocalization of eQTL and GWAS associated SNPs across 11 different diseases (Soskic et al., 2022). The eQTL data was generated from 119 human derived isolated and activated naive and memory CD4⁺ T-cell. Each cell was profiled at resting, 16 h, 40 h, and 5 days (Soskic et al., 2022). The eQTL and GWAS SNP results implicated genes in a broad range of immune related diseases, which included Crohn’s disease, multiple sclerosis and SLE (80). More details on various types of QTL/GWAS colocalization and methods can be found elsewhere (Hormozdiari et al., 2014; Kang et al., 2023; Zuber et al., 2022; Cano-Gamez and Trynka, 2020; Suhre et al., 2021; Zhang et al., 2024; Abood and Farber, 2021; Fabo and Khavari, 2023).

Similar to eQTLs, pQTL and caQTL have major applications. Although these approaches serve slightly different purposes, they incorporate genotype data with protein levels for pQTLs, and chromatin accessibility for caQTLs. A previous study focusing on serum protein successfully overlapped pQTLs with lead GWAS variants for multiple different phenotypes (Gudjonsson et al., 2022). In the study, two body mass index (BMI) associated loci were faound to overlap with protein serum levels of Agouti signaling protein (ASIP) (Gudjonsson et al., 2022). The study also found waist-to-hip ratio GWAS signals within the LRRC36 gene overlapping with levels of Agouti-related protein (Gudjonsson et al., 2022). One caQTL study had success when assessing GWAS signals for T2D (77). Chromatin accessibility sites were assessed in pancreatic islet cells, resulting in the nomination of causal variants at 13 GWAS loci (Khetan et al., 2021). These candidate causal loci were then functionally assessed in vitro via luciferase assay in MIN6 cells (Khetan et al., 2021). Out of the 13 loci, more than half were identified as having differential allelic regulatory roles (Khetan et al., 2021).

Additional QTL approaches that can be applied to GWAS follow-up are outlined in Table 2.

Similar to QTL, Transcriptome-Wide Association Study (TWAS) (and the newer casual TWAS that allows for confounding adjustments within the model (Zhao et al., 2024)) can aid in gene prioritization for GWAS follow-up. TWAS leverages genomic and transcriptomic data to discover how genetic differences might impact gene expression across different tissues. This approach can further explain how genetic variants found through GWAS not only affect gene expression, but also influence disease risk. A study conducted by Gusev et al. applied TWAS by using expression data across multiple tissues in conjunction with GWAS summary stats focused on traits such as height, body mass index (BMI), and lipids (Gusev et al., 2016). By leveraging this type of data, the study revealed 69 novel gene-trait associations (Gusev et al., 2016).

While many TWAS methods tend to be univariate (such as PMR-Egger (Yuan et al., 2020), PrediXcan (Gamazon et al., 2015), and FUSION(90)), more recent TWAS methods have tried to expand this type of model with the rationale of potential pleiotropic effects (Liu et al., 2021; Feng et al., 2021). One such study that developed a TWAS method referred to as moPMR-Egger which accommodates the analysis of multiple traits at a time (as opposed to one trait), lead to 13.5% increased gene associations findings when applied to United Kingdom biobank traits (Liu et al., 2021). While has different models available, it can still be a great genomic-based tool that offers a potential resolution to determining how GWAS identified variants might function within a biological setting by suggesting the genetic target(s) of GWAS varaints. More in depth information of the various TWAS approaches along with valuable resources can be found elsewhere (Feng et al., 2021; Li and Ritchie, 2021; Zhu and Zhou, 2021; Mashhour et al., 2024; Xie et al., 2021).

While outside the scope of this review, it is worth mentioning the use of Phenome-wide association studies (PheWAS) for its ability to also connect gene-trait associations. A general overview on PheWAS and some resources can be found elsewhere (Liu and Crawford, 2022; Bastarache et al., 2022).

Successful incorporation of pathway and network-based analyses can inform V2F by implicating potential hubs influencing a phenotype of interest. One AD study identified 32 additional candidate genes by including a network analysis in their study (Schwartzentruber et al., 2021). They first generated an overall gene interaction list based on information retrieved from the STRING, IntAct, and BioGRID databases (Schwartzentruber et al., 2021). Through propagation methods utilizing genes found in the initial retrieval process, the study was able to highlight previously established genes associated with AD (Schwartzentruber et al., 2021). Interestingly, the study included high ranked genes for AD GWAS loci with the lowest P-values, indicating larger GWAS may be required to fully evaluate all putative loci (Schwartzentruber et al., 2021). Another recent study focused on kidney renal clear cell carcinoma (KIRC) utilized a Protein-Protein Interaction (PPI) network method and the authors elected to further evaluate the top four genes (referred to as “hub” genes) in the associated list (Ali et al., 2023). Through further assessment of their implicated hub genes, the study reported that two out of the four genes were upregulated while the other two were downregulated in KIRC patients (Ali et al., 2023).

Although there is debate regarding the utility and reproducibility of pathway and network based analyses (Tomczak et al., 2018), a wide-range of studies have included such results as a means to further understand potential underlying gene interactions on a broader scale (Yu et al., 2019; Jin et al., 2022). Pathway analysis can be accomplished through either a non-topology or a topology-driven method. A non-topology-driven method is traditionally known as an overrepresentation analysis (ORA) or functional enrichment analysis. An enrichment analysis considers a list of significantly differentially expressed genes (DEG) from a larger given data set. The list is then used to determine the percent of DEG present within a pathway. When more than 10% of DEGs are present in a given pathway, that specific network is considered “enriched” and worth further investigation. ORA tools include FuncAssociate (Berriz et al., 2009), GeneMerge (Castillo-Davis and Hartl, 2003), EASE (Hosack et al., 2003), g:Profiler (Kolberg et al., 2023), DAVID ((Huang et al., 2009a), (Huang et al., 2009b)), WebGestalt (Liao et al., 2019), AmiGO 2 (Carbon et al., 2009), GeneWeaver (Baker et al., 2012), BiNGO (Maere et al., 2005), GoMiner (Zeeberg et al., 2003), ontologizer (Bauer et al., 2008), etc. Functional class scoring (FCS) is another type of ORA similar to functional enrichment analysis but utilizes the entire gene set data instead of only the DEG. Common FCS approaches include GSEA (Subramanian et al., 2005; Mootha et al., 2003), GSA (Mooney and Wilmot, 2015), GlobalTest (Hulsegge et al., 2009), PADOG (Tarca et al., 2012), SAM-GS (Dinu et al., 2007), FunCluster (Henegar et al., 2006), etc.

Gene Ontology (GO) analysis is another ORA-based enrichment method that allows users to assign functional annotations based on various categories. GO analysis can help assign genes or gene products based on molecular, biological, and cellular functions (Zhao et al., 2020). GO analysis is an example of a pathway analysis, which identifies biological pathways involved in a phenotype by using gene expression data and available pathway databases. This approach is often used to help elucidate how genes and variants impact specific cellular pathways. Pathway-based databases can help discern involved gene and protein networks, which helps further understand the underlying interactions within a given complex trait or disease. There are multiple online resources available to incorporate GO analysis. Most notable databases include Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000), PANTHER (Thomas et al., 2022), Reactome (Gillespie et al., 2022), Pathway Commons (Rodchenkov et al., 2020), Wiki-Pathways (Martens et al., 2021), and PathBank (Wishart et al., 2020).

Although non-topology ORA analyses are helpful and can yield biological insights, such analyses do not take into account interactions of genes, which often play a role in contributing to complex disease and traits. For this reason, topology-based approaches can be a better alternative to non-topology ORAs. Pathway topology (PT) considers the independent gene role, position, magnitude, and interactions. Fold change of a gene is propagated onto the gene directly downstream in a pathway. iPathwayGuide (Ahsan and Drăghici, 2017) provides a web-based system to conduct a PT approach to gene expression analysis tools to better rank pathways involved in a given phenotype. More information on pathway analysis and additional resources can be found elsewhere (Maleki et al., 2020).

Pathway analyses provide information regarding gene and gene products within a set pathway impacting a phenotype of interest, but not necessarily how proteins across different pathways potentially interact. Such information can yield additional insight into the underlying biological network leading to a complex disease or trait. For this purpose, PPI networks can help highlight how proteins within various pathways intersect and interact at a given point to serve an overall biological process. A valuable tool for a PPI network includes the STRING (Szklarczyk et al., 2021) database, which can also be used for enrichment and PPI network analysis.

Although it aids understanding of the underlying biological network, the main goal is for functional and mechanistic significance. Insight from additional approaches like phenotype annotation and comparative genomics can further aid in understanding how genetic variants lead to a specific functional outcome (i.e., disease, drug response, biological process, etc.). By incorporating tools like Ensembl (Martin et al., 2023), linking genetic changes to phenotype can help determine functional consequences. Furthermore, using evolutionally conservation data can also help understand functional significance through comparative genomics. Comparing genomic regions across species for GWAS signal interpretation may reveal a conserved gene regulatory site. Using such knowledge can further aid in understanding whether a specific locus is in a region potentially involved in regulating an essential gene in a given biological pathway. Available tools for a comparative genomic approach include VISTA (Frazer et al., 2004; Dubchak et al., 2000), CoGE (Lyons and Freeling, 2008), PipMakers (Schwartz et al., 2000), etc.

Regulatory abilities of putative non-coding regulatory elements are traditionally assessed through individual reporter-based assays. Although reporter assays are not used to explicitly nominate genes being regulated by a putative enhancer region, they can represent an initial step in determining which nominated variants drive expression changes. The luciferase reporter assay system is an example of an individual reporter-based assay that is very useful when investigating the regulation on gene expression. Incorporating reporter assays has shown success in assessing variant regulatory impact of across different complex disease and traits (Zhang et al., 2010; Ustiugova et al., 2019; Rivas et al., 2011; Ramachandran et al., 2022). While investigating opioid addiction and the role of rs569356 in the gene OPRD1 gene promoter, luciferase reporter plasmids were constructed with the major (A) and minor (G) alleles (Zhang et al., 2010). These constructs were transfected into HEK293 cells, and results indicated the G allele led to an increased expression in the reporter assay (Zhang et al., 2010). The differential allelic response was used to suggest a potential mechanism in regulation of OPRD1 leading to opioid addition (Zhang et al., 2010). Another study focused on GWAS associated loci in autoimmune diseases, incorporating luciferase reporter assay into their study design to determine allele specific functionality (Ustiugova et al., 2019). Both risk and protective alleles from six associated loci (rs12946510, rs2313430, rs4795397, rs12709365, rs13380815, rs8067378) were included in the reporter assay and assessed in six different cell lines (Nalm6, MP1, Jurkat, MT-2, U-937, and activated U-937) (Ustiugova et al., 2019). The results indicated cell specific differential allelic activity for at least four of the six loci, with the strongest effect seen for rs12946510 across three cell types (Nalm6, MP1, and activated U-937) (Ustiugova et al., 2019).

Although simplistic in design, luciferase assays are still useful in determining whether a specific genomic region harboring GWAS loci potentially impacts gene expression. Luciferase assays use plasmids containing a luciferase reporter gene located downstream of a regulatory element of interest and a minimal promoter (Fan and Wood, 2007). The final reporter construct is transfected into a specific cell type (animal, plant, or bacteria). Since the reporter gene is essentially fused to the regulatory element, detecting transcription changes in the reporter gene is directly correlated to the relative regulatory activity of the regulatory element (cis-acting) (Fan and Wood, 2007). The luciferase gene encodes for a specific enzyme that produces fluorescence, which is quantified by measuring the light intensity (Fan and Wood, 2007).

Although individual reporter assays help determine functional non-coding variants nominated from GWAS, they are very time-consuming. The time constraints associated with individual reporter assays require stringent prioritization of nominated GWAS variants due to their inability to incorporate multiple variants in a single experiment. Current genomic technologies have led to an expanded and comprehensive form of individual reporter-based assays, referred to as high-throughput reporter assays. Such high-throughput reporter assays allows for the evaluation of thousands of putative non-coding regulatory variants simultaneously (Melnikov et al., 2012; Kheradpour et al., 2013; Kwasnieski et al., 2012; White et al., 2013; Inoue and Ahituv, 2015; Arnold et al., 2013). High-throughput reporter assays result in a more streamlined and comprehensive follow-up approach for GWAS-nominated variants.

Most high-throughput reporter assays, like Massively Parallel Reporter Assay (MPRA) and Self-Transcribing Active Regulatory Region Sequencing (STARR-seq) (both discussed below), use a similar plasmid-based concept as utilized in individual reporter assays when determining the relative regulatory ability of specific non-coding regions (Gallego Romero and Lea, 2023; Das et al., 2023). The main difference between individual reporter assays and high-throughput assays is how the expression is quantified. In individual reporter assays, the relative enhancer activity is qualitatively determined using light emission created by substrate-enzymatic reactions (Fan and Wood, 2007; Melnikov et al., 2012; Kheradpour et al., 2013; Tewhey et al., 2016). High-throughput reporter-based assays are far more quantitative by incorporating high throughput sequencing to count generated sequences caused by a specific regulatory region (Gallego Romero and Lea, 2023; Das et al., 2023).

MPRA is a highly reproducible and sensitive high-throughput reporter assay that simultaneously evaluates thousands of putative regulatory sequences while determining allele-specific activity (Melnikov et al., 2012; Kheradpour et al., 2013; Ulirsch et al., 2016; Lu et al., 2021). Inclusion of the MPRA design has been utilized in a wide range of contexts from answering evolutionary questions (Du et al., 2022) to determining previously unexplored allele specific activity of complex diseases/traits. In fact, multiple studies have shown great success in identifying functional GWAS loci that were previously unexplored through incorporation of MPRA (Matoba et al., 2020; Long et al., 2022; Mouri et al., 2022). One study, in particular, focused on autoimmune diseases where T cell involvement was known. Integrating variants associated with inflammatory bowel syndrome (IBD), multiple sclerosis (MS), type 1 diabetes (T1D), psoriasis and rheumatoid arthritis (RA) were included in the MPRA design and subsequently assessed for their functionality within T-cells (Mouri et al., 2022). With over 18,000 loci assessed, the study found 313 variants with differential expression when comparing the reference and alternative alleles (Mouri et al., 2022). Other studies have achieved success by leveraging data from MPRA and eQTLs combined, allowing further biological perturbations from previously overlooked loci (Choi et al., 2020). This study included over 800 loci associated with melanoma, and assessed their regulatory influence within a melanoma cell line (Choi et al., 2020). Overlapping the MPRA data with local eQTL data, the study could prioritize nine variants for which differential gene expression data appeared to corroborate potential variant endogenous gene regulation (Choi et al., 2020).

Incorporating MPRA to evaluate GWAS-associated variants include constructs with sentinel single nucleotide polymorphisms (SNPs) and variants in LD. Including variants in LD aids in determining whether the SNP directly genotyped for the GWAS is influencing the biological system or the SNP in high LD with the SNP directly genotyped. Once a list of noncoding variants is determined, various manufacturers (such as Agilent Technologies, Dynegene, and Twist Bioscience (Agilent, 2024; Dynegene, 2024; Bioscience, 2024)) can create large-scale oligonucleotide (oligo) library pools. The entire MPRA oligo library is generated or synthesized by microarray technologies and is currently limited to 230 base pairs (bp) (Melnikov et al., 2014). Although traditional MPRAs are highly reproducible and sensitive enough to determine allelic variation, the MPRA reporter plasmid design cannot guarantee biological relevance. The construct may not represent an actual regulatory element since MPRA oligos are limited in sequence length. Regulatory elements often span much larger regions than the length-restricted regions found in MPRA oligos, which may lead to type I and II errors (Blackwood and Kadonaga, 1998). Since regulatory regions can span large regions, it is essential to validate MPRA findings to include genomic regions that span larger bp regions centered on the putative variant that can be transformed into individual reporter plasmids (Blackwood and Kadonaga, 1998). The ability to further evaluate larger regions of any high-confidence regulatory element found in MPRA assays with individual reporter assays is one way to mitigate the negative consequences of the limited sequence lengths in MPRA oligo library pools. Another alternative approach to the limited length of current MPRA designs is the Tiling MPRA method (Ernst et al., 2016). Tiling MPRA allows one to extend the length of testable regulatory regions by using multiple 175bp constructs for one variant loci represented at the center of the construct and varying bp lengths to the left and right from the center of the constructs (Ernst et al., 2016).

In addition to size restrictions, MPRA plasmid pool designs do not consider the endogenous biological context. Although using the same minimal promoter and reporter gene for the entire MPRA library pool is beneficial for direct comparison across individual regulatory elements within the MPRA pool, it lacks biological context and relevance by excluding endogenous promoters and genes. MPRA experiments cannot measure the putative regulatory element’s activity in its endogenous chromatin environment. Additionally, GWAS can nominate noncoding variants that may be involved in gene regulation but do not indicate putative tissue or cell types involved. Lack of cell-specific involvement in a disease or trait becomes another limitation for MPRAs when incorporating library pools for a research design. MPRA libraries are created based on all top GWAS hits, which means false positives and negatives can occur when utilizing this technique within cell types that are not actively involved in the complex trait or disease of interest. Including chromatin accessibility data for specific cell types is one way to avoid such errors when determining which cell type best suits the MPRA design. Alternatively, the high-throughput reporter assay called STARR-seq can supplement the MPRA design to analyze active enhancer regions within a given temporal context (Arnold et al., 2013).

Similar to MPRAs, STARR-seq can simultaneously determine the regulatory activities of thousands of regulatory elements. Unlike MRPA, STAR-seq method can determine active regulatory regions of the genome in a state/temporal dependent manner while directly assessing cell-specific regulatory activity (Arnold et al., 2013). Studies incorporating STAR-seq design have had success in determining regulatory elements for cellular response to various pharmacological drugs, such as dexamethasone and other glucocorticoids (GC) (Johnson et al., 2018; Penner-Goeke et al., 2023). Such studies placed cells under the influence of steroid-based drugs to induce a GC- response. Regulatory regions that were responsive to the pharmacological insult would reside in open chromatin, allowing for fragmentation and placement into the reporter plasmid for incorporation into the STARR-seq design to detect novel GC-responsive regulatory loci within the genome (Johnson et al., 2018; Penner-Goeke et al., 2023). Incorporating GC-responsive elements into the STARR-seq technique has previously allowed researchers to identify an overlap between functionally validated drug responsive elements that were enriched with identified GWAS variants associated with psychiatric traits (Penner-Goeke et al., 2023). Under the driving idea that a “stress” induced response would result in various psychiatric outcomes, the STARR-seq reporter plasmid was introduced into osteosarcoma and brain glioblastoma cell lines (Penner-Goeke et al., 2023). Furthermore, the identified GC-responsive elements were found to regulate transcripts enriched in genes shown to be differentially expressed in the cerebral cortex of psychiatric disorders, such as schizophrenia (SCZ), major depressive disorder (MDD) and autism spectrum disorder (ASD) (Penner-Goeke et al., 2023).

Importantly, STARR-seq can only include open chromatin accessibility sites in the reporter plasmid pool (Arnold et al., 2013). In some situations, the direct cell-specific assessment makes STARR-seq cost effective compared to MPRA as a plasmid pool is unnecessary for STARR-seq (Das et al., 2023). Instead, any cell type or tissue needed for a project can be used to extract cellular DNA and subsequently sheared to obtain smaller fragments of DNA (typically 300-500bp fragments) (Arnold et al., 2013). The fragmented DNA is transformed into the reporter plasmid. Open chromatin regions can be obtained by targeting regions by incorporating chromatin immunoprecipitation (CHIP) techniques to select for specific transcription factor binding sites or histone modifications associated with open chromatin sites and active enhancers (H3K27ac and H3K4me1) which can be advantageous when only wanting to assess GWAS associated SNP regions (Arnold et al., 2013; Heintzman et al., 2007). The STARR-seq reporter plasmid is then transfected into the cell type from which the regulatory elements were initially derived. Following transfection, the cells are lysed and submitted for high-throughput sequencing (Arnold et al., 2013).

Another difference seen between MPRA and STAR-seq methods is in the subsequent analysis. Variation in the analysis technique is due to the plasmid organization. MPRA plasmids place the regulatory regions upstream of a minimal promoter and reporter gene. The only way to determine regulatory activity in an MPRA experiment is to sequence the unique identifier downstream of the reporter gene. Unlike the MPRA plasmid organization, the STARR-seq method places the regulatory element downstream of the core promoter and reporter gene (Arnold et al., 2013). The basic concept driving the STARR-seq method assumes active enhancers can act independently of their position (Arnold et al., 2013). Placement of the regulatory elements in the STARR-seq plasmid will allow active enhancer regions to transcribe themselves, essentially being their own unique identifier. Analysis for STARR-seq experiments differs from MPRA analysis primarily due to the need for alignment, which is due to the incorporation of a fragmentation step in creating the STAR-seq reporter plasmid pool (Arnold et al., 2013).

Incorporating functional annotation and validation methods can collectively implicate a link between GWAS-nominated variants and target gene expression; however, these techniques still lack the ability to physically confirm whether GWAS loci are causal within an endogenous setting. Recent techniques like CRISPR become useful in fully elucidating how variants impact phenotypes within an endogenous setting. This powerful genome editing technique has shown great utility in a wide range of diseases (such as Parkinson’s (Soldner et al., 2016), schizophrenia (Forrest et al., 2017) and nasopharyngeal carcinoma (Wang et al., 2023)). Furthermore, CRIPSR has been used in pursuit of GWAS loci for complex traits such as bone mineral density (Pippin et al., 2021). Our study focused on bone mineral density connected GWAS associated variants to a target gene by incorporating promoter capture C (172). After obtaining a putative gene target (EPDR1), CRIPSR was used to knockdown the gene in a cell model equivalent to mesenchymal stem-cell derived osteoblasts and we showed the gene impacted osteoblast differentiation (Pippin et al., 2021).

CRISPR aids in such investigations by allowing direct quantification of gene expression based on endogenous single nucleotide changes (Wang et al., 2016; Irion et al., 2014). Editing an individual nucleotide in a regulatory sequence allows direct assessment of variant function on putative target gene expression. This was accomplished in a study interesting in bone mineral density and hyperglycemic phenotypes (Sinnott-Armstrong et al., 2021). Chromosomal capture techniques were used to assess ADCY5 as the potential target of the rs56371916 pleiotropic locus (Sinnott-Armstrong et al., 2021). CRIPSR was implemented to edit the locus in adipose-derived mesenchymal stem cells (AMSCs) to be either homozygous for CC or TT (Sinnott-Armstrong et al., 2021). Following osteoblast or adipocyte induction, ADCY5 expression was assessed (Sinnott-Armstrong et al., 2021). Findings indicated ADCY5 expression was increased in osteoblast induction for the TT homozygous genotype, while conversely ADYC5 expression was decreased in the adipocyte induction for the CC homozygous genotype (Sinnott-Armstrong et al., 2021). Overall, the change in ADCY5 expression confirmed a pleiotropic effect of rs5637916 seen through lipolysis regulation in adipocytes and lipid-oxidation differentiation of osteoblasts (Sinnott-Armstrong et al., 2021).

Using CRISPR to edit single nucleotide changes requires the Cas9 protein, gRNA, and a repair template strand (Wang et al., 2016; Irion et al., 2014). The repair template strand is used to repair the genomic break caused by the Cas9 protein through the homology-directed repair (HDR) and nonhomologous end joining (NHEJ) mechanisms (Li et al., 2023a). Following nucleotide editing validation, the impact of downstream gene expression can be determined with traditional qPCR techniques. Unfortunately, off-target effects while using CRISPR and designing functional guide RNAs (gRNAs) are a valid concern. Exploiting the high-fidelity use of HDR for single nucleotide editing has the unfortunate drawback of having low efficiency with previous rate estimates of approximately <5% (Wang et al., 2016; Irion et al., 2014; Komor et al., 2017). However, this rate has recently been drastically improved to >50% (Li et al., 2023a).

CRISPR screens can be implemented as a pooled (gRNAs in bulk cells) and arrayed (gRNAs intro CRISPR screens) for a large-scale GWAS follow-up (Bock et al., 2022) or at a single-cell resolution (Faial, 2023). While pooled screens are great for discovery-based research, array-based screens are beneficial for follow-up-based studies. In the context of GWAS signals, array-based CRISPR screens can make implementing single-base pair changes in the genome possible, and measurable phenotypes can be determined based on the perturbation. With the continued use of CRISPR screen applications for GWAS functional follow-up, focus on single-cell CRISPR-based (scCRISPR) applications has increased. Some notable methods that incorporate the scCRISPR approach include CRISP-seq (Jaitin et al., 2016), CROP-seq (Datlinger et al., 2017), Mosaic-seq (Xie et al., 2017), Perturb-seq (Adamson et al., 2016; Dixit et al., 2016), and transcript-informed single-cell CRISPR sequencing (TISCC-seq) (Kim et al., 2024a). Additional details on the progress of CRISPR-based screen approaches has been recently explored by Kim et al. (2024b), Cooper et al. (2024).

AI is a general term used to describe technology that utilizes computer algorithms like machine learning or deep learning (Sarker, 2022). The benefits of incorporating AI programs can be seen in their ability to handle large amounts of data, which can significantly decrease the overall time of determining putative gene targets of GWAS variants (or variants in LD with GWAS variants). AI tools have been trained on different datasets for the overall purpose of pattern recognition. Having the ability to assess patterns quickly, AI has become an efficient way to predict which variants might disrupt regulatory motifs and gene function, leading to a more streamline method to prioritize the most relevant variant and putative target gene for functional validation.

Researchers have already benefited from AI integration in tools that can help suggest the potential functional impact of GWAS variants such as the online tools GeneMANIA (Warde-Farley et al., 2010), STRING (Szklarczyk et al., 2021; Martin et al., 2023), PhenoScanner V2 (Kamat et al., 2019), and Variant Effect Predictor (VEP) (Martin et al., 2023; McLaren et al., 2016). Tools like these, capitalize on pattern recognition from intersecting AI and multi-omics datasets (such as transcriptomics, functional annotations, etc.). Other tools, like DeepSEA (Zhou and Troyanskaya, 2015) and ExCAPE (Sturm et al., 2020), use a convolutional neural network (Gu et al., 2018) to determine the potential impact of regulatory elements and gene expression and how this could translate to a pharmaceutical approach, respectively (Zhou and Troyanskaya, 2015; Sturm et al., 2020).

Genetic variants effect predicting tools have further been inhanced by the inclusion of deep learning tool. Some notable examples include ESM (Lin et al., 2023) (iterations include ESM-1, ESM-2, and recently ESM-3), AlphaMissense (Cheng et al., 2023; Tordai et al., 2024), AlphaFold2 (Yang et al., 2023b), and DeepVariant (Chen et al., 2023). Various iterations of ESM focus on model protein structures. ESM assesses evolutionary sequence data and genetic mutations into its model to suggest how variants affect protein folding, stability, and/or interactions (Lin et al., 2023; Callaway, 2024). ESM-3 has extended this protein-based application to predicting new proteins that could be created to enhance current GFP and CRISPR proteins to enhance current techniques used in functional assays (Callaway, 2024). Similarly, AlphaMissense AI tool predicts protein activity caused by variants that impact amino acid sequences by using deep learning and structural bioinformatics (Tordai et al., 2024). Apart from protein prediction effect, other advancements in AI have provided newer tools like DeepVariant that have improved accuracy of mapping variants to genes and/or regulatory elements they might affect (Chen et al., 2023).

Once a target gene or regulatory element is prioritized with the available AI algorithms, other forms of AI tools can further aid in developing the assays used for functional assays. Already mentioned was the latest ESM-3 tool that is actively attempting to create new protein structures based on known sequences that could provide higher efficient CRISPR proteins for genome editing (Callaway, 2024). ESM-3 is currently too new to fully know the degree of efficiency this may provide to current CRISPR techniques used for functional follow-up, but a important to mention in the context of how AI tools may further advance functional assessments in the future (Callaway, 2024). AI-powered design tools like CRISPR-DO provides predictive feedback on designing guide RNAs used for genome editing, which is useful for designing the most efficient gRNA for successful application of CRISPR technique (Ma et al., 2016). Additional AI powered tools and method have been explored elsewhere (Sigala et al., 2023; Nicholls et al., 2020).