Advances in Genome-Wide Association Studies (GWAS)

Genome-wide association studies (GWAS) have transformed our understanding of the genetic underpinnings of complex human traits and diseases. By analyzing millions of single nucleotide polymorphisms (SNPs) across the entire genome in large groups of people, GWAS have identified thousands of genetic variations linked to a vast array of characteristics, ranging from diseases like cancer and Alzheimer's to behavioral traits like height and intelligence.

The field of GWAS is constantly evolving, with new methods and technologies emerging to address limitations of earlier studies. This blog post will explore some of the key advancements driving GWAS forward.

Larger Studies and Polygenic Scores:

A significant leap in GWAS has been the ability to analyze increasingly larger datasets. Early GWAS were often restricted by sample sizes of just a few thousand individuals. Thanks to large-scale biobanking initiatives and international collaborations, studies now routinely analyze hundreds of thousands, or even millions, of participants. This statistical power allows for the detection of smaller genetic effects and the development of polygenic scores. Polygenic scores are combined risk scores that integrate the effects of multiple SNPs associated with a particular trait. Polygenic scores hold promise for improved disease risk prediction and patient stratification in clinical settings.

The two graphics illustrate sampling distributions of polygenic scores and the predictive ability of stratified sampling on polygenic risk score with increasing age. + The left panel shows how risk—(the standardized PRS on the x-axis)—can separate 'cases' (i.e., individuals with a certain disease, (red)) from the 'controls' (individuals without the disease, (blue)). The y-axis (vertical axis) indicates how many in each group are assigned a certain score. + At the right panel, the same population is divided into three groups according to their predicted risk, i.e., their assigned score, as high (red), middle (gray), or low (blue). The y-axis shows the observed risk amounts, where the x-axis shows the groups separating in risk as they age—corresponding with the predicted risk scores.

Beyond SNPs: Exploring Diverse Genetic Variation:

Traditionally, GWAS have focused primarily on SNPs, the most common form of genetic variation. However, recent studies are incorporating other forms of genetic variation, such as copy number variations (CNVs) and structural variants, into the analysis. CNVs involve deletions or duplications of larger DNA segments and can have a more substantial impact on gene function. Including these variations offers a more comprehensive picture of the genetic landscape influencing complex traits.

Genome-wide association study (GWAS) and Fst and nucleotide diversity filtration identified fblx19 and pkca genes that are related to the disease resistance on Chr 17. (A) Regional Manhattan plot for Chr 17. The red line indicates the significance thresholds (−log 10 p = 6). (B) The genomic positions of the GWAS-SNPs, fblx19 and pkca gene. Dark blue, light blue, yellow and green bar represents exon, intron, candidate regions, and the disease resistance quantitative trait locus (QTL) previously identified (Dai et al., 2017), respectively. (c, D) Relative messenger RNA (mRNA) expression of fblx19 and pkca gene in the V. harveyisusceptible (VS) and V. harveyi-resistant (VR) families, detected by quantitative real-time PCR (qPCR) with β-actin gene as the internal control. The average values of three samples in each group were used to represent the expression level. Asterisks indicate significance difference (p < 0.05).

Leveraging Functional Genomics and Epigenetics:

A major challenge in GWAS has been pinpointing the causal genes from the identified risk regions. Many associated SNPs reside in non-coding regions, making it unclear how they influence gene expression or disease development. Advances in functional genomics techniques like chromatin conformation capture (Hi-C) are helping to identify regulatory elements and genes targeted by the associated variants. Additionally, integrating epigenetic data on DNA methylation patterns can provide insights into how environmental exposures might interact with genetic variants to influence disease risk. Researchers rely on high-quality reagents to ensure the accuracy and reproducibility of these experiments. Companies like Gentaur Group are among those offering a wide range of reliable solutions for functional genomics and epigenetics studies.

Flow of a typical process from initial GWAS to functional dissection. a A typical GWAS involves selection of the study populations, either case-control cohorts or general populations; genotyping of variants across the genome by single-nucleotide polymorphism (SNP) array or whole genome sequencing; and statistical analysis of variant-trait/disease associations. Regional Manhattan plots (also termed as LocusZoom plots) are generated to show the P values of all variants in a genomic region, to explore the patterns of linkage disequilibrium (LD) between the sentinel variant and each variant, and to annotate the genes within this region. b Statistical fine-mapping and genomic annotations are used to prioritize candidate causal variants. Normally, a credible set of causal variants are prioritized according to posterior inclusion probability (PIP) of each variant and genomic annotations, including chromatin accessibility, histone markers, and transcription factor binding potential, are summarized to guide the following functional studies. c Target genes are predicted according to enhancer-target gene promoter interaction (chromatin confirmation capture) and correlation between causal variant genotypes and target gene expression. ASE, allele-specific expression. d Various experimental approaches are employed to investigate the functions of causal variants and target genes and to link them back to the original phenotype

Trans-ethnic and Ancestry-Specific GWAS:

Historically, GWAS have been primarily conducted in populations of European descent. This has resulted in a bias towards identifying genetic variants relevant to these populations, potentially missing variants important in other ancestries. There is a growing effort towards trans-ethnic and ancestry-specific GWAS to improve the generalizability of findings and ensure all populations benefit from these discoveries.

Trans-ancestry GWAS meta-analysis identifies 183 loci associated with serum urate Outer ring: Dot size represents the genetic effect size of the index SNP at each labeled locus on serum urate. Blue band: −log10(two-sided meta-analysis P) for association with serum urate (n = 457,690) by chromosomal position (GRCh37 (hg19) reference build). The red line indicates genome-wide significance (P = 5 × 10⁻⁸). The blue gene labels indicate novel loci; the gray labels indicate loci reported in previous GWAS of serum urate. Green band: −log10(two-sided meta-analysis P) for association with gout (n = 763,813) by chromosomal position. The red line indicates genome-wide significance (P = 5 × 10⁻⁸). Association P values are truncated at 10–30. Inner band: the dots represent the index SNPs with significant heterogeneity and are color-coded according to their source: green for ancestry-related heterogeneity (Panc-het < 2.7 × 10⁻⁴ (0.05/183)); red for residual heterogeneity (Pres-het < 2.7 × 10⁻⁴); and yellow for both (Panc-het and Pres-het < 2.7 × 10⁻⁴). Loci are labeled with the gene closest to the index SNP. Panc-het and Pres-het were generated using MR-MEGA

Statistical Methods and Machine Learning:

The development of novel statistical methods and the incorporation of machine learning algorithms are further refining GWAS analyses. These advancements allow researchers to account for population structure, identify rare variants with larger effects, and perform more robust fine-mapping to pinpoint causal variants within associated loci.


GWAS have become a powerful tool for dissecting the genetic basis of complex traits and diseases. The continuous advancements in sample sizes, variant analysis, functional genomics integration, and statistical approaches promise to unlock even deeper insights into human health and disease in the years to come.

Genome Wide Association Studies (GWAS) Explained in 7 Minutes in the following video:

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Advances in Genome-Wide Association Studies (GWAS)
Gen store June 10, 2024
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