Genome-wide association studies (GWAS) aim to identify the genetic variants associated with a dichotomous (e.g., type 2 diabetes case/control status) or quantitative (e.g., serum fasting glucose levels) traits. The study involves a high-density scan of the genome, genotyping single nucleotide polymorphisms (SNPs) and then using these genotyped SNPs, matched with an appropriate reference sequence panel to impute the remaining genetic variants relying on the known patterns of linkage disequilibrium (i.e., correlation between the genetic variants) in the reference panel. “Risk” genetic variants, either genotyped or imputed, are expected to occur more frequently in people with the disease of interest than in people without it, or with riskier levels of the quantitative trait of interest which may be higher or lower depending on the specific trait (e.g., high LDL and low HDL are associated with risk for heart disease). These associated variants identified through GWAS demarcate the genomic regions harboring susceptibility loci that are potentially involved in disease development and/or progression.
Given the patterns of linkage disequilibrium a GWAS loci often contains multiple associated variants highly correlated with one another, such that statistical fine-mapping methods are required to predict which of the associated variants is putatively causal. Thus, GWAS are often considered hypothesis-generating studies that require extensive functional analyses as follow-up to determine the true causal variant.
GWAS has multiple applications, including gaining insight into a phenotype’s underlying mechanism, estimating its heritability, identifying potential targets for drug development, making clinical risk predictions, and inferring the underlying genetic architecture between clinical risk factors and health outcomes. Specific to predicting risk, GWAS summary statistics can be used to combine association results across the genome into polygenic risk scores (PRSs) to calculate an individual’s genome-wide genetic risk for disease.
In recent years, GWAS have used thousands to hundreds of thousands of samples, including some studies that have used more than a million samples in order to have the statistical power required for the discovery of new genetic variants associated with complex traits. Many of these studies have shown that not only it is necessary to use a large number of samples in GWAS, but it is also necessary to use samples from multi-ancestral/ multi-ethnic backgrounds. non-Eurocentric samples globally would contribute to reducing biases in human genomics research which has been predominantly conducted, until a few years ago, European populations (80%), followed by East Asian populations. The inclusion of diverse populations has led to the identification of novel genetic variants associated with several complex traits of public health. Additionally, it has allowed the identification of PRS estimates that tend to be more transferable among ancestrally diverse populations.
Therefore, we encourage, and welcome research conducted on ancestrally and ethnically diverse populations across the world in this Research Topic.
Given the importance of GWAS in precision medicine and public health, this Research Topic will focus on recent advances related to empirical and methodological studies of GWAS. We welcome original research articles, systematic reviews, and meta-analyses within the scope of the Research Topic. We are specifically interested in human studies but will consider work related to outbred animal populations. The submissions can include but are not limited to the following subtopics:
• Use of GWAS to identify the mechanisms underlying complex diseases
• Estimation of heritability from GWAS data
• Advancement(s) and limitations of GWAS fine-mapping and identification of putative causal variants
• Use of GWAS data to identify potential drug/therapeutic targets
• Polygenic Risk Score (PRS) construction.
Genome-wide association studies (GWAS) aim to identify the genetic variants associated with a dichotomous (e.g., type 2 diabetes case/control status) or quantitative (e.g., serum fasting glucose levels) traits. The study involves a high-density scan of the genome, genotyping single nucleotide polymorphisms (SNPs) and then using these genotyped SNPs, matched with an appropriate reference sequence panel to impute the remaining genetic variants relying on the known patterns of linkage disequilibrium (i.e., correlation between the genetic variants) in the reference panel. “Risk” genetic variants, either genotyped or imputed, are expected to occur more frequently in people with the disease of interest than in people without it, or with riskier levels of the quantitative trait of interest which may be higher or lower depending on the specific trait (e.g., high LDL and low HDL are associated with risk for heart disease). These associated variants identified through GWAS demarcate the genomic regions harboring susceptibility loci that are potentially involved in disease development and/or progression.
Given the patterns of linkage disequilibrium a GWAS loci often contains multiple associated variants highly correlated with one another, such that statistical fine-mapping methods are required to predict which of the associated variants is putatively causal. Thus, GWAS are often considered hypothesis-generating studies that require extensive functional analyses as follow-up to determine the true causal variant.
GWAS has multiple applications, including gaining insight into a phenotype’s underlying mechanism, estimating its heritability, identifying potential targets for drug development, making clinical risk predictions, and inferring the underlying genetic architecture between clinical risk factors and health outcomes. Specific to predicting risk, GWAS summary statistics can be used to combine association results across the genome into polygenic risk scores (PRSs) to calculate an individual’s genome-wide genetic risk for disease.
In recent years, GWAS have used thousands to hundreds of thousands of samples, including some studies that have used more than a million samples in order to have the statistical power required for the discovery of new genetic variants associated with complex traits. Many of these studies have shown that not only it is necessary to use a large number of samples in GWAS, but it is also necessary to use samples from multi-ancestral/ multi-ethnic backgrounds. non-Eurocentric samples globally would contribute to reducing biases in human genomics research which has been predominantly conducted, until a few years ago, European populations (80%), followed by East Asian populations. The inclusion of diverse populations has led to the identification of novel genetic variants associated with several complex traits of public health. Additionally, it has allowed the identification of PRS estimates that tend to be more transferable among ancestrally diverse populations.
Therefore, we encourage, and welcome research conducted on ancestrally and ethnically diverse populations across the world in this Research Topic.
Given the importance of GWAS in precision medicine and public health, this Research Topic will focus on recent advances related to empirical and methodological studies of GWAS. We welcome original research articles, systematic reviews, and meta-analyses within the scope of the Research Topic. We are specifically interested in human studies but will consider work related to outbred animal populations. The submissions can include but are not limited to the following subtopics:
• Use of GWAS to identify the mechanisms underlying complex diseases
• Estimation of heritability from GWAS data
• Advancement(s) and limitations of GWAS fine-mapping and identification of putative causal variants
• Use of GWAS data to identify potential drug/therapeutic targets
• Polygenic Risk Score (PRS) construction.
