New GIFT Method Outshines GWAS in Genetic Breakthrough

By Dr. Chinta SidharthanReviewed by Lily Ramsey, LLMNov 8 2024

Genome-wide association studies (GWAS) have traditionally been used for mapping genotypes to phenotypes, which has been central to understanding the genetic influences on complex traits. However, GWAS has several limitations in fully capturing the genetic interactions, especially in smaller or more complicated datasets.

In a recent study published in Physiological Genomics, a team of scientists explored an alternative method called Genomic Informational Field Theory (GIFT). GIFT is designed to enhance the detection of genetic associations through a “top-down” approach where phenotypic variation directly influences the analysis. The scientists also compared the effectiveness of GIFT against that of GWAS using datasets on sheep genetics and metabolism.

​​​​​​​Study: Investigative power of genomic informational field theory relative to genome-wide association studies for genotype-phenotype mapping. Image Credit: Prostock-studio/Shutterstock.com

Background

Numerous genetic studies have focused on mapping the relationship between genetic variation and complex traits, traditionally using GWAS. Studies using GWAS can explore large population data and identify genetic variants linked to specific phenotypes by calculating statistical averages across data groups.

Although this method has uncovered many important associations in human disease, it requires large sample sizes, and capturing the intricate genotype-phenotype relationships is often challenging for GWAS in datasets involving subtle genetic interactions.

About the Study

The present study discussed GIFT, a new method developed to improve genetic analysis and phenotypic association accuracy for traits impacted by multiple genetic and environmental factors.

The researchers used two datasets to evaluate the analytic abilities of GIFT and compare them to those of the traditionally used GWAS. The first dataset focused on 600 Scottish Blackface lambs and examined the association of bone area traits in the ischium located in the pelvis with specific genetic variants.

They measured the cross-sectional bone area using computed tomography (CT) scans while accounting for factors like the age of the female parent or dam, birth year, sex, and farm group as fixed effects.

GWAS was used initially to identify significant quantitative trait loci (QTLs) on chromosome 6 linked to these traits. Using this dataset, the researchers compared the variant-based GWAS with the alternative top-down approach provided by GIFT.

The second dataset consisted of liver samples obtained from Texel lambs from various farms in the United Kingdom (U.K.). Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) were extracted from the liver tissue, followed by targeted single-nucleotide polymorphism (SNP) profiling to examine seven specific metabolites involved in the methionine cycle and one-carbon metabolism pathways.

These metabolites include S-adenosyl methionine, methylcobalamin, and trimethylglycine, all essential for methionine cycling and succinate synthesis.

The study used custom SNP arrays to profile genetic markers associated with these metabolites, creating a specific genotype-phenotype map for liver metabolism.

For both datasets, the researchers used GIFT to map the genetic paths based on phenotype data and generate cumulative sum curves for individual SNPs, called genetic paths. They then compared these to random permutations to establish a null hypothesis.

The GWAS results were then contrasted with GIFT-generated genetic paths to assess each method's capacity to capture phenotypic variability and potential regulatory elements in these genetic associations.

Major Findings

The study found that GIFT significantly enhanced the detection of genetic associations in comparison to GWAS.

In the first dataset, both GWAS and GIFT identified chromosome 6 as a key region for the bone area trait. However, the analysis using GIFT revealed additional associations on other chromosomes, highlighting the more subtle genetic influences that GWAS was unable to detect.

The high sensitivity of GIFT to the more subtle genetic associations allowed it to capture variations beyond the primary QTL on chromosome 6, suggesting that the bone area traits were associated with more genetic markers than were detected using GWAS.

Furthermore, GIFT also outperformed GWAS in the analysis for dataset 2 and revealed significant associations between certain SNPs and one-carbon metabolism metabolites. Several SNPs with strong connections to metabolites, such as S-adenosyl methionine and trimethylglycine, were identified by GIFT, and GWAS did not detect these SNPs.

By utilizing the entire range of phenotypes, GIFT demonstrated higher precision in detecting these associations, allowing researchers to detect the regulatory elements potentially involved in metabolic pathways.

Furthermore, the overlap of SNPs identified by both methods was limited, which highlighted the ability of GIFT to identify unique genetic information that the averaging techniques used in GWAS might miss.

Conclusions

Overall, the study provided evidence that GIFT's top-down approach revealed a more comprehensive genetic association with traits and demonstrated that GIFT offered a more sensitive alternative to GWAS for analyzing complex characteristics.

These findings supported the potential applications of GIFT in medical and agricultural genomics, as well as numerous other fields.