Unlocking the Secrets of Our Genes: Tools for Predicting Complex Traits
Have you ever wondered why some people are more prone to certain diseases than others? Or why some people are taller or have a different body type? A big part of the answer lies in our genes. Scientists are working hard to understand how our genes influence these complex traits, and they're making exciting progress. This blog post will explore a recent study that introduces new tools to improve our ability to predict these traits using genetic information.
The Challenge: Predicting Traits
Many of the traits that make us who we are – like height, weight, disease risk, and even personality – are influenced by many different genes working together. These are called complex traits, and predicting them is a difficult task. One way to do this is to create a polygenic risk score (PRS). A PRS is essentially a formula that combines the effects of many genetic variations (called SNPs) to estimate an individual's likelihood of having a particular trait.
The basic idea is that each SNP contributes a little something to a trait, and by adding up all the small contributions, you get a prediction. The problem is that existing tools for building these prediction models often assume that all SNPs contribute equally. However, this isn't how it works in real life. Some SNPs have a bigger impact than others. The study I'm about to describe addresses this issue.
The Innovation: Heritability Models
The researchers developed new prediction tools that allow scientists to specify a heritability model that dictates how much each SNP contributes to a given trait. This is a big step forward because it lets researchers use a more realistic picture of how genes influence complex traits.
* Existing methods often use the "GCTA Model," which assumes that all SNPs contribute equally.
* The new tools allow the user to choose from different heritability models. One of the best is the "BLD-LDAK Model," which considers the frequency of the SNP, local levels of linkage disequilibrium, and functional annotations.
* Linkage disequilibrium is the tendency for some SNPs to be inherited together.
* Functional annotations refer to information about the function of the SNP.
How the New Tools Work
The researchers created eight new prediction tools that build upon existing ones, but with the key difference that they allow the user to choose the heritability model. They developed tools for two scenarios:
* When individual-level genotype and phenotype data are available they recommend LDAK-Bolt-Predict. This is a generalized version of the existing tool, Bolt-LMM.
* When only summary statistics are available, they recommend LDAK-BayesR-SS, which is a generalized version of SBayesR.
The researchers compared the performance of the new tools with existing methods using a huge dataset from the UK Biobank. This dataset includes genetic and health information from hundreds of thousands of people.
The Results: A Significant Improvement
The results were impressive. The new tools, when using an improved heritability model, performed better than existing tools for nearly all traits analyzed.
* For individual-level data, using the BLD-LDAK model with LDAK-Bolt-Predict improved prediction accuracy by an average of 14%. The tool outperformed existing tools such as Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes.
* For summary statistics, the LDAK-BayesR-SS tool using the BLD-LDAK model outperformed existing tools like lassosum, sBLUP, LDpred, and SBayesR for 223 out of 225 phenotypes.
* The improvement was equivalent to increasing the sample size by about 25%.
The research also showed that the new tools improved prediction accuracy across a wide range of traits, whether they were continuous, binary, or ordinal, and whether they had low, medium or high heritability.
Why This Matters
These findings have important implications for the future of genetic research and personalized medicine.
* Better understanding of genetic architecture: Improved prediction tools can help scientists better understand how genes influence complex traits.
* Identifying disease risk: More accurate PRS can help identify individuals at higher risk of developing certain diseases, allowing for earlier intervention and preventive measures.
* Precision medicine: These tools can also help tailor treatments to individual patients based on their genetic makeup. For example, it may allow scientists to determine who is most likely to benefit from a particular drug.
Moving Forward
This study is a significant step toward unlocking the full potential of our genetic information. While this new method improves the accuracy of PRS, there is still room for improvement. The researchers suggest that the focus should now be on creating even more realistic heritability models, since this seems to be one of the biggest areas for improving prediction accuracy. As our understanding of the genome grows, we can expect even better tools for predicting complex traits and improving human health.
In Conclusion
This study is an exciting advancement in the field of genetic prediction and further demonstrates the complexity of how our genes determine our traits. By moving beyond simple assumptions and toward more realistic models, scientists are bringing us closer to understanding the secrets of our DNA and paving the way for a future of personalized medicine.
References:
Zhang, Q., Privé, F., Vilhjálmsson, B. et al. Improved genetic prediction of complex traits from individual-level data or summary statistics. Nat Commun 12, 4192 (2021).
