Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a given trait. However, the standard PRS fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). Machine learning algorithms can be used to account for such non-linearities and interactions. We trained and validated polygenic prediction models for five complex phenotypes in a multi-ancestry population: total cholesterol, triglycerides, systolic blood pressure, sleep duration, and height. We used an ensemble method of LASSO for feature selection and gradient boosted trees (XGBoost) for non-linearities and interaction effects. In an independent test set, we found that combining a standard PRS as a feature in the XGBoost model increases the percentage variance explained (PVE) of the prediction model compared to the standard PRS by 25% for sleep duration, 26% for height, 44% for systolic blood pressure, 64% for triglycerides, and 85% for total cholesterol. Machine learning models trained in specific racial/ethnic groups performed similarly in multi-ancestry trained models, despite smaller sample sizes. The predictions of the machine learning models were superior to the standard PRS in each of the racial/ethnic groups in our study. However, among Blacks the PVE was substantially lower than for other groups. For example, the PVE for total cholesterol was 8.1%, 12.9%, and 17.4% for Blacks, Whites, and Hispanics/Latinos, respectively. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

/pdf/polygenic-risk-prediction-using-gradient-boosted-trees-4z8spji1i2.pdf

Polygenic Risk Prediction using Gradient Boosted Trees Captures Non-Linear Genetic Effects and Allele Interactions in Complex Phenotypes

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Polygenic risk score (PRS) analysis is a powerful method been used to estimate an
individual's genetic risk towards targeted traits. PRS analysis could be used to obtain
evidence of a genetic effect beyond Genome-Wide Association Studies (GWAS) results
i.e. when there are no significant markers. PRS analysis has been widely applied to
investigate the genetic basis of several traits including rare diseases. However, the
accuracy of PRS analysis depends on the genomic data of the underlying population. For
instance, several studies showed that obtaining higher prediction power of PRS analysis
is challenging for non-Europeans. In this manuscript, we reviewed the conventional PRS
methods and their application to sub-saharan Africa communities. We concluded that
the limiting factor of applying PRS analysis to sub-saharan populations is the lack of
sufficient GWAS data. Also, we recommended developing African-specific PRS tools.

Polygenic Risk Score in Africa Population: Progress andchallenges

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Polygenic Risk Score in Africa Population: Progress andchallenges