GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study

Abstract

In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.

Improvement of Genomic Predictions in Small Breeds by Construction of Genomic Relationship Matrix Through Variable Selection

“… is the inversion of the coefficient matrix of the mixed model equation where aa is the block referring to the genetic effect of animals. What was reported by Pocrnic et al (2019) could explain the lower performance identified by Akbarzadeh et al (2021) when subsets of 1 and 5% of SNPs were considered ( Akbarzadeh et al, 2021 ). Indeed, discarding too many SNPs from the construction of G may omit the inclusion of important eigenvalues.…”

“…This finding agrees with that of the extensive literature supporting the increased accuracy of Bayesian variable selection models in many different species (Lourenco et al, 2014; Mehrban et al, 2021 ; Yoshida et al, 2018; Zhu et al, 2021 ). For example, Akbarzadeh et al (2021) integrated only a subset of chosen SNPs into the GBLUP framework based on a classical GWAS analysis (i.e., 1, 5, 10, and 50% of significant SNPs). A slightly greater accuracy than that in the canonical GBLUP was observed when G was constructed using only the best 10 and 50% SNPs; contrariwise, models using the 1 and 5% of the SNP prediction underperformed.…”

“…A slightly greater accuracy than that in the canonical GBLUP was observed when G was constructed using only the best 10 and 50% SNPs; contrariwise, models using the 1 and 5% of the SNP prediction underperformed. Furthermore, Akbarzadeh et al (2021) reported a dramatic decline in performance when the same percentage of SNPs was randomly chosen. We tried preliminary tests of a similar approach—construction of the G matrix using the top 500, 1,000, and 50,000 SNPs ranked by their absolute SNP effect values calculated through back solutions—in Rendena breed; however, we immediately discarded this approach because of the extreme bias and inflated breeding value predictions (these findings are reported by Mancin et al, 2022 in press ).…”

The AGT epistasis pattern proposed a novel role for ZBED9 in regulating blood pressure: Tehran Cardiometabolic genetic study (TCGS)

“…Blood samples of TCGS participants were genotyped using humanOmniExpress-24-v1 bead chips on 652919 single nucleotide polymorphisms with an average mean distance of 4 kilobases for each individual at deCODE genetic company (16). Genomic quality control (QC) was implemented in comprehensive steps (10,17).…”

The AGT Epistasis Pattern Proposed a Novel Role for ZBED9 In Regulating Blood Pressure: Tehran Cardiometabolic Genetic Study (TCGS)

“…Blood samples of TCGS participants were genotyped using humanOmniExpress-24-v1 bead chips on 652919 single nucleotide polymorphisms with an average mean distance of 4 kilobases for each individual at deCODE genetic company (16). Genomic quality control (QC) was implemented in comprehensive steps (10,17).…”