Hi Philipp, I think your explanation of variance explained is more like prediction accuracy (i.e., construct polygenic score (PGS) and calculate the squared correlation between PGS and phenotype from an independent target sample). In my understanding, variance explained is the proportion of variance, which is the variance of the estimated PGS divided by the variance of phenotype. It is more like heritability.
In Yengo 2018 height and BMI paper, he actually distinguished the two (i.e., variance explained and prediction accuracy) by
For height, the variance explained increased from ~24.6% using 3,290 GWS SNPs to ~34.7% (s.e.1.9%) using ~15,000 SNP with p<0.001. The prediction R2 also increased from ~19.7% to ~24.4%.
Let's not use any software to see how "97 SNPs explain 2.7% of BMI". First, we use the training sample to estimate effect size for all SNPs (e.g., OLSE). Then we construct PGS of that 97 SNPs by calculating the sum of estimated effect sizes multiplying allele dosage from an independent sample (i.e., target sample). Then for each sample in the target sample, there will be a corresponding PGS. Next, we calculate the variance of PGS and divided it by the phenotypic variance in the target sample.
Please correct me if I have any misunderstanding. Thank you.
That probably means something like...
"You can determine someone's racial composition or location by looking at their SNPs. Those are both factors in BMI, which makes the SNPs correlated. These SNPs have no known causal relationship with BMI, but it's easy to use them to publish papers."
The rationale here is "heritability" which measures the proportion of the total phenotypic variation that's due to genetic variance. The percentage here is to describe the percentage of BMI variance due to genetic variance in the study cohort. (Total phenotypic variance = genetic variance + environmental variance). But I still don't know how is this calculated.
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