The reflected-sigmoidish part doesn’t occur when predicting with a linear combination of genetic variants, only when predicting with the percentiles. Since PGSes are normally distributed, converting them to percentiles puts them through a a sigmoidish function, and so you need a reflected-sigmoidish function to invert it.
The connection between the raw PGS is exponential, as can be seen in the top graphs (or more realistically it is presumably sigmoidal, but we’re on the exponential part of the curve).
The reflected-sigmoidish part doesn’t occur when predicting with a linear combination of genetic variants, only when predicting with the percentiles. Since PGSes are normally distributed, converting them to percentiles puts them through a a sigmoidish function, and so you need a reflected-sigmoidish function to invert it.
The connection between the raw PGS is exponential, as can be seen in the top graphs (or more realistically it is presumably sigmoidal, but we’re on the exponential part of the curve).