(marginal) least-squares estimate of an individual marker effect I am reading the paper from Vilhjálmsson et al (2015), Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores, at the "Phenotype Model" section, they said:

Let Y be a N × 1 phenotype vector and X be a N × M genotype matrix, where N is the number of individuals, and M is the number of genetic variants. For simplicity, we will assume throughout that the phenotype Y and individual genetic variants Xi have been mean centered and standardized to have variance 1. We model the phenotype as a linear combination of M genetic effects and an independent environmental effect ε, i.e., $Y=\sum_{i=1}^{M}X_{i}\beta_{i}+\epsilon$, where $X_{i}$ denotes the ith genetic variant, $\beta_{i}$ is its true effect, and ε is the environmental and noise contribution. In this setting, the (marginal) least-squares estimate of an individual marker effect is $\hat{\beta_{i}} = {X_{i}^{'}}\mathbf {Y}/N$.

I don't know how they got the final expression of the mariginal least-squares estimate of each individual marker effect? From the [OLS Wikipedia] (https://en.wikipedia.org/wiki/Ordinary_least_squares),
$${\hat {\boldsymbol {\beta }}}=\left(\mathbf {X} ^{\mathsf {T}}\mathbf {X} \right)^{-1}\mathbf {X} ^{\mathsf {T}}\mathbf {y} $$
But, how does this link to the expression in this paper?
I really need some help. Thanks in advance.

The prime notation $'$ actually means $^T$ (transpose) here. And if you look lower down on the wikipedia page:

The product $N=X^T X$ is a Gram matrix

So they're different ways of writing the same function.
Also, just a note that PRS-CS is much more commonly used than LDPred these days.
