There are few good textbooks for this, mostly because the field is progressing very rapidly. The book by Mills, Barban and Tropps is a good introduction.
A first good introduction to the methods of estimation with R is the page of Meszaros.
This tutorial gives the essential elements. As stated in the Intro:
This tutorial provides a step-by-step guide to performing basic polygenic risk score (PRS) analyses and accompanies our PRS Guide paper. The aim of this tutorial is to provide a simple introduction of PRS analyses to those new to PRS, while equipping existing users with a better understanding of the processes and implementation "underneath the hood" of popular PRS software.
The basic material on Genomic SEM is in the page of the Lifespan Development Lab. The model is presented in the supplementary material to the paper.
An introduction to Genomic SEM is here
You can start playing around with data by following the instructions provided by Nivard and Delmange here for example, where they say that to get:
some experience using GenomicSEM, go here for a general introduction to GenomicSEM and basic tutorials. This code will not download LD scores, the HapMap3 reference file or the 1000Genomes based allele frequency reference file. Those are found here, here and here.
An application of Genomic SEM is GWAS by subtraction. A tutorial is available here. As they explain in the introductory paragraph:
The preprint by Demange et al. (here on BioRxiv) uses GenomicSEM and the input GWAS of educational attainment (EA) and cognitive performance (CP) to derive two new GWASes: cognitive (Cog) and non-cognitive (NonCog) contributions to educational attainment. We feel this is a very valuable analysis as it allows us to evaluate the genetic architecture and correlates of traits, other than cognition, that contribute to educational success. For a full account please refer to the paper, the present document is a tutorial on GWAS-by-subtraction which will enable you to better understand our work but perhaps more importantly run your own GWAS-by-subtraction.
The original LDPred paper is this. The GitHub for the method is here.
LDPred2 is an extension of the original method. A presentation of the methods is in the Prive page on the topic. You can find a tutorial here. , as a section in the general tutorial on PGS already mentioned in the section Computation of PGS above. In the tutorial you can see how the method works on data. The data are also available in the tutorial, which is self-contained.
