Update docs for ancestry release

CITATIONS.md

@@ -13,12 +13,12 @@
 * [PGS Catalog API](https://pubmed.ncbi.nlm.nih.gov/33692568/)
     > Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, Buniello A, McMahon A, Abraham G, Chapman M, Parkinson H, Danesh J. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics. 2021 Apr;53(4):420-5. doi: 10.1038/s41588-021-00783-5. PubMed PMID: 33692568.
 
-* [PLINK 1](https://pubmed.ncbi.nlm.nih.gov/17701901/)
-    > Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics. 2007 Sep 1;81(3):559-75. doi: 10.1086/519795. PubMed PMID:  17701901. PubMed Central PMCID: PMC1950838.
-
 * [PLINK 2](https://pubmed.ncbi.nlm.nih.gov/25722852/)
     > Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015 Dec 1;4(1):s13742-015. doi: 10.1186/s13742-015-0047-8. PubMed PMID: 25722852. PubMed Central PMCID: PMC4342193.
 
+* [FRAPOSA](https://pubmed.ncbi.nlm.nih.gov/32196066/)
+    > Zhang, D., et al. (2020) Fast and robust ancestry prediction using principal component analysis. Bioinformatics 36(11):3439–3446. https://doi.org/10.1093/bioinformatics/btaa152
+
 ## Software packaging/containerisation tools
 
 * [Anaconda](https://anaconda.com)

README.md

@@ -5,9 +5,9 @@
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7577371.svg)](https://doi.org/10.5281/zenodo.7577371)
 
 [![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-≥22.10.0-23aa62.svg?labelColor=000000)](https://www.nextflow.io/)
-[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
 [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)
 [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)
+[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)
 
 ## Introduction
 
@@ -19,23 +19,26 @@ and/or user-defined PGS/PRS.
 ## Pipeline summary
 
 <p align="center">
-  <img width="70%" src="https://user-images.githubusercontent.com/11425618/195053396-a3eaf31c-b3d5-44ff-a36c-4ef6d7958668.png">
+  <img width="80%" src="https://github.com/PGScatalog/pgsc_calc/assets/11425618/f766b28c-0f75-4344-abf3-3463946e36cc">
 </p>
 
+The workflow performs the following steps:
+
 * Downloading scoring files using the PGS Catalog API in a specified genome build (GRCh37 and GRCh38).
 * Reading custom scoring files (and performing a liftover if genotyping data is in a different build).
 * Automatically combines and creates scoring files for efficient parallel computation of multiple PGS
     - Matching variants in the scoring files against variants in the target dataset (in plink bfile/pfile or VCF format)
 * Calculates PGS for all samples (linear sum of weights and dosages)
 * Creates a summary report to visualize score distributions and pipeline metadata (variant matching QC)
 
-### Features in development
+And optionally:
+
+- Genetic Ancestry: calculate similarity of target samples to populations in a
+  reference dataset ([1000 Genomes (1000G)](http://www.nature.com/nature/journal/v526/n7571/full/nature15393.html)), using principal components analysis (PCA)
+- PGS Normalization: Using reference population data and/or PCA projections to report
+  individual-level PGS predictions (e.g. percentiles, z-scores) that account for genetic ancestry
 
-- *Genetic Ancestry*: calculate similarity of target samples to populations in a
-  reference dataset (e.g. [1000 Genomes (1000G)](http://www.nature.com/nature/journal/v526/n7571/full/nature15393.html), 
-  [Human Genome Diversity Project (HGDP)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115999/)) using principal components analysis (PCA).
-- *PGS Normalization*: Using reference population data and/or PCA projections to report
-  individual-level PGS predictions (e.g. percentiles, z-scores) that account for genetic ancestry.
+See documentation for a list of planned [features under development](https://pgsc-calc.readthedocs.io/en/latest/index.html#Features-under-development).
 
 ## Quick start
 
@@ -87,7 +90,7 @@ manuscript describing the tool is in preparation. In the meantime if you use the
 tool we ask you to cite the repo and the paper describing the PGS Catalog
 resource:
 
-- >PGS Catalog Calculator _(in development)_. PGS Catalog
+- >PGS Catalog Calculator _(preprint forthcoming)_. PGS Catalog
   Team. [https://github.com/PGScatalog/pgsc_calc](https://github.com/PGScatalog/pgsc_calc)
 - >Lambert _et al._ (2021) The Polygenic Score Catalog as an open database for
 reproducibility and systematic evaluation.  Nature Genetics. 53:420–425

docs/_templates/globaltoc.html

@@ -6,12 +6,12 @@ <h3>Contents</h3>
   <li><a href="{{ pathto('how-to/index') }}">How-to guides</a></li>
   <li><a href="{{ pathto('reference/index') }}">Reference guides</a></li>
   <ul>
-    <li><a href="{{ pathto('reference/params') }}">Input Parameters/Flags</a></li>
-    <li><a href="{{ pathto('reference/input') }}">Samplesheet schema</a></li>
-    <li><a href="{{ pathto('reference/containers') }}">Containers</a></li>
+    <li><a href="{{ pathto('reference/params') }}">Workflow parameters</a></li>
   </ul>
   <li><a href="{{ pathto('explanation/index') }}">Explanations</a></li>
   <ul>
+    <li><a href="{{ pathto('explanation/plink2') }}">Why not use plink2?</a></li>
+    <li><a href="{{ pathto('explanation/geneticancestry') }}">Adjusting PGS with genetic ancestry</a></li>
     <li><a href="{{ pathto('explanation/output') }}">Outputs & report</a></li>
   </ul>
   <li><a href="{{ pathto('troubleshooting') }}">Troubleshooting</a></li>
@@ -32,6 +32,7 @@ <h3>Useful links</h3>
   <li><a href="https://github.com/pgscatalog/pgsc_calc">pgsc_calc Github</a></li>
   <ul>
       <li><a href="https://github.com/pgscatalog/pgsc_calc/issues">Issue tracker</a></li>
+      <li><a href="https://github.com/PGScatalog/pgsc_calc/discussions">Discussion board</a></li>
   </ul>
   <li><a href="https://github.com/PGScatalog/pgscatalog_utils">pgscatalog_utils Github</a></li>
 </ul>

docs/changelog.rst

@@ -8,7 +8,25 @@ will only occur in major versions with changes noted in this changelog.
 
 .. _`semantic versioning`: https://semver.org/
 
-pgsc_calc v1.3.2 (2022-01-27)
+pgsc_calc v2.0.0 (2023-08-08)
+-----------------------------
+
+This major release features breaking changes to samplesheet structure to provide
+more flexible support for extra genomic file types in the future. Two major new
+features were implemented in this release:
+
+- Genetic ancestry group similarity is calculated to a population reference panel
+  (default: 1000 Genomes) when the ``--run_ancestry`` flag is supplied. This runs
+  using PCA and projection implemented in the ``fraposa_pgsc (v0.1.0)`` package.
+- Calculated PGS can be adjusted for genetic ancestry using empirical PGS distributions
+  from the most similar reference panel population or continuous PCA-based regressions.
+
+These new features are optional and don't run in the default workflow. Other features
+included in the release are:
+
+- Speed optimizations for PGS scoring (skipping allele frequency calculation)
+
+pgsc_calc v1.3.2 (2023-01-27)
 -----------------------------
 
 This patch fixes a bug that made some PGS Catalog scoring files incompatible
@@ -18,7 +36,7 @@ reporting the problem.
 
 .. _`@j0n-a`: https://github.com/PGScatalog/pgsc_calc/issues/79
 
-pgsc_calc v1.3.1 (2022-01-24)
+pgsc_calc v1.3.1 (2023-01-24)
 -----------------------------
 
 This patch fixes a bug that breaks the workflow if all variants in one or more