Imported from huang-sh/DeepScience (packages/science/skills/biology/comparative-genomics/bio-comparative-genomics-positive-selection/SKILL.md). Install upstream with npx skills add huang-sh/DeepScience --skill bio-comparative-genomics-positive-selection. Copyright stays with the author.

Version Compatibility

Reference examples tested with: PAML 4.10.7+, HyPhy 2.5.62+ (BUSTED-MH from Lucaci 2023 bioRxiv; FUBAR-MH from same), datamonkey.org 2024+ for web jobs, IQ-TREE 2.3.6+, MACSE V2.07+, PRANK 170427+, MAFFT 7.526+, PREQUAL 1.02+, HmmCleaner 0.243+, GARD (HyPhy bundled), RDP5 5.59+, ete4 4.1.0+, BioPython 1.84+, scipy 1.13+, polyDFE 2.0+, DFE-alpha 2.16+, GRAPES 1.1.1+, RERconverge 0.3.0+, CSUBST 1.6.0+, PhyloAcc 2.4.0+. Quest-for-Selection benchmark refreshed annually.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: codeml (PAML; check by codeml /dev/null -- prints version banner), hyphy --version, gard --help
• Python: pip show pyhyphy; introspect ete4 API for tree-labeling
• R: packageVersion('RERconverge'); ?correlateWithBinaryPhenotype

If code throws branch-site test LRT non-positive, omega2 hit upper bound 999, MEME ML mixed gradient, the most common cause is alignment error or saturated dS -- inspect alignment with TCS / Guidance2 and dS-vs-divergence-time. PAML 4.10 changed several control-file keywords from 4.9 (getSE = 1 syntax tightened).

Positive Selection Analysis

"Is this gene / branch / site under positive selection?" -> dN/dS (omega = nonsynonymous-to-synonymous substitution rate ratio) framework with explicit choice of WHICH question is being asked (gene-wide / branch-specific / site-specific / episodic) and WHICH null is being rejected. The "test failed because of selection" claim has more known confounders than any other comparative-genomics inference; mandatory pre-screens are: recombination (GARD), alignment errors (PREQUAL or HmmCleaner), saturation (dS distribution), and gBGC (W->S substitution bias). Skipping any one inflates Type-I error to ~20-50% (Anisimova & Yang 2007 MBE 24:1219; Pond 2006 Mol Biol Evol 23:1891).

• CLI: codeml PAML site, branch, branch-site models
• CLI: hyphy busted hyphy meme hyphy fel hyphy fubar hyphy absrel hyphy relax hyphy gard
• Web: datamonkey.org for HyPhy jobs without local install
• R: RERconverge::correlateWithBinaryPhenotype() for trait-rate associations
• CLI: csubst analyze for convergent substitution
• R/CLI: phyloacc for noncoding accelerated evolution

Algorithmic Taxonomy

Methodology evolves; verify the latest HyPhy / PAML manuals and the Álvarez-Carretero "Beginner's Guide" (Álvarez-Carretero et al 2023 MBE 40:msad041) before locking on a single method. The BUSTED-MH and FUBAR-MH (multi-hit) extensions specifically address known Type-I inflation from multi-nucleotide substitutions and are now recommended over basic BUSTED / FUBAR.

Decision Tree by Experimental Scenario

Per-Method Failure Modes

Recombination producing false positive selection

Trigger: Running codeml or BUSTED on a gene with recombination breakpoints (viral genes, immune genes, paralog families).

Mechanism: All single-tree codon models assume one phylogeny across all sites. Recombination produces different trees for different segments; treating them as one tree forces the model to invent rate variation that mimics positive selection (Anisimova et al 2003 Genetics 164:1229).

Symptom: PAML M8 strongly rejects M7 (LRT > 50), with omega2 = 999 (PAML upper bound) at several "selected sites"; HyPhy BUSTED highly significant; sites clustered in specific gene regions.

Fix: MANDATORY: run GARD before any positive selection test. If GARD detects breakpoints (p < 0.05), partition the alignment at breakpoints and analyze each segment separately, or use the recombination-aware MEME with the partitioned tree set. RDP5 (Martin 2021 Virus Evol 7:veaa087) is an alternative for viral genomes. GARD output .json lists breakpoint positions and posterior support.

Alignment errors producing false positives

Trigger: Using default MAFFT or MUSCLE alignment on divergent CDS sequences; skipping codon-aware aligner.

Mechanism: Frame-shifted or misaligned codons introduce apparent non-synonymous substitutions at every position; codon-aware tools see these as positive selection (Schneider 2009 GBE 1:114; Markova-Raina & Petrov 2011 GR 21:863).

Symptom: "Selected sites" cluster in alignment regions with > 30% gaps; per-site posteriors in BEB / FUBAR concentrate in ambiguous columns; PREQUAL or Guidance2 marks these regions as poorly aligned; protein alignment shows obvious mismatches.

Fix: Use codon-aware aligner: PRANK (Loytynoja 2014 Methods Mol Biol 1079:155) is the standard for selection analysis (correctly models insertions); MACSE V2 (Ranwez 2018 MBE 35:2582) handles frameshifts and pseudogenes natively; OMM_MACSE wrapper combines them. After alignment, filter with PREQUAL (segment-level) or HmmCleaner (Di Franco 2019 BMC Eco Evo 19:21); do NOT use block-filtering (Gblocks, trimAl) which removes informative sites. Segment-level filtering preferred for selection (Di Franco 2019).

Saturated synonymous sites

Trigger: Comparing distantly related taxa (deep eukaryotic divergence, > 100 Myr); dS > 3 across most pairs.

Mechanism: Synonymous sites have undergone multiple substitutions; the observed dS underestimates true dS. The model can't recover the true rate; omega = dN/dS becomes unstable at the upper bound or low (depending on which direction the bias goes).

Symptom: PAML M0 omega = 999 or near-zero; per-branch dS variance huge; sites with omega > 1 in M8 BEB are at conserved residues (paradox).

Fix: Reduce taxon sampling to species with dS < 2 on internal branches. For deep selection inference on conserved residues, use protein-level methods (BUSTED with --model GTR AA codon translation; aBSREL with protein model option) or restrict to subclade with reasonable saturation. Yang 2007 PAML manual recommends dS < 1.5 per branch.

gBGC inflating apparent positive selection

Trigger: Mammalian / vertebrate gene with W->S substitution bias on a fast-evolving lineage.

Mechanism: GC-biased gene conversion fixes A/T -> G/C alleles preferentially in regions of high recombination, independent of selection (Galtier & Duret 2007 Trends Genet 23:273; Capra 2013 PLoS Genet 9:e1003684). Standard codon models attribute this to positive selection because nonsynonymous substitutions are unequally distributed across codon positions.

Symptom: Branch with apparent positive selection sits in high-recombination region; W->S / S->W substitution ratio > 1.5; selected sites concentrate at non-degenerate codon positions; HyPhy MEME-MH and BUSTED-MH attribute signal to multi-hit rather than positive selection.

Fix: Test for gBGC: W->S substitution rates on selected branch / S->W rates; report ratio. Re-run selection analysis with HyPhy BUSTED-MH (multi-hit aware); if signal vanishes, the original "selection" was gBGC + multi-hit substitutions. For genome-wide scans, mask sub-telomeric / high-recombination regions.

Branch-site test foreground specification

Trigger: Running codeml branch-site mod A after looking at the data to choose foreground branch.

Mechanism: The branch-site test is designed for a single a priori foreground; post hoc specification inflates Type-I by ~5x because the choice was informed by the data.

Symptom: Branch-site test highly significant for the "interesting" branch; aBSREL on same data shows no significant branch (aBSREL has built-in multiple-testing correction).

Fix: Specify foreground branches in registered protocol before looking at data. For exploratory branch-wise analysis, use aBSREL (Smith 2015 MBE 32:1342) which adaptively assigns branch-specific omega classes with multiple-testing built in. If branch-site test was post hoc, apply Bonferroni correction across all branches tested + report explicitly.

LRT critical value confusion

Trigger: Computing branch-site test p-value using standard chi-square df=2.

Mechanism: The branch-site test compares mod A (4 omega classes) against mod A1 (omega2 fixed at 1). The LRT statistic distribution is a 50:50 mixture of point-mass-at-0 and chi-square(df=1), not chi-square(df=2) (Self & Liang 1987 JASA 82:605; Zhang 2005 MBE 22:2472; Wong 2004 Genetics 168:1041). Using df=2 makes the test conservative; using df=1 standard makes it anticonservative.

Symptom: Branch-site p-values incorrectly inflated or deflated; users report finding selection at very stringent thresholds.

Fix: Use the 50:50 mixture critical value: 2.71 at p=0.05 (NOT 3.84). PAML's chi2 1 LRT command applies the mixture. Many published applications use chi-square df=2 conservatively, which loses power but doesn't inflate; chi-square df=1 directly is wrong and inflates Type-I.

omega2 hitting upper bound (999)

Trigger: PAML codeml output shows omega2 = 999 for an "under selection" site class.

Mechanism: PAML codeml uses an internal upper bound of 999 (= "infinity" in single precision). Hitting it indicates numerical issue: extremely few synonymous sites in the selected class, dS underestimation, or numerical optimization failure.

Symptom: Sites flagged as positive selection have omega2 = 999; BEB posteriors for those sites are weirdly distributed.

Fix: Re-run with multiple starting values of omega (fix_omega=0, vary omega = 0.1, 0.5, 1.0, 2.0, 5.0 across runs); check that all converge to same omega. Inspect alignment at flagged sites for unusual residue conservation. If omega = 999 persists, the gene may have rare-substitution patterns; switch to BUSTED-MH which accounts for multi-hit substitutions.

Multiple-testing burden in genome scans

Trigger: Running selection tests across thousands of genes without correction.

Mechanism: With ~5000 protein-coding genes in a typical analysis, 250 will be significant at p=0.05 under H0. The false-discovery rate without correction is 50%.

Symptom: Implausibly large gene lists "under selection"; functional categories enriched are non-specific (e.g. all immune genes by FDR).

Fix: Apply FDR correction (Benjamini-Hochberg). Genes in syntenic regions are non-independent; use Benjamini-Yekutieli for stronger control under dependence. For HyPhy site-level methods, the per-site p < 0.1 default is a starting point; multiple-test correction within a gene is typically not applied (sites within a gene are dependent), but cross-gene correction is necessary. Holm-Bonferroni for strict Type-I.

Convergent substitution misinterpreted as positive selection

Trigger: Lineage-specific selection found at a residue that has independently changed in multiple unrelated lineages.

Mechanism: Convergent substitutions at the same site in independent lineages produce signals in branch-site and other tests; this is convergence, not adaptive evolution per se (though convergent residues often ARE adaptive).

Symptom: Same residue flagged in multiple unrelated lineages by branch-site test; alignment shows convergent substitutions.

Fix: Switch from selection test to convergence test: CSUBST (Fukushima & Pollock 2023 Nat Eco Evo 7:155) for combinatorial substitution analysis; RERconverge (Redlich 2024 MBE 41:msae210) for relative-rate-vs-phenotype across categorical traits; PCOC (Rey 2018) for biophysical convergence. Report both convergence test and selection test results.

Quantitative Thresholds

Selection Scan Standard Pipeline

Goal: Test all coding genes in a clade for evidence of positive selection, with full quality control.

Approach: Align with PRANK -> filter with PREQUAL -> pre-screen with GARD -> run BUSTED-MH (gene-wide) + MEME (sites) + aBSREL (branches); FDR-correct across genes; verify top candidates pass alignment / saturation / gBGC checks.

# Per-gene pipeline (parallelizable)
for og in orthogroups/*.fa; do
    base=$(basename $og .fa)

    # 1. Codon-aware MSA
    prank -d=$og -o=msa/$base.prank -codon -F

    # 2. Filter alignment errors (segment-level)
    PREQUAL -i msa/$base.prank.best.fas -o msa_filt/$base

    # 3. Recombination pre-screen
    hyphy gard --alignment msa_filt/$base.filtered --output gard/$base.json
    # If breakpoints found: partition and treat per-segment

    # 4. Gene-wide test (multi-hit aware)
    hyphy busted --alignment msa_filt/$base.filtered \
        --tree species_tree.nwk --output busted_mh/$base.json \
        --srv Yes --multiple-hits Double+Triple

    # 5. Site-level
    hyphy meme --alignment msa_filt/$base.filtered \
        --tree species_tree.nwk --output meme/$base.json

    # 6. Branch-level
    hyphy absrel --alignment msa_filt/$base.filtered \
        --tree species_tree.nwk --output absrel/$base.json
done

# 7. Aggregate and FDR
python aggregate_selection_scan.py busted_mh/ meme/ absrel/ > selection_results.tsv

'''Aggregate genome-wide selection scan results; FDR-correct.'''
import json, glob, pandas as pd
from scipy.stats import false_discovery_control

def parse_busted(p):
    d = json.load(open(p))
    return {'p_value': d.get('test results', {}).get('p-value'),
            'LRT': d.get('test results', {}).get('LRT'),
            'omega_DH': d.get('fits', {}).get('Unconstrained model', {}).get('omega3')}

def count_meme_sig(p, alpha=0.1):
    d = json.load(open(p))
    mle = d.get('MLE', {}).get('content', {}).get('0', {})
    headers = [h[0] for h in d.get('MLE', {}).get('headers', [[]])]
    pi = headers.index('p-value') if 'p-value' in headers else -1
    return sum(1 for v in mle.values() if pi >= 0 and v[pi] < alpha)

rows = []
for path in glob.glob('busted_mh/*.json'):
    gene = path.split('/')[-1].replace('.json', '')
    rows.append({'gene': gene, **parse_busted(path),
                 'meme_sig_sites': count_meme_sig(f'meme/{gene}.json')})
df = pd.DataFrame(rows)
df['busted_fdr'] = false_discovery_control(df['p_value'].fillna(1.0), method='bh')
df['adaptive'] = (df['busted_fdr'] < 0.05) & (df['meme_sig_sites'] > 0)
df.sort_values('busted_fdr').to_csv('selection_results.tsv', sep='\t', index=False)

PAML Branch-Site Test (Operational)

Goal: Test for episodic positive selection on a pre-specified foreground branch.

Approach: Mark foreground in newick (#1) -> codeml branch-site mod A vs A1 -> LRT against 50:50 mixture chi^2(0):chi^2(1).

# Mark foreground branch: use ete4 or manually
python -c "
from ete4 import Tree
t = Tree('species_tree.nwk', format=1)
target = t.search_nodes(name='target_species')[0]
target.name = target.name + ' #1'
print(t.write(format=1))
" > foreground.nwk

# Branch-site mod A (alternative)
cat > codeml_modA.ctl << 'EOF'
seqfile = alignment.phy
treefile = foreground.nwk
outfile = mod_A.mlc
runmode = 0
seqtype = 1
CodonFreq = 2
model = 2
NSsites = 2
fix_kappa = 0
kappa = 2
fix_omega = 0
omega = 0.4
RateAncestor = 1
cleandata = 0
EOF
codeml codeml_modA.ctl

# Null model A1 (omega_2 = 1)
cp codeml_modA.ctl codeml_modA1.ctl
sed -i 's/^omega = 0.4/omega = 1/' codeml_modA1.ctl
sed -i 's/^fix_omega = 0/fix_omega = 1/' codeml_modA1.ctl
sed -i 's/outfile = mod_A.mlc/outfile = mod_A1.mlc/' codeml_modA1.ctl
codeml codeml_modA1.ctl

'''Branch-site test LRT with 50:50 mixture critical value.'''
from scipy.stats import chi2

def branch_site_lrt(lnL_alt, lnL_null):
    lrt = 2 * (lnL_alt - lnL_null)
    if lrt <= 0:
        return {'LRT': lrt, 'p_value': 0.5}
    # 50:50 mixture of chi^2(0) and chi^2(1)
    p = 0.5 * (1 - chi2.cdf(lrt, df=1))
    return {'LRT': lrt, 'p_value': p}

Foreground branch must be specified before viewing data; for genome-wide screens with no a priori branch, use aBSREL instead. Bayes Empirical Bayes (BEB) sites with posterior > 0.95 on positive-selection class are the per-site call.

McDonald-Kreitman with Asymptotic Alpha

Goal: Estimate adaptive substitution rate alpha = 1 - (Ds Pn) / (Dn Ps), corrected for slightly deleterious bias.

Approach: Compute counts of synonymous and nonsynonymous polymorphisms (P) and divergences (D); fit asymptotic alpha by binning by minor-allele frequency and extrapolating.

# Standard MK
mk_alpha <- function(Dn, Ds, Pn, Ps) {
    1 - (Ds * Pn) / (Dn * Ps)
}

# Asymptotic alpha via the Messer-Petrov 2013 web tool
# (https://benhaller.com/messerlab/asymptoticMK.html) or the impMKT R package
# (Murga-Moreno 2022 G3 12:jkac206) which wraps the asymptotic computation.
library(impMKT)
# Inputs: per-frequency-bin (Pn, Ps) plus genome-wide (Dn, Ds)
freq_bins <- seq(0.01, 0.5, 0.01)
pn_by_freq <- c(...)  # nonsyn polymorphism count per bin
ps_by_freq <- c(...)  # syn polymorphism count per bin
fit <- asymptoticMK(
    Dn = total_dn, Ds = total_ds,
    Pn = pn_by_freq, Ps = ps_by_freq,
    x = freq_bins
)
fit$alpha_asymptotic       # adaptive substitution rate, corrected
fit$alpha_original         # original MK (biased)

For full DFE inference (alpha + distribution of fitness effects), use polyDFE (Tataru-Bataillon 2019):

polyDFE -d data.txt -m C -i estimates.init -o output_basename

DFE-alpha (Eyre-Walker 2009) and GRAPES (Galtier 2016) are alternatives; GRAPES is most robust for genome-wide adaptive-substitution scans.

RERconverge for Trait-Correlated Rate Shifts

Goal: Identify genes whose evolutionary rate correlates with a binary or categorical phenotype across the species tree.

Approach: Compute per-gene relative evolutionary rates -> correlate against phenotype -> Bonferroni or FDR-correct across genes.

library(RERconverge)

# Read alignments and tree
trees <- readTrees('orthogroup_trees.txt', minSpecies = 10)
rer <- getAllResiduals(trees, useSpecies = species_names, transform = 'sqrt',
                       weighted = TRUE, scale = TRUE)

# Define binary phenotype (e.g., echolocation in mammals)
phen_paths <- foreground2Paths(c('Bat1', 'Bat2', 'Dolphin'), trees, clade = 'terminal')
phen_vec <- foreground2Tree(c('Bat1', 'Bat2', 'Dolphin'), trees, clade = 'terminal')

# Correlate
cors <- correlateWithBinaryPhenotype(rer, phen_paths, min.sp = 10, min.pos = 2,
                                      weighted = 'auto')
top_genes <- cors[order(cors$P), ][1:50, ]

For categorical traits (more than binary), Redlich 2024 MBE 41:msae210 extends RERconverge.

Reconciliation: When Methods Disagree

Operational rule for publication: GARD pre-screen documented as negative + PREQUAL/HmmCleaner filtering applied + dS < 1.5 per branch + W->S ratio not elevated + BUSTED-MH significant (gene-wide) + MEME flags sites + aBSREL flags branches with consistent direction = publication-ready evidence. Single-method significance (especially M8 vs M7 alone) should be downgraded.

Cohort Gotchas

• Immune / MHC loci: intra-genic recombination is high; GARD pre-screen mandatory; high apparent positive selection often reflects gene conversion between alleles, not adaptive change
• Viral genomes: rapid evolution + recombination + multi-hit substitutions common; BUSTED-MH and FUBAR-MH essential; use RDP5 for recombination detection
• Plasmodium / Trypanosoma: high codon-usage bias and multi-hit substitutions; use BUSTED-MH and BUSTED-S
• Mammalian X-chromosome: higher dS than autosomes (male-driven evolution); gBGC asymmetry by chromosome; reduce dS threshold for X-linked genes
• Recent human / population genetics: dS dramatically underestimated at recent divergence; use SFS-based methods (asymptotic alpha, polyDFE)
• Convergent evolution traits (echolocation, marine): RERconverge / CSUBST / PhyloAcc-noncoding designed for these; codon-based methods alone miss the convergent signal

Anticipated Reviewer Pushback

Common Errors

Tool Installation Notes

conda install -c bioconda paml hyphy gard prank prequal hmmcleaner
# RDP5: http://web.cbio.uct.ac.za/~darren/rdp.html
# MACSE V2: wget https://bioweb.supagro.inra.fr/macse/releases/macse_v2.07.jar
pip install ete4 pyhyphy csubst
Rscript -e "install.packages(c('asymptoticMK', 'polyDFE'))"
Rscript -e "remotes::install_github('nclark-lab/RERconverge')"
# polyDFE / GRAPES / DFE-alpha source binaries at respective github / bioconda channels

For genome-wide scans (> 5000 genes), parallelize per-gene analyses with Snakemake / Nextflow.

References

• Yang Z 1997 CABIOS 13:555 (PAML codeml)
• Yang Z et al 2000 Genetics 155:431 (codon models M0-M8)
• Yang Z & Bielawski JP 2000 TREE 15:496 (codon model framework)
• Zhang J et al 2005 MBE 22:2472 (branch-site mod A); Wong WSW et al 2004 Genetics 168:1041 (LRT mixture); Self SG & Liang K-Y 1987 JASA 82:605 (LRT boundary)
• Swanson WJ et al 2003 Genetics 165:725 (M8a null); Bielawski JP & Yang Z 2004 J Mol Evol 59:121 (clade models)
• Anisimova M & Yang Z 2007 MBE 24:1219 (PAML comparison); Anisimova M et al 2003 Genetics 164:1229 (recombination FP); Anisimova M, Bielawski JP & Yang Z 2001 MBE 18:1585 (LRT power)
• Pond SLK et al 2006 MBE 23:1891 (GARD); Martin DP et al 2021 Virus Evol 7:veaa087 (RDP5)
• Kosakovsky Pond SL & Frost SDW 2005 MBE 22:1208 (FEL); Murrell B et al 2012 PLoS Genet 8:e1002764 (MEME); Murrell B et al 2013 MBE 30:1196 (FUBAR)
• Murrell B et al 2015 MBE 32:1365 (BUSTED); Wisotsky SR et al 2020 MBE 37:2430 (BUSTED-S); Lucaci AG et al 2023 bioRxiv 2022.12.02.518889 (BUSTED-MH)
• Smith MD et al 2015 MBE 32:1342 (aBSREL); Wertheim JO et al 2015 MBE 32:820 (RELAX)
• McDonald JH & Kreitman M 1991 Nature 351:652 (MK); Smith NGC & Eyre-Walker A 2002 Nature 415:1022 (alpha); Messer PW & Petrov DA 2013 PNAS 110:8615 (asymptotic alpha)
• Murga-Moreno J et al 2022 G3 12:jkac206 (impMKT); Tataru P & Bataillon T 2019 Bioinformatics 35:2868 (polyDFE); Eyre-Walker A & Keightley PD 2009 MBE 26:2097 (DFE-alpha); Galtier N 2016 PLoS Genet 12:e1005774 (GRAPES)
• Galtier N & Duret L 2007 Trends Genet 23:273 (gBGC); Capra JA et al 2013 PLoS Genet 9:e1003684 (gBGC genome-scale)
• Schneider A et al 2009 GBE 1:114 + Markova-Raina P & Petrov D 2011 GR 21:863 (alignment-error FP)
• Loytynoja A 2014 Methods Mol Biol 1079:155 (PRANK); Ranwez V et al 2018 MBE 35:2582 (MACSE V2); Whelan S et al 2018 Bioinformatics 34:3929 (PREQUAL); Di Franco A et al 2019 BMC Eco Evo 19:21 (HmmCleaner)
• Yang Z 2007 PAML manual; Álvarez-Carretero S et al 2023 MBE 40:msad041 (Beginner's Guide PAML)
• Kowalczyk A et al 2019 Bioinformatics 35:4815 + Redlich R et al 2024 MBE 41:msae210 (RERconverge)
• Fukushima K & Pollock DD 2023 Nat Eco Evo 7:155 (CSUBST); Hu Z et al 2019 MBE 36:1086 (PhyloAcc); Pollard KS et al 2010 GR 20:110 (phyloP); Rey C et al 2018 MBE 35:2296 (PCOC)

Related Skills

• comparative-genomics/ortholog-inference - Single-copy ortholog alignments as input
• comparative-genomics/ancestral-reconstruction - Branch-specific ancestral sequence inference
• comparative-genomics/gene-tree-species-tree-reconciliation - Reconciled gene trees as PAML input
• alignment/multiple-alignment - PRANK / MACSE codon-aware MSA
• alignment/alignment-trimming - PREQUAL / HmmCleaner segment filtering
• phylogenetics/modern-tree-inference - Tree inference required for codeml
• population-genetics/selection-statistics - SFS-based alpha + DFE methods
• causal-genomics/heritability-partitioning - LDSC partition includes positive-selection annotations
• variant-calling/variant-annotation - Functional annotation of selected sites