bio-comparative-genomics-positive-selection

name	bio-comparative-genomics-positive-selection
description	Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
origin	openai4s
category	bioskills/comparative-genomics
metadata	{"tool_type":"mixed","primary_tool":"PAML","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: PAML 4.10.7+, HyPhy 2.5.62+ (BUSTED-MH from Lucaci 2023 MBE 40:msad150; 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

Method	Question	Null model	Strength	Fails when
PAML codeml M0 (Yang 1997 CABIOS 13:555)	Gene-wide single-omega estimate	-- (point estimate)	Standard reference omega; baseline test	Site heterogeneity (use M3+)
codeml M1a vs M2a (Yang 2000 Genetics 155:431)	Any site under selection?	Nearly neutral, 2-category	Conservative; LRT df=2	Low power for episodic selection
codeml M7 vs M8	More-sensitive site test	Beta(0,1)	Higher power than M1a/M2a	Higher false-positive rate; relaxed-constraint mimics selection
codeml M8 vs M8a (Swanson 2003 MBE 20:18)	Conservative site test (omega2 = 1 null)	Beta + omega2=1	Cleanest LRT df=1; preferred site test	Lower power than M7 vs M8
codeml branch-site mod A (Zhang 2005 MBE 22:2472)	Selection on pre-specified foreground branch	A1 (omega2=1 fixed)	Most powerful for episodic per-branch selection	Foreground specified post hoc -> Type-I inflation
codeml clade model (Bielawski & Yang 2004 J Mol Evol 59:121)	Different omega between named clades	M3 with shared categories	Tests for shifted selection regime	Requires clade pre-specification
codeml free-ratio	Per-branch omega estimates (exploratory)	M0	Visualizes branch-wise variation	Unidentifiable for short branches; no formal LRT
HyPhy BUSTED (Murrell 2015 MBE 32:1365)	Any episodic selection on any branch site?	No omega+ class	Site + branch joint; foreground assignable	Sensitive to alignment errors
HyPhy BUSTED-S (Wisotsky 2020 MBE 37:2430)	BUSTED with synonymous-rate variation	--	Corrects for SRV; reduces false positives	Slightly less power than BUSTED
HyPhy BUSTED-MH (Lucaci 2023 MBE 40:msad150)	BUSTED with multi-nucleotide substitutions	--	Captures complex (multi-hit) substitutions; reduces false positives from MNMs	Newer; limited benchmarking
HyPhy BUSTED-PH	Two phenotypes; selection on one not other	--	Tests phenotype-specific selection

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

Scenario	Recommended approach	Why
Single gene, mammalian (~60 Myr), pre-specified foreground branch	codeml branch-site mod A AND HyPhy aBSREL on foreground	Mutual validation; mod A LRT df=1 + aBSREL adaptive site classes
Single gene, deep eukaryote (~500+ Myr), no foreground hypothesis	GARD pre-screen -> BUSTED-MH gene-wide -> MEME for sites	Episodic-selection-only methods; saturation-aware (HyPhy under MG94 codon model)
Genome-wide scan, vertebrates	codeml M7 vs M8 OR HyPhy FUBAR-MH per gene; FDR-correct	Pervasive-selection sites; multi-hit correction critical at scale
Episodic selection scan	HyPhy MEME genome-wide (per gene); FDR-correct	Site-level episodic detection
Branch-specific selection on unspecified branches	HyPhy aBSREL	Adaptive per-branch test with built-in multiple-testing
Comparing selection regimes between two phenotypes	HyPhy BUSTED-PH or RELAX	Phenotype-specific or relaxation-detection
Recently diverged species (low divergence)	MK / asymptotic alpha (population genetics)	Codon dN/dS unreliable at low divergence; SFS-based instead
Within-species, dense polymorphism + divergence	polyDFE / GRAPES / asymptotic-MK	Full DFE + alpha jointly; preferred for adaptive-substitution rate
Coding selection genome-wide, with SFS available	grapes -m AUTO_ALL	Demography-aware alpha; standard population-genetics-aware adaptive-substitution scan
Noncoding accelerated evolution (CNEs / ECRs)	PhyloAcc, phyloP-acc	Codon-based unsuitable; PhyloAcc Bayesian convergence
Convergent substitutions across independent lineages	CSUBST	Combinatorial-substitution omegaC; null-corrected
Trait-correlated rate shifts genome-wide	RERconverge	Categorical / binary phenotype; correlates RERs across thousands of genes
Suspected positive selection but dS > 2	Use protein-level method or reduce taxon sampling	Codon-based methods unreliable at saturation; protein-only ASR can still work
Recombination expected (immune genes, viral genomes)	GARD pre-screen mandatory	Recombination + tree-based selection -> false positives (Anisimova 2003)

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 Evol Biol 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

Quantity	Threshold	Source / Rationale
dN/dS interpretation	omega < 1 purifying; omega = 1 neutral; omega > 1 positive (per site, branch, or gene depending on model)	Yang & Bielawski 2000 TREE 15:496; foundational
Branch-site test LRT critical value	2.71 at p=0.05 (50:50 mixture chi^2)	Self-Liang 1987 JASA 82:605; Zhang 2005 MBE 22:2472
Site-level p-value default	p <= 0.1 (FEL, MEME, FUBAR); FUBAR posterior >= 0.9	Murrell 2012/2013; Datamonkey conventions
BEB posterior probability	>= 0.95 significant; >= 0.99 highly significant	Yang & Bielawski 2000
dS upper limit for reliability	dS < 1.5 per branch; dS < 3 overall	Yang 2007 PAML manual
Minimum sequences for codeml	>= 8 with sufficient divergence	Anisimova et al 2001 MBE 18:1585
Branch-site test minimum lineages	>= 20 in tree; >= 4 background branches	Yang 2007
GARD breakpoint significance	p < 0.05 to partition alignment	Pond 2006; mandatory pre-screen
MK alpha threshold	alpha > 0 indicates adaptive substitutions; report 95% CI	Smith & Eyre-Walker 2002
Asymptotic alpha minimum SFS density	>= 50 sites per frequency bin	Messer & Petrov 2013
FDR genome-wide selection scan	q < 0.05 Benjamini-Hochberg	Standard
MEME minimum site-level support	p < 0.1; +/-3 sequences with substitutions	Murrell 2012
aBSREL p-value	p < 0.05 (corrected by Holm-Bonferroni internally)	Smith 2015
RELAX k interpretation	k < 1 relaxed; k > 1 intensified	Wertheim 2015
Codon usage bias ENC	ENC < 35 high bias; consider effect on dS	Wright 1990 Gene 87:23
W->S substitution ratio for gBGC	> 1.5 suggests gBGC	Operational convention
BUSTED-MH multi-hit threshold	omega_DH > 1 indicates multi-hit pattern	Lucaci 2023
HyPhy SRV (Synonymous Rate Variation)	Use BUSTED-S when dS varies across sites > 2x	Wisotsky 2020

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
python3 -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

Pattern	Likely cause	Action
codeml M8 significant, BUSTED null	M8 vs M7 inflated by relaxed constraint mimicking selection	Trust BUSTED; check M8a vs M8 instead (stricter null)
codeml branch-site significant, aBSREL null	Branch-site test foreground post hoc	aBSREL with built-in multiple-testing is correct; downgrade claim
BUSTED significant, MEME no sites	Episodic at sites BUSTED can't pinpoint; or basic BUSTED detected SRV not selection	Run BUSTED-S; if signal vanishes, was SRV; if persists, gene-wide episodic
MEME positive, FEL null	Episodic selection (MEME-specific)	Trust MEME for episodic; FEL only detects pervasive
Multiple tests positive at same site	High-confidence site under selection	Report; consider experimental validation
Test positive but PREQUAL flagged 20% of alignment	Alignment artifact	Re-filter (HmmCleaner); re-test; downgrade if positive site is in filtered region
Test positive but in high-recombination region	gBGC	W->S substitution test; if gBGC-attributable, downgrade
BUSTED-MH null where BUSTED significant	Multi-hit substitutions misattributed	Trust BUSTED-MH; original positive was multi-hit pattern
RELAX k > 1 with branch-site test null	Selection regime intensification (more purifying)	RELAX captures regime shift; branch-site missed because foreground different
Branch-site significant on Drosophila branch but no signal in mammals	Lineage-specific adaptation; or dS saturation in mammals	Inspect dS distribution; if mammals dS < 0.5 across branch, signal is real; if dS > 2, saturation explanation
asymptotic alpha < 0	DFE has high deleterious load; or demographic violation	Check polyDFE / GRAPES with demographic correction

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

Pushback	Standard response
"GARD pre-screen?"	Yes; no breakpoints (or partitioned at p < 0.05 breakpoints); per-segment results consistent
"Alignment filtering?"	PRANK codon-aware MSA; PREQUAL segment filter applied; Guidance2 scores reported
"Saturation?"	dS distribution shown; max per-branch dS < 1.5; analysis restricted to subclades meeting this
"Branch-site test foreground post hoc?"	Foreground pre-registered OR exploratory analysis acknowledged + aBSREL with built-in multiple-testing used
"Multiple-testing correction?"	FDR (Benjamini-Hochberg) across genes; per-site within gene not corrected (dependence)
"gBGC?"	W->S substitution ratio not elevated; non-sub-telomeric; BUSTED-MH null in candidates rules out
"Multi-hit?"	BUSTED-MH used; if signal persists, robust to multi-hit confounder
"Why this LRT df?"	Branch-site test uses 50:50 mixture (Self-Liang 1987; Zhang 2005); critical value 2.71 at p=0.05
"Sensitivity to model choice?"	Cross-validated PAML vs HyPhy; consistent across both; reported both p-values

Common Errors

Error / symptom	Cause	Solution
codeml runs but rst file empty	RateAncestor = 0 or path not writable	Set RateAncestor = 1; check output directory
codeml omega2 = 999	Numerical pathology / saturated dS	Vary starting omega; reduce taxon sampling to dS < 2
codeml LRT negative (-0.001)	Numerical noise at convergence	Round; treat as no signal; rerun with different starting values
HyPhy "tree branches don't match alignment"	Mismatched taxa names	Use exact same labels in tree and alignment
HyPhy MEME returns "no sites significant"	High alignment uncertainty; or no episodic selection	Re-filter alignment; try BUSTED-S for gene-wide signal
GARD takes forever	> 50 sequences	Reduce to representative subset; or use RDP5 for viral data
MK alpha negative	Demographic issue or DFE has many slightly deleterious	Use polyDFE / GRAPES with demography correction
RERconverge "too few species per gene"	Stringent default	Reduce min.sp = 5; document
CSUBST omega_C unstable	Few combinations; small clade	Need >= 5 clades for stable convergence estimate
PhyloAcc convergence failure	Insufficient lineages	Re-run with relaxed prior; check input MAF distribution

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 MBE 20:18 (M8a null); Bielawski JP & Yang Z 2004 J Mol Evol 59:121 (clade models)
• Anisimova M & Yang Z 2007 MBE 24:1219 (multiple-testing / branch-site power); 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 MBE 40:msad150 (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 Evol Biol 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