Bio population genetics scikit allel analysis skills scikit allel analysis

Version Compatibility

Reference examples tested with: scikit-allel 1.3.13+, numpy 1.26+, zarr 2.18+.

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

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Version traps that change results, not just syntax: scikit-allel is in MAINTENANCE mode (latest line v1.3.x, e.g. v1.3.13 Sep 2024); the README names sgkit (xarray+dask) as the successor but states it is "not yet at feature parity", so scikit-allel remains the pragmatic choice for established stat workflows. average_patterson_f3/average_patterson_d are the current names; pre-2020 code used blockwise_patterson_* (gone). to_n_alt() default is fill=0, not fill=-1. read_vcf is eager and loads the whole file into RAM. The single source of truth for versions is this block, not headings.

scikit-allel Analysis

"Analyze population genetics in Python" -> Read a VCF into array structures, then compute frequency-, diversity-, differentiation-, and haplotype-based statistics with correct denominators.

• Python: allel.GenotypeArray, allel.AlleleCountsArray, allel.windowed_diversity(..., is_accessible=), allel.average_hudson_fst, allel.pca

Scope: in-memory and dask/zarr scikit-allel analysis - the array-API mechanics of the data model, diversity/SFS, FST and f/D admixture statistics, LD pruning, PCA, and selection-scan computation. PLINK-format QC routes to plink-basics; PCA/ADMIXTURE via CLI tools to population-structure; selection-scan DESIGN (standardization, outlier calling, demographic confounding) to selection-statistics; phased input to phasing-imputation/haplotype-phasing; VCF generation to variant-calling/vcf-basics.

The Single Most Important Insight -- one denominator bug, two faces

1. Almost every statistic scikit-allel computes is a RATIO or a DENSITY, and the two ways the denominator is silently wrong both return a finite, plausible, unflagged number.
2. Face (a) - the per-base denominator: pi, Watterson's theta, and Dxy divide a segregating-site sum by the number of bases; omit is_accessible= and they divide by total span (stop-start+1) instead of callable bp, deflating values 2-5x AND distorting the genome-wide landscape because callability varies per window.
3. Face (b) - FST is a ratio of variance components: the correct multi-locus estimate is sum(a)/(sum(a)+sum(b)+sum(c)), NOT mean(per_snp_fst) (which is rare-variant-dominated and biased, Bhatia 2013); scikit-allel returns the (a,b,c) components from weir_cockerham_fst and (num,den) from hudson_fst/patterson_fst precisely to force ratio-of-sums.
4. Both bugs run silently with no exception or warning, so correctness is a design decision the analyst makes, not something the library enforces.

Tool Taxonomy -- the data model and the scaling path

Object / path	Shape / form	Role	When
GenotypeArray	(n_variants, n_samples, ploidy) int8, -1 = missing	the fundamental call array	diploid genotypes from calldata/GT
HaplotypeArray	(n_variants, n_haplotypes)	phased chromosomes	iHS/XP-EHH/nSL/Garud H (REQUIRE phasing)
AlleleCountsArray	(n_variants, n_alleles) int32	currency of all frequency stats	gt.count_alleles(); ignores -1
to_n_alt 012 matrix	(n_variants, n_samples)	input to PCA/LD	gt.to_n_alt(fill=...) (default fill=0 imputes to ref)
in-memory numpy	dense, all in RAM	fast, simple	fits-in-memory regions/chromosomes
GenotypeDaskArray + zarr	chunked, on-disk, lazy	out-of-core / parallel	biobank-scale; vcf_to_zarr once then dask
scikit-allel	maintenance mode, v1.3.x	established stat workflows	the pragmatic default today
sgkit	xarray+dask, active	successor, NOT yet feature-parity	greenfield biobank-scale infrastructure

Decision Tree by Scenario

Scenario	Use	Why
Per-base pi/theta/Dxy	windowed_diversity(..., is_accessible=mask)	without the mask the per-base denominator is total span, not callable bp
Genome-wide FST point estimate + SE	average_hudson_fst(ac1, ac2, blen)	ratio-of-sums + block-jackknife done correctly; Hudson is robust to unequal n (Bhatia 2013)
FST landscape across the genome	moving_hudson_fst / windowed_weir_cockerham_fst	per-window ratio aggregation, not mean(per_snp_fst)
SFS without a confident ancestral allele	sfs_folded(ac)	folds on minor-allele count; sfs() is unfolded and treats ALT as derived
Test if pop C is admixed	average_patterson_f3(acc, aca, acb, blen)	significantly negative f3 (z < ~-3) is the formal admixture test; C goes FIRST
LD-prune before PCA	locate_unlinked(gn) iterated ~3 rounds	one pass leaves residual LD; PCs otherwise track LD blocks/inversions
Selection scan on phased data	ihs/nsl then standardize_by_allele_count (DAF bins); xpehh then genome-wide standardize	raw scores are uninterpretable; the standardization differs by statistic
Whole-genome callset (tens of M SNPs)	vcf_to_zarr + GenotypeDaskArray	read_vcf is eager and OOMs; dask materializes per chunk

Reading Genotypes and Counting Alleles

Goal: Load a VCF region into a GenotypeArray and derive the allele-count currency, missing-aware.

Approach: Read only the needed fields (read_vcf is eager), wrap GT, count alleles per site (missing ignored), and get per-population counts in one pass with count_alleles_subpops.

import allel
import numpy as np

callset = allel.read_vcf('data.vcf.gz', fields=['samples', 'calldata/GT', 'variants/POS', 'variants/CHROM'], region='2L:1-5000000')
gt = allel.GenotypeArray(callset['calldata/GT'])   # (n_variants, n_samples, 2); -1 = missing
pos = callset['variants/POS']

ac = gt.count_alleles()                            # ignores -1, so per-site allele number varies
subpops = {'pop1': [0, 1, 2, 3, 4], 'pop2': [5, 6, 7, 8, 9]}
ac_subpops = gt.count_alleles_subpops(subpops)     # one pass, consistent variant axis
ac1, ac2 = ac_subpops['pop1'], ac_subpops['pop2']

Per-base Diversity with the Accessibility Mask

Goal: Compute pi and Watterson's theta as honest per-base quantities, not span-deflated ones.

Approach: Pass a boolean callability mask (one entry per base, from coverage/mappability, NOT from variant positions) as is_accessible; inspect the returned n_bases per window to confirm the denominator.

# is_accessible: bool array over genomic positions (a callable-loci mask), NOT the VCF variant sites.
pi = allel.sequence_diversity(pos, ac, is_accessible=is_accessible)
theta_w = allel.watterson_theta(pos, ac, is_accessible=is_accessible)

# windowed_diversity returns 4 values; n_bases is the accessible-bp denominator PER window.
pi_w, windows, n_bases, counts = allel.windowed_diversity(pos, ac, size=100000, is_accessible=is_accessible)
# windowed_tajima_d returns 3 values (no n_bases): Tajima's D is dimensionless, no is_accessible.
D, td_windows, td_counts = allel.windowed_tajima_d(pos, ac, size=100000)

FST as a Ratio of Sums

Goal: Get a genome-wide FST point estimate with a jackknife SE, and a per-window landscape, without the mean-of-ratios bias.

Approach: Let average_hudson_fst do the ratio-of-sums plus block-jackknife; if hand-aggregating, sum the components THEN divide; size blocks (blen) to exceed the LD scale.

# Genome-wide estimate + standard error (ratio-of-sums + delete-one-block jackknife):
fst, se, vb, vj = allel.average_hudson_fst(ac1, ac2, blen=2000)   # blen must exceed the LD decay length

# Hand-aggregating Hudson correctly (NEVER mean of per-SNP fst):
num, den = allel.hudson_fst(ac1, ac2)
fst_manual = np.sum(num) / np.sum(den)

# Weir-Cockerham returns per-allele components (a, b, c); aggregate over BOTH axes:
a, b, c = allel.weir_cockerham_fst(gt, subpops=[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
fst_wc = np.sum(a) / (np.sum(a) + np.sum(b) + np.sum(c))

# Landscape: per-window FST is already ratio-aggregated within each window.
fst_windows = allel.moving_hudson_fst(ac1, ac2, size=1000)

Admixture: f3 and D (block-jackknifed)

Goal: Formally test whether a population is admixed (f3) or whether gene flow violates a tree (D / ABBA-BABA).

Approach: Call the average_* form for the jackknife z-score; put the test population FIRST in f3; treat a significantly negative f3 (z < ~-3) as admixture and |z| > ~3 for D as treeness violation.

# f3(C; A, B): TEST population C is the FIRST argument. Returns (f3, se, z, vb, vj).
f3, se3, z3, vb3, vj3 = allel.average_patterson_f3(acc, aca, acb, blen=2000)
# Significantly negative f3 (z3 < ~-3) => C is admixed between A and B.

# D-statistic (ABBA-BABA): returns (d, se, z, vb, vj). |z| > ~3 flags gene flow.
d, sed, zd, vbd, vjd = allel.average_patterson_d(aca, acb, acc, acd, blen=2000)

LD Pruning and PCA

Goal: Project samples onto ancestry axes that reflect drift, not LD blocks or inversions.

Approach: Convert to a missing-free 012 matrix, LD-prune iteratively with locate_unlinked, mask known inversions by position, then run Patterson-scaled PCA (randomized at scale).

gn = gt.to_n_alt(fill=-1)                          # default fill=0 imputes missing to REF; use -1 then handle
gn = np.where(gn < 0, 0, gn)                        # impute-to-reference is a deliberate choice here

# Iterate LD pruning ~3 rounds; one pass leaves residual LD. Returns a KEEP mask (True = unlinked).
for _ in range(3):
    keep = allel.locate_unlinked(gn, size=100, step=20, threshold=0.1)
    gn = gn[keep]

coords, model = allel.randomized_pca(gn, n_components=10, scaler='patterson', random_state=0)
explained = model.explained_variance_ratio_        # scree; coords is (n_samples, n_components)

Selection Scans on Phased Haplotypes

Goal: Score the genome for recent selection from haplotype structure.

Approach: Reshape phased genotypes to a HaplotypeArray, compute the raw scan, then standardize - iHS/nSL binned by derived-allele frequency (standardize_by_allele_count), XP-EHH genome-wide (standardize); the raw scores are not directly interpretable.

h = gt.to_haplotypes()                              # VALID only if data are PHASED
ihs_raw = allel.ihs(h, pos, min_maf=0.05)           # unstandardized
ihs_std, bins = allel.standardize_by_allele_count(ihs_raw, ac[:, 1])   # bin by DERIVED count; ac[:,1] is derived only if REF is ancestral (polarize first); |z| > 2 flags candidates
h1, h12, h123, h2_h1 = allel.garud_h(h)             # soft-vs-hard-sweep haplotype-homozygosity stats

Per-Function Failure Modes

sequence_diversity / watterson_theta without is_accessible

Trigger: calling per-base diversity with no callability mask. Mechanism: divides the numerator by stop-start+1 (total span) instead of callable bp. Symptom: pi/theta deflated 2-5x and the genome-wide landscape distorted because callability varies per window. Fix: pass is_accessible= from a coverage/mappability callable-loci mask and inspect the returned n_bases.

mean(per_snp_fst) instead of ratio-of-sums

Trigger: averaging per-SNP a/(a+b+c) for the genome-wide FST. Mechanism: mean-of-ratios is dominated by low-frequency SNPs with tiny noisy denominators. Symptom: a biased FST that differs from published estimates of the same comparison (Bhatia 2013). Fix: sum(a)/(sum(a)+sum(b)+sum(c)), or average_hudson_fst/average_weir_cockerham_fst which do it plus a jackknife SE.

to_n_alt default fill=0

Trigger: gt.to_n_alt() with no fill. Mechanism: missing calls become 0 alt alleles = homozygous reference. Symptom: PCA/LD silently biased toward the reference allele. Fix: to_n_alt(fill=-1) then impute deliberately, or pre-filter for high call rate; state the imputation choice.

sfs() fed alt counts and run unfolded

Trigger: allel.sfs(ac[:, 1]) without a confident ancestral allele. Mechanism: sfs() is unfolded and treats the ALT count as the DERIVED count; ALT != DERIVED. Symptom: mis-polarized spectrum biasing demographic/DFE inference. Fix: use sfs_folded(ac) when polarization is uncertain; use sfs(dac) only with a verified ancestral allele.

iHS/XP-EHH/nSL on unphased or unstandardized data

Trigger: running selection scans on unphased genotypes or reporting raw scores. Mechanism: these stats need phased haplotype structure, and raw output is on an unstandardized scale. Symptom: meaningless scans; un-binned scores not comparable across the genome. Fix: require PHASED input and standardize - standardize_by_allele_count (DAF bins) for iHS/nSL, genome-wide standardize for XP-EHH.

blen smaller than the LD scale

Trigger: a small blen in any average_* FST or average_patterson_f3/_d. Mechanism: blocks within an LD region are correlated, so the delete-one-block jackknife under-estimates the SE. Symptom: spurious-significant f3 admixture / D-statistics. Fix: size blen to exceed the LD decay length (multi-Mb / >~1 cM for humans).

read_vcf on a whole-genome callset

Trigger: allel.read_vcf('genome.vcf.gz') with no region/fields limits. Mechanism: read_vcf is eager and materializes the entire file in RAM. Symptom: out-of-memory crash on biobank-scale data. Fix: vcf_to_zarr once, then GenotypeDaskArray for out-of-core counting/filtering; limit read_vcf(fields=, region=).

Quantitative / correctness notes

Item	Value / rule	Rationale
Accessibility deflation	multiplicative AND per-window	a 40%-accessible window deflates pi ~2.5x, a 90% one ~1.1x - the relative landscape is wrong
FST estimator default	Hudson for unequal n / rare variants	Bhatia 2013 recommends the ratio estimator robust to sample-size imbalance
Jackknife block size	blen > LD decay length	too-small blocks are correlated -> anticonservative SE -> false significance
f3 admixture	z < ~-3 (negative)	a significantly negative f3(C; A, B) is the formal admixture test for C
D / ABBA-BABA	|z| > ~3	conventional treeness-violation / gene-flow threshold
iHS/XP-EHH/nSL	standardized |z| > 2, in CLUSTERS	sweeps show clusters of extreme binned z-scores, not isolated SNPs
LD pruning rounds	~3 iterations of locate_unlinked	one pass leaves residual LD; expect to discard most SNPs
PCA scaler	'patterson' (default)	centers then divides each SNP by sqrt(p(1-p)); equal expected variance under drift

Thresholds are conventions, not laws - inspect distributions and verify current best practice before applying numbers blindly.

Common Errors

Error / symptom	Cause	Solution
pi/theta look 2-5x too small	is_accessible= omitted	pass a callable-loci mask; check the returned n_bases
FST disagrees with published value	mean(per_snp_fst) aggregation	sum(num)/sum(den) or average_hudson_fst(ac1, ac2, blen)
hudson_fst/patterson_fst "FST" out of range	treating the first return as FST	they return (num, den); aggregate np.sum(num)/np.sum(den)
ValueError unpacking windowed_tajima_d	expecting 4 values	it returns 3 (D, windows, counts); windowed_diversity returns 4
PCA skewed toward reference allele	to_n_alt() default fill=0	to_n_alt(fill=-1) then impute deliberately, or pre-filter
pca raises on -1/NaN	missing values in the 012 matrix	impute or filter; the patterson scaler cannot handle missing
f3 admixture test makes no sense	wrong argument order	average_patterson_f3(acc, aca, acb, blen) - test pop C is FIRST
blockwise_patterson_f3 AttributeError	old name	use average_patterson_f3 / average_patterson_d
raw iHS values uninterpretable	not standardized	standardize_by_allele_count(score, aac) binned by DAF
MemoryError on read_vcf	eager whole-genome read	vcf_to_zarr + GenotypeDaskArray; limit fields=/region=

References

1. Miles A, Harding N, et al. scikit-allel: explore and analyse genetic variation. Zenodo; cite-all-versions DOI:10.5281/zenodo.597309. Docs: scikit-allel.readthedocs.io; successor sgkit: github.com/sgkit-dev/sgkit.
2. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution 1984; 38(6):1358-1370. DOI:10.1111/j.1558-5646.1984.tb05657.x.
3. Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics 1992; 132(2):583-589. PMID:1427045.
4. Bhatia G, Patterson N, Sankararaman S, Price AL. Estimating and interpreting FST: the impact of rare variants. Genome Research 2013; 23(9):1514-1521. PMID:23861382.
5. Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, Genschoreck T, Webster T, Reich D. Ancient admixture in human history. Genetics 2012; 192(3):1065-1093. DOI:10.1534/genetics.112.145037.
6. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genetics 2006; 2(12):e190. DOI:10.1371/journal.pgen.0020190.
7. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 1989; 123(3):585-595. PMID:2513255.
8. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. PLoS Biology 2006; 4(3):e72. DOI:10.1371/journal.pbio.0040072.
9. Sabeti PC, et al. Genome-wide detection and characterization of positive selection in human populations. Nature 2007; 449:913-918. DOI:10.1038/nature06250.
10. Ferrer-Admetlla A, Liang M, Korneliussen T, Nielsen R. On detecting incomplete soft or hard selective sweeps using haplotype structure. Molecular Biology and Evolution 2014; 31(5):1275-1291. DOI:10.1093/molbev/msu077.
11. Garud NR, Messer PW, Buzbas EO, Petrov DA. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genetics 2015; 11(2):e1005004. DOI:10.1371/journal.pgen.1005004.

Related Skills

• selection-statistics - selection-scan design, standardization, and demography-aware outlier interpretation (this skill owns the array mechanics)
• population-structure - PCA and ADMIXTURE via PLINK2/FlashPCA2
• linkage-disequilibrium - LD pruning and clumping
• plink-basics - PLINK-format QC before array-based analysis
• variant-calling/vcf-basics - VCF generation and manipulation before loading
• phasing-imputation/haplotype-phasing - phased haplotypes required for iHS/XP-EHH/nSL