Effect Allele

The effect allele serves as the reference point for the sign of the beta coefficient. A positive beta indicates the effect allele increases the trait value or disease risk, while a negative beta indicates a decreasing effect. This directional assignment is critical: flipping the effect allele to the non-effect allele reverses the sign of the beta, which can cause catastrophic errors in polygenic risk score calculation if not harmonized across datasets.

An individual's risk allele dosage is computed by counting the number of effect allele copies they carry at a given locus:

• 0 copies: Homozygous for the non-effect allele
• 1 copy: Heterozygous
• 2 copies: Homozygous for the effect allele

For imputed genotypes, dosages are continuous values between 0 and 2, representing the expected allele count based on imputation probabilities.

A persistent challenge in cross-study harmonization is strand ambiguity. If a GWAS reports the effect allele on the forward strand and a target dataset is aligned to the reverse strand, the effect allele may appear as its complementary base (A/T or C/G). For palindromic SNPs where the allele and its complement are identical, resolving strand without external reference data is impossible, risking systematic dosage errors.

The effect allele frequency (EAF) varies significantly across ancestral populations. An allele that is common in a European GWAS discovery cohort may be rare or monomorphic in an African or East Asian target population. This frequency divergence directly impacts the variance contributed by the variant and is a primary driver of the portability gap in cross-ancestry polygenic risk score performance.

There is no universal biological rule dictating which allele is designated as the effect allele in GWAS summary statistics. It is often arbitrarily set to the ALT allele in the reference genome or simply the allele coded as '1' in the analysis software. This arbitrary assignment necessitates rigorous harmonization pipelines that match alleles by genomic position and sequence context, not by assumption of functional relevance.

The estimated effect size assigned to the effect allele in the discovery GWAS is subject to winner's curse—a systematic overestimation bias that occurs when selecting variants based on statistical significance thresholds. The effect allele's beta in the discovery dataset is, on average, inflated relative to its true population effect, requiring shrinkage corrections such as empirical Bayes methods in tools like LDpred2 and PRS-CS.

The quantitative count of effect alleles an individual carries at a specific locus, typically encoded as 0, 1, or 2 for diploid genomes. This dosage is multiplied by the variant's beta coefficient from GWAS summary statistics to calculate the individual's weighted genetic burden. In imputed datasets, dosages can be continuous values between 0 and 2, reflecting the imputation uncertainty at untyped variants.

• 0 = homozygous for the non-effect allele
• 1 = heterozygous
• 2 = homozygous for the effect allele
• Continuous dosages account for genotype probability distributions

The estimated log-odds ratio (for binary traits) or linear regression coefficient (for continuous traits) assigned to the effect allele in a GWAS. This value represents both the direction and magnitude of the variant's association with the phenotype. In PRS construction, the beta coefficient serves as the weight multiplied by the individual's effect allele dosage.

• Positive beta: effect allele increases trait value or disease risk
• Negative beta: effect allele is protective
• Winner's curse can inflate betas in discovery GWAS

A critical quality control step ensuring the effect allele reported in GWAS summary statistics aligns with the same DNA strand as the genotype data used for PRS calculation. Mismatches occur because A/T and C/G SNPs are palindromic—the allele on the forward strand is identical to its complement on the reverse strand. Unresolved strand flips cause sign errors in risk allele dosage, systematically reversing the direction of effect.

• Check allele frequencies against reference panels
• Remove ambiguous SNPs without frequency disambiguation
• Align effect alleles to the same strand across all input files

The effect allele is not synonymous with the reference allele defined by a genome build (e.g., GRCh37/hg19). The reference allele is simply the base present in the reference genome at that position, while the effect allele is the specific allele to which the GWAS effect size is anchored. An effect allele can be either the reference or the alternative allele. Confusing these concepts leads to allele misassignment and incorrect PRS calculations.

• Reference allele: genome build convention
• Effect allele: GWAS statistical convention
• Always verify which allele the beta coefficient references

The population frequency of the effect allele critically influences its contribution to a polygenic risk score. Minor allele frequency (MAF) determines statistical power in GWAS and the variant's information content in PRS. Rare effect alleles (MAF < 1%) often have larger effect sizes but contribute to risk in fewer individuals, while common effect alleles (MAF > 5%) typically have small effects but explain more population-level variance.

• Common variants: small effects, high population attributable risk
• Rare variants: larger effects, low population frequency
• MAF thresholds are applied during QC to exclude poorly imputed variants

The computational pipeline that standardizes effect allele definitions across multiple GWAS summary statistics files before meta-analysis or multi-ancestry PRS construction. Harmonization resolves conflicts where different studies report effects relative to different alleles at the same variant. The process involves flipping effect alleles and inverting beta coefficient signs to ensure all effects are anchored to a consistent reference.

• Detect allele mismatches across input files
• Flip effect/non-effect allele assignments
• Negate beta coefficients to preserve direction of effect
• Output harmonized summary statistics with unified allele coding