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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
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Abstract
Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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“LD Score regression suggested polygenicity rather than bias from population stratification10,12, as the (default settings) LD Score intercept and attenuation ratio were estimated to be 1.115 (SE = 0.019) and 0.054 (SE = 0.009), respectively.”
From Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. (2021) · cites this paper as [12]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The LD score regression intercept was computed with LDSC v1.0.1 software using the ‘baselineLD’ LD scores built from 1000 Genomes phase 3 (ref. 64).”
From New insights into the genetic etiology of Alzheimer’s disease and related dementias (2022) · cites this paper as [64]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“LD score regression intercepts were calculated for each GWAS using LD score (v.1.0.1)45 and corrected standard errors (SEldsc) were calculated by multiplying the standard error by the square root of the LD score regression intercept in cases where the LD score regression intercept was more than 1.”
From Genomic and transcriptomic analyses of aortic stenosis enhance therapeutic target discovery and disease prediction (2026) · cites this paper as [45]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In deCODE, correction for inflation of test statistics due to relatedness and population stratification was performed using the intercept estimate (1.30) from linkage disequilibrium (LD) score regression21.”
From Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias (2026) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We performed LDSC v1.0.1 (ref. 18) to estimate the SNP-based heritability of EduYears in the TWB, KoGES, EAS and EUR populations14 using population-specific LD scores based on the 1KG Project phase 3 data.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [18]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Linkage disequilibrium (LD) score regression (LDSC) can use GWAS data to estimate SNP heritability, the proportion of phenotypic variation explained by common SNPs20,23.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [20]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A large proportion of samples in the meta-analysis were from UK-based studies such as UK Biobank and Generation Scotland, for which the LD scores, generated using 1000 Genomes project (phase 3) European samples (CEU, TSC, FIN, GBR), have been shown to fit reasonably well20.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [20]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“While modest inflation was observed (lambda=1.12, lambda1000 = 1.006), LD score regression analysis 19 indicates this is arising from polygenicity (> 96%, see Methods) rather than confounding.”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“The degree to which the deviation in the test statistics can be ascribed to cryptic relatedness and population stratification rather than to polygenicity was measured from the intercept in LD score regression 19 (LDSC) as the ratio of (intercept1) and (mean(χ 2 )-1).”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“the genome-wide genetic correlation for EduYears between TWB and KoGES was 0.871 (standard error (s.e.) 0.073) using LDSC18”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“Genome-wide associations in the TWB, KoGES and their meta-analysis were consistent with the highly polygenic architecture of educational attainment and did not indicate inflation due to potential population stratification (linkage disequilibrium score regression (LDSC)18 intercept ranged from 1.029 to 1.046).”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“However, a large number of samples were from Scandinavian populations (HUNT study, FinnTwin), where LD mismatch leading to elevated LDSC intercept/ratios has been previously discussed20.”
From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“The LDSC intercept has been proposed as a measure of confounding, with deviations above one argued to represent the extent of spurious inflation in GWAS test statistics33.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“Inferences drawn from the application of LD score regression (LDSC) to GWAS results appear to be fairly robust to confounding due to population stratification, at least some of which is adjusted out by LDSC5,33.”
From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“According to our LD Score regression13 estimates, only a small share (~5%) of this inflation is attributable to bias (Supplementary Figure 2, Supplementary Table 1).”
From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“a linkage disequilibrium (LD) score intercept 17 of 1.08 (s.e.= 0.02)”
From Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence (2018) · cites this paper as [17]
The cited source says what the claim attributes to it.
In the citing paper
“According to the LD score regression8 intercept (1.66), confounding accounts for 7% of the inflation, similar to previous GWAS of EA2”
From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [8]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Significant genetic correlations between ASD ( N = 46,350) and other traits after Bonferroni correction for testing a total of 234 traits available at LDhub with the addition of a handful of new phenotypes.”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [19]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Due to the epidemiological correlation between AD and cardiometabolic traits, coupled with the fact that many cardiometabolic traits are genetically correlated with one another, additional broader-scale pleiotropic studies are warranted, and recently the field has begun to do so [4, 11].”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [11]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“At the genome-wide level, we first showed that the SNP-based heritability for EduYears was estimated to be 9.7% in TWB, 8.7% in KoGES and 9.0% in the EAS GWAS meta-analysis”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [18]
Experimental — accuracy not yet measuredIn the citing paper
“There was a strong genetic correlation between each of these cohorts. The rG between PGC_139k and 23andMe_307k was 0.85 (s.e = 0.03). The UK Biobank had a rG of 0.87 (s.e = 0.04) with PGC_139k. A similar rG was also found between UK Biobank and 23andMe_307k (0.85, s.e = 0.03).”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [10]
In the citing paper
“The estimate of the SNP-based heritability of depression within our meta-analysis was 0.089 (0.003) on the liability scale using LDSC regression 10.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [10]
In the citing paper
“Linkage Disequilibrium Score (LDSC) regression 10 produced a genomic inflation factor (λGC) estimate of 1.63 with an intercept of 1.015 (0.011) prior to inflation correction”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [10]
In the citing paper
“Linkage disequilibrium score (LDSC) regression intercepts 10 were used for genomic inflation control of the three contributing cohorts and the final meta-analysis results.”
From Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions (2019) · cites this paper as [10]
In the citing paper
“univariate linkage disequilibrium score (LDSC) regression12,13 estimates indicated that the majority of this inflation was due to a polygenic signal, with the intercept being close to 1 (1.026, s.e.m. = 0.006).”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [13]
In the citing paper
“In addition, we performed LDSC regression via LD Hub v.1.9.0 (refs. 12,13) to calculate the LD score regression intercept and derive a heritability estimate for the inverse-variance weighted meta-analysis summary statistics.”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [13]
In the citing paper
“LDSC-SEG analysis60,61 showed that loci associated with human osteocyte transcriptome signature gene orthologs contribute to the heritability of OA at any site (P = 1.6 × 10−7), at the knee (P = 1.9 × 10−6), hip (P = 4.5 × 10−3) and hip and/or knee (P = 3.2 × 10−6).”
From Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease (2021) · cites this paper as [61]
In the citing paper
“Estimation of the LD Score intercepts, using LD score regression26, revealed intercepts near 1.00 (range: 0.98–1.02), indicating that the genomic inflation was due to a polygenic architecture rather than inflation due to uncorrected population structure.”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [26]
In the citing paper
“Genetic correlations32 of trait architectures as measured using LD score regression26 showed strong correlation between men and women; correlations ranged from 0.92 for overall activity to 0.97 for sleep duration (Supplementary Fig. 6).”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [26]
In the citing paper
“Sensitivity analysis using LD score regression26 on the overall activity trait showed we did not need to include principal components of ancestry as covariates in our linear mixed models (lambda = 1.20 vs. 1.20, LD intercept = 1.0085 vs. 1.0092, genome-wide genetic correlation (Rg) = 0.99).”
From GWAS identifies 14 loci for device-measured physical activity and sleep duration (2018) · cites this paper as [26]
In the citing paper
“Second, several methods can calculate the heritability of a trait using SNP array data (Bulik-Sullivan et al., 2015; Yang et al., 2011). These provide assessments of heritability based on genome-wide genotypes, and improves upon traditional heritability measurements given their basis in direct genetic measurements.”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Bulik-Sullivan et al., 2015
In the citing paper
“Some of the genetic correlations from the LD score regression were greater than one; genetic correlation from LDSC does not bound to one,13 and the instances with values higher than one occurred when testing in the same sample with similar phenotype (rg 1.07, SE=0.0343) between MDD and SR-Depression within MVP), or between the somewhat smaller FinnGen sample and the large PGC/UKB broad depression”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [13]
In the citing paper
“Phenotypes with heritability z-scores ≥ 4 were considered suitable for genetic correlation against MDD-META. 13”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [13]
In the citing paper
“SNP heritability calculated using LD Score Regression17 ranged from 4.2% for DrnkWk to 8.0% for CigDay (Figure 3; Supplementary Table 13)”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [17]
In the citing paper
“We did not observe statistically significant genetic correlation between endometriosis and 199 common complex traits, based on LD score regression analysis25 using LD hub26.”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [25]
In the citing paper
“If all genetic variants are valid instruments and the relationships between all variables (genetic variants, exposure and outcome) are linear and homogeneous for all individuals in the population, then we would expect the variant-specific estimates (that is, the ratio estimates based on each variant in turn) to all target the same causal parameter, and for there to be no more heterogeneity between”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [13]
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Genetic Markers of ADHD-Related Variations in Intracranial Volume
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Metadata
- Authors
- Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- The University of Queensland (AU)
- Corresponding author
- Neale, Benjamin M
- ORCID
- Venue
- Nature Genetics
- Published
- 2015-02-02
- Volume
- 47
- Issue
- 3
- Pages
- 291-295
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4495769
- OpenAlex
- W2153860431
- Cited by
- 5,559
- References
- 52
- Citations by year
- 2026: 620 · 2025: 852 · 2024: 805 · 2023: 634 · 2022: 612 · 2021: 558 · 2020: 394 · 2019: 392 · 2018: 312 · 2017: 200 · 2016: 120 · 2015: 50
- FWCI
- 155.57
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- EPubDev Repository (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- GRO (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion)
- RUNE (Research UNE) (submittedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Funders
- National Institutes of Health (5u01mh094432-04)
- Medical Research Council (MR/L023784/2, MR/L010305/1, MR/L023784/1)
- National Institute of Mental Health (U01 MH094432, U01MH094421, R01MH085548, R01 MH094421, R01MH101244)
- National Human Genome Research Institute (R01HG006399, F32HG007805)
- National Cancer Institute (R03CA173785)
- National Center for Research Resources (UL1RR025758)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- LD score regression, polygenicity, confounding, linkage disequilibrium, cryptic relatedness, population stratification, genomic control, summary statistics, polygenic traits
- MeSH terms
- Computer Simulation, Humans, Regression Analysis, Linkage Disequilibrium, Genome, Human, Sample Size, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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