CoChat
Sign Up
Cited by 79Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome‐wide association studies

Open access
Lee, James J., McGue, Matt, Iacono, William G., Chow, Carson C.·2018·Genetic Epidemiology
DOI 10.1002/gepi.22161PMC PMC6758917

Published version of a preprint

Open this paper in CoChat — free account

Abstract

To infer that a single‐nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP‐phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome‐wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/gepi.22161

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Beyond uncorrected-for population stratification, other phenomena may lead to LDSC intercepts above one, including violation of LDSC assumptions about genetic architecture, differences in LD between the reference and meta-analysis samples, and cryptic relatedness.

    “Beyond uncorrected-for population stratification, other phenomena may lead to LDSC intercepts above one, including: violation of LDSC assumptions about genetic architecture48, differences in LD between the reference and meta-analysis samples, and cryptic relatedness9.”

    From Family-GWAS reveals effects of environment and mating on genetic associations (2024) · cites this paper as [48]

    The Results section (page unavailable) reports that relatedness between sample members raised the LDSC intercept, and that a simulated violation of regression linearity could produce an intercept above one without confounding. The passages do not establish that differences in LD between reference and meta-analysis samples raise the intercept; searches for that specific mechanism found no supporting passage. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 47 · Cited by 79

Research works printed but not linked: 42

  1. Barban N, Jansen R, deVlaming R, Vaez A, Mandemakers J, Tropf FC, & Mills MC (2016). Genome-wide analysis identifies 12 loci inuencing human reproductive behavior. Nature Genetics, 48, 1462– 1472. 10.1038/ng.3698 27798627 PMC5695684 · DOI
  2. Bates TC, Maher BS, Medland SE, McAloney K, Wright MJ, Hansell NK, & Gillespie NA (2018). The nature of nurture: Using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Research and Human Genetics, 21, 73– 83. 10.1017/thg.2018.11 29530109 · DOI
  3. Beauchamp JP, Cesarini D, Johannesson M, Lindqvist E, & Apicella C (2011). On the sources of the height-intelligence correlation: New insights from a bivariate ACE model with assortative mating. Behavior Genetics, 41(2), 242– 252. 10.1007/s10519-010-9376-7 20603722 PMC3044837 · DOI
  4. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, & Neale BM (2015). An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11), 1236– 1241. 10.1038/ng.3406 26414676 PMC4797329 · DOI · Paper page
  5. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, & Lee JJ (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience, 4, 7 10.1186/s13742-015-0047-8 25722852 PMC4342193 · DOI · Paper page
  6. Chen G-B (2016). On the reconciliation of missing heritability for genome-wide association studies. European Journal of Human Genetics, 24(12), 1810– 1816. 10.1038/ejhg.2016.89 27436266 PMC5117938 · DOI
  7. Clark G (2014). The son also rises: Surnames and the history of social inequality. Princeton, NJ: Princeton University Press.
  8. de los Campos G, Sorensen D, & Gianola D (2015). Genomic heritability: What is it? PLOS Genetics, 11(5), e1005048. 10.1371/journal.pgen.1005048 PMC4420472 25942577 · DOI
  9. de Vlaming R, Johannesson M, Magnusson PKE, Ikram MA, & Visscher PM (2017a). Equivalence of LD-score regression and individual-level-data methods. bioRxiv, 10.1101/2111821 · DOI
  10. de Vlaming R, Okbay A, Rietveld CA, Johannesson M, Magnusson PKE, Uitterlinden AG, & Koellinger PD (2017b). Meta-GWAS accuracy and power (MetaGAP)calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLOS Genetics, 13(1), e1006495. 10.1371/journal.pgen.1006495 PMC5240919 28095416 · DOI · Paper page
  11. Der G, Batty GD, & Deary IJ (2006). Effect of breast feeding on intelligence in children: Prospective study, sibling pairs analysis, and meta-analysis. British Medical Journal, 333(7575), 945 10.1136/bmj.38978.699583.55 17020911 PMC1633819 · DOI
  12. Duffy DL, & Martin NG (1994). Inferring the direction of causation in cross-sectional twin data: Theoretical and empirical considerations. Genetic Epidemiology, 11(6), 483– 502. 10.1002/gepi.1370110606 7713391 · DOI
  13. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, & Price AL (2015). Partitioning heritability by functional annotation using genomewide association summary statistics. Nature Genetics, 47(11), 1228– 1235. 10.1038/ng.3404 26414678 PMC4626285 · DOI · Paper page
  14. Fisher RA (1941). Average excess and average effect of a gene substitution. Annals of Eugenics, 11(1), 53– 63.
  15. Freedman D (1999). From association to causation: Some remarks on the history of statistics. Statistical Science, 14(3), 243– 258.
  16. Gazal S, Finucane HK, Furlotte NA, Loh P-R, Palamara PF, Liu X, & Price AL (2017). Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nature Genetics, 49(10), 1421– 1427. 10.1038/ng.3954 28892061 PMC6133304 · DOI · Paper page
  17. Goldstein DB (2011). The importance of synthetic associations will only be resolved empirically. PLOS Biology, 9(1), e1001008. PMC3022532 21267066
  18. Hadfield JD (2012). The quantitative genetic theory of parental effects In Royle NJ, Smiseth PT, & Kolliker M (Eds.), The evolution of parental care (pp. 267– 284). Oxford, UK: Oxford University Press.
  19. Hill WD, Hagenaars SP, Marioni RE, Harris SE, Liewald DCM, Davies G, & Deary IJ (2016). Molecular genetic contributions to social deprivation and household income in UK Biobank. Current Biology, 26(22), 3083– 3089. 10.1016/j.cub.2016.09.035 27818178 PMC5130721 · DOI
  20. Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI, Thorgeirsson TE, & Stefansson K (2018). The nature of nurture: Effects of parental genotypes. Science, 359(6374), 424– 428. 10.1126/science.aan6877 29371463 · DOI · Paper page
  21. Laird NM, & Lange C (2006). Family-based designs in the age of large-scale geneassociation studies. Nature Reviews Genetics, 7(5), 385– 394. 10.1038/nrg1839 16619052 · DOI
  22. Lee JJ (2012). Correlation and causation in the study of personality (with discussion). European Journal of Personality, 26(4), 372– 412. 10.1002/per.1863 · DOI
  23. Lee JJ, & Chow CC (2013). The causal meaning of Fisher’s average effect. Genetics Research, 95(2–3), 89– 109. 10.1017/S0016672313000074 23938113 PMC3779663 · DOI
  24. Lee JJ, & Chow CC (2014). Conditions for the validity of SNP- based heritability estimation. Human Genetics, 133(8), 1011– 1022. 10.1007/s00439-014-1441-5 24744256 PMC4159725 · DOI
  25. Lee JJ, Vattikuti S, & Chow CC (2016). Uncovering the genetic architectures of quantitative traits. Computational and Structural Biotechnology Journal, 14, 28– 34. 10.1016/j.csbj.2015.10.002 27076877 PMC4816193 · DOI
  26. Loh P-R, Kichaev G, Gazal S, Schoech AP, & Price AL (2018). Mixed-model association for biobank-scale datasets. Nature Genetics, 50(7), 906– 908. 10.1038/s41588-018-0144-6 29892013 PMC6309610 · DOI · Paper page
  27. Lundberg F, Cnattingius S, D’Onofrio BM, Altman D, Lambe M, Hultman C, & Iliadou A (2010). Maternal smoking during pregnancy and intellectual performance in young adult Swedish male offspring. Paediatric and Perinatal Epidemiology, 24(1), 79– 87. 10.1111/ppe.2010.24.issue-1 20078833 PMC3653250 · DOI
  28. Marceau K, McMaster MTB, Smith TF, Daams JG, van Beijsterveldt CEM, Boomsma DI, & Knopik VS (2016). The prenatal environment in twin studies: A review on chorionicity. Behavior Genetics, 46(3), 286– 303. 10.1007/s10519-016-9782-6 26944881 PMC4858569 · DOI
  29. Marioni RE, Davies G, Hayward C, Liewald D, Kerr SM, Campbell A, & Deary IJ (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44, 26– 32. 10.1016/j.intell.2014.02.006 24944428 PMC4051988 · DOI
  30. Miller MB, Basu S, Cunningham J, Eskin E, Malone SM, Oetting WS, & McGue M (2012). The Minnesota Center for Twin and Family Research genomewide association study. Twin Research and Human Genetics, 15(06), 767– 774. 10.1017/thg.2012.62 23363460 PMC3561927 · DOI
  31. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, & Benjamin DJ (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539– 542. 10.1038/nature17671 27225129 PMC4883595 · DOI · Paper page
  32. Palla L, & Dudbridge F (2015). A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. American Journal of Human Genetics, 97(2), 250– 259. 10.1016/j.ajhg.2015.06.005 26189816 PMC4573448 · DOI
  33. Pearl J (2009). Causality: Models, reasoning, and inference (2nd ed.). New York: Cambridge University Press.
  34. Sacerdote B (2007). How large are the effects from changes in family environment? A study of Korean American adoptees. Quarterly Journal of Economics, 122(1), 119– 157. 10.1162/qjec.122.1.119 · DOI
  35. Shi H, Mancuso N, Spendlove S, & Pasaniuc B (2017). Local genetic correlation gives insights into the shared genetic architecture of complex traits. American Journal of Human Genetics, 101(5), 737– 751. 10.1016/j.ajhg.2017.09.022 29100087 PMC5673668 · DOI
  36. Skoglund C, Chen Q, D’Onofrio BM, Lichtenstein P, & Larsson H (2014). Familial confounding of the association between maternal smoking during pregnancy and ADHD in offspring. Journal of Child Psychology and Psychiatry, 55(1), 61– 68. 10.1111/jcpp.12124 25359172 PMC4217138 · DOI · Paper page
  37. Speed D, Hemani G, Johnson MR, & Balding DJ (2012). Improved heritability estimation from genome-wide SNPs. American Journal of Human Genetics, 91(6), 1011– 1021. 10.1016/j.ajhg.2012.10.010 23217325 PMC3516604 · DOI
  38. van Beijsterveldt CEM, Overbeek LIH, Rozendaal L, McMaster MTB, Glasner TJ, Bartels M, & Boomsma DI (2016). Chorionicity and heritability estimates from twin studies: The prenatal environment of twins and their resemblance across a large number of traits. Behavior Genetics, 46(3), 304– 314. 10.1007/s10519-015-9745-3 26410687 PMC4858554 · DOI
  39. Vattikuti S, Guo J, & Chow CC (2012). Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLOS Genetics, 8(3), e1002637. 10.1371/journal.pgen.1002637 PMC3315484 22479213 · DOI
  40. Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, & Visscher PM (2015). Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nature Genetics, 47(10), 1114– 1120. 10.1038/ng.3390 26323059 PMC4589513 · DOI · Paper page
  41. Yengo L, Yang J, & Visscher PM (2018). Expectation of the intercept from bivariate LD score regression in the presence of population stratification. bioRxiv, 10.1101/310565 · DOI
  42. Zaitlen NA, Kraft P, Patterson N, Pasaniuc B, Bhatia G, Pollack S, & Price AL (2013). Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLOS Genetics, 9(5), e1003520. 10.1371/journal.pgen.1003520 PMC3667752 23737753 · DOI · Paper page

Authors
Lee, J. J., McGue, M., Iacono, W. G., & Chow, C. C.
Affiliations
  • University of Minnesota (US)
  • National Institutes of Health (US)
Corresponding author
Lee, James J.
ORCID
Venue
Genetic Epidemiology
Publisher
Wiley
Published
2018-09-24
Volume
42
Issue
8
Pages
783-795
ISSN
  • 0741-0395 (ISSN-L)
  • 1098-2272
DOI
10.1002/gepi.22161
PMC
PMC6758917
OpenAlex
W2892561411
Cited by
79
References
47
Citations by year
2026: 5 · 2025: 10 · 2024: 17 · 2023: 11 · 2022: 18 · 2021: 4 · 2020: 8 · 2019: 5 · 2018: 1
FWCI
2.76
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Genetic Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute on Drug Abuse (DA013240, DA05147)
  • National Institute of Mental Health (MH066140)
  • National Institute on Alcohol Abuse and Alcoholism (AA09367, AA11886)
  • National Institute of Diabetes and Digestive and Kidney Diseases
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
LD score regression, confounding, genetic correlation, genome-wide association study, linkage disequilibrium, unbiasedness, false positives, genetic covariance, parent-offspring transmission, environmental confounding
MeSH terms
Computer Simulation, Genotype, Humans, Models, Genetic, Phenotype, Regression Analysis, Twins, Linkage Disequilibrium, Confounding Factors, Epidemiologic, Polymorphism, Single Nucleotide, Inheritance Patterns, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review