Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Ni, Guiyan; Moser, G.; Wray, Naomi R.; Lee, Sang

doi:10.1101/194019

Cited by 29 publications

(28 citation statements)

References 38 publications

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“…Recently, a study suggested that rA estimates from LDSC are less accurate than rA obtained by GREML(d), perhaps because of the uncertainty of homogeneity among combined data sets. 31 Still, our twin-SEM and SNP-GREML(d) models give estimates of rA and rE largely in agreement with each other for most correlated cardiometabolic pairs.…”

Section: Discussionsupporting

confidence: 71%

“…However, the estimates of rA from LDSC were >1 for 4 pairs of blood lipids, indicating potential inflation in signal, potentially resulting from using overlapped individuals (Table ) or imputed SNPs to capture the covariation. Recently, a study suggested that rA estimates from LDSC are less accurate than rA obtained by GREML(d), perhaps because of the uncertainty of homogeneity among combined data sets . Still, our twin‐SEM and SNP‐GREML(d) models give estimates of rA and rE largely in agreement with each other for most correlated cardiometabolic pairs.…”

Section: Discussionmentioning

confidence: 57%

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Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits

Chen

Kuja‐Halkola

Chang

et al. 2018

JAHA

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BackgroundThe variation and covariation for many cardiometabolic traits have been decomposed into genetic and environmental fractions, by using twin or single‐nucleotide polymorphism (SNP) models. However, differences in population, age, sex, and other factors hamper the comparison between twin‐ and SNP‐based estimates.Methods and ResultsTwenty‐four cardiometabolic traits and 700,000 genotyped SNPs were available in the study base of 10 682 twins from TwinGene cohort. For the 27 highly correlated pairs (absolute phenotypic correlation coefficient ≥0.40), twin‐based bivariate structural equation models were performed in 3870 complete twin pairs, and SNP‐based bivariate genomic relatedness matrix restricted maximum likelihood methods were performed in 5779 unrelated individuals. In twin models, the model including additive genetic variance and unique/nonshared environmental variance was the best‐fitted model for 7 pairs (5 of them were between blood pressure traits); the model including additive genetic variance, common/shared environmental variance, and unique/nonshared environmental variance components was best fitted for 4 pairs, but estimates of shared environment were close to zero; and the model including additive genetic variance, dominant genetic variance, and unique/nonshared environmental variance was best fitted for 16 pairs, in which significant dominant genetic effects were identified for 13 pairs (including all 9 obesity‐related pairs). However, SNP models did not identify significant estimates of dominant genetic effects for any pairs. In the paired t test, twin‐ and SNP‐based estimates of additive genetic correlation were not significantly different (both were 0.67 on average), whereas the nonshared environmental correlations from these 2 models differed slightly from each other (on average, twin‐based estimate=0.64 and SNP‐based estimate=0.68).ConclusionsBeside additive genetic effects and nonshared environment, nonadditive genetic effects (dominance) also contribute to the covariation between certain cardiometabolic traits (especially for obesity‐related pairs); contributions from the shared environment seem to be weak for their covariation in TwinGene samples.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 57%

Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits

Chen

Kuja‐Halkola

Chang

et al. 2018

JAHA

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“…The UKBB1+GIANT2015 estimate was reported by Ni et al 23 Bars are 95% confidence interval. For RNM, the model was y = α0+ α1×c + e. For RR-GREML and GCI-GREML, the model was y = α0+ α1×c + e. In RR-GREML and GCI-GREML, samples were arbitrarily stratified into four different groups according to the covariate levels.…”

Section: Softwarementioning

confidence: 95%

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

Werf²,

Zhou

et al. 2018

Preprint

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The genomics era has brought useful tools to dissect the genetic architecture of complex traits. We propose a reaction norm model (RNM) to tackle genotype-environment correlation and interaction problems in the context of genome-wide association analyses of complex traits. In our approach, an environmental risk factor affecting the trait of interest can be modeled as dependent on a continuous covariate that is itself regulated by genetic as well as environmental factors. Our multivariate RNM approach allows the joint modelling of the relation between the genotype (G) and the covariate (C), so that both their correlation (association) and interaction (effect modification) can be estimated. Hence we jointly estimate genotype-covariate correlation and interaction (GCCI). We demonstrate using simulation that the proposed multivariate RNM performs better than the current state-of-theart methods that ignore G-C correlation. We apply the method to data from the UK Biobank (N= 66,281) in analysis of body mass index using smoking quantity as a covariate. We find a highly significant G-C correlation, but a negligible G-C interaction. In contrast, when a conventional G-C interaction analysis is applied (i.e., G-C correlation is not included in the model), highly significant G-C interaction estimates are found. It is also notable that we find a significant heterogeneity in the estimated residual variances across different covariate levels probably due to residual-covariate interaction. Using simulation we also show that the residual variances estimated by genomic restricted maximum likelihood (GREML) or linkage disequilibrium score regression (LDSC) can be inflated in the presence of interactions, implying that the currently reported SNP-heritability estimates from these methods should be interpreted with caution. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses and that the failure to do so may lead to substantial biases in inferences relating to genetic architecture of complex traits, including estimated SNP-heritability.

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“…There is no current literature on the expected standard error or power from LD score regression, however it can be compared to those expected from the linear mixed model maximum likelihood method (GREML), which estimates SNP-heritabilities and genetic correlations from GWAS genotype data (Visscher et al, 2014). Empirical comparisons have shown that the error associated with using LD score regression is approximately fifty percent larger than that of GREML (Ni et al, 2017). Using the GCTA- Table 2A †=…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, as genotyping becomes cheaper, genomewide SNP data is becoming more readily and widely available than pedigree data. Moreover, unbiased estimates of genetic correlations are achievable with minimal computing resources from analysis of summary statistics from genome-wide association studies via the LD-score regression method (Bulik-Sullivan et al, 2015a; Ni et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans

Sodini

Kemper

Wray

et al. 2018

Preprint

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Accurate estimation of genetic correlation requires large sample sizes and access to genetically informative data, which are not always available. Accordingly, phenotypic correlations are often assumed to reflect genotypic correlations in evolutionary biology. Cheverud's conjecture asserts that the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence of the conjecture has been found across plant and animal species, with results suggesting that there is indeed a robust relationship between the two. Here, we investigate the conjecture in human populations, an analysis made possible by recent developments in availability of human genomic data and computing resources. A sample of 108,035 British European individuals from the UK Biobank was split equally into discovery and replication datasets. 17 traits were selected based on sample size, distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score regression applied to the genome-wide association summary statistics of pairs of traits, and compared within and across datasets. Strong and significant correlations were found for the between-dataset comparison, suggesting that the genetic correlations from one independent sample were able to predict the phenotypic correlations from another independent sample within the same population. Designating the selected traits as morphological or non-morphological indicated little difference in correlation. The results of this study support the existence of a relationship between genetic and phenotypic correlations in humans. This finding is of specific interest in anthropological studies, which use measured phenotypic correlations to make inferences about the genetics of ancient human populations.

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Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Cited by 29 publications

References 38 publications

Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits

Genetic and Environmental Contributions to the Covariation Between Cardiometabolic Traits

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

Comparison of Genotypic and Phenotypic Correlations: Cheverud’s Conjecture in Humans

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