Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Zhang, Yiliang; Lu, Qiongshi; Ye, Yixuan; Huang, Kunling; Liu, Wei; Wu, Yuchang; Zhong, Xiaoyuan; Li, Boyang; Yu, Zhaolong; Travers, Brittany G.; Werling, Donna M.; Li, James J.; Zhao, Hongyu

doi:10.1101/2020.05.08.084475

Cited by 22 publications

(33 citation statements)

References 110 publications

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“…Followed up in three independent cohorts of ASD proband-parent trios, we identified significant over-transmission of the direct component of EA from healthy parents to ASD probands but not to the healthy siblings. These results added value to the recent advances in understanding common genetic variations' roles in ASD etiology 33,35,38,39 and provided critical new insights into the shared genetic basis of ASD and cognitive ability. These results also call for extreme caution in human genome editing and embryo screening 40 .…”

Section: Discussionmentioning

confidence: 82%

Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

Zhong

Lin

et al. 2020

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Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a novel statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower BMI, less active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. Polygenic transmission disequilibrium test showed a significant over-transmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

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Section: Discussionmentioning

confidence: 82%

Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

Zhong

Lin

et al. 2020

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“…Genetic correlation estimation methods can be classified as methods requiring individual-level data 6,[10][11][12][13] and methods that use GWAS summary statistics as input [3][4][5][14][15][16][17][18][19] . Restricted maximum likelihood (REML) is the most common approach among individual-level-data-based methods where genetic correlation is estimated as one of the (co)variance component parameters of LMM.…”

Section: Introductionmentioning

confidence: 99%

“…Cross-trait linkage disequilibrium (LD) score regression (LDSC) is the first method that uses GWAS summary statistics alone as input to estimate genetic correlation 3 . Built upon LDSC, methods have been developed to estimate annotation-stratified 4 , local [14][15][16] , and trans-ethnic 17 genetic correlation from GWAS summary statistics. Zhang et al 15 showed that most existing methods are based on the idea of minimizing the "distance" between the empirical and theoretical covariance matrices of marginal z-scores obtained from GWASs of two phenotypes.…”

Section: Introductionmentioning

confidence: 99%

“…Built upon LDSC, methods have been developed to estimate annotation-stratified 4 , local [14][15][16] , and trans-ethnic 17 genetic correlation from GWAS summary statistics. Zhang et al 15 showed that most existing methods are based on the idea of minimizing the "distance" between the empirical and theoretical covariance matrices of marginal z-scores obtained from GWASs of two phenotypes. Due to the information loss in GWAS summary statistics, the standard errors of estimates from summary-statistics-based methods can be substantially higher than those from methods based on individual-level data 21 .…”

Section: Introductionmentioning

confidence: 99%

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Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

Zhang¹,

Cheng²,

Jiang³

et al. 2020

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Genetic correlation is the correlation of additive genetic effects on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlations based on data collected from genome- wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications compared to other methods due to the imprecision of LD obtained from reference panels. Our findings offer a guidance on how to appropriately choose the method for genetic correlation estimation in post-GWAS analysis in interpretation.

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“…Recently, heritability studies and GWAS implicated critical roles of common SNPs in the etiology of ASD [5, 6]. Additionally, multi-trait modeling revealed strong genetic correlations of ASD with various complex traits [7]. Compared to other approaches, pTDT leverages a robust, family-based design to assess the transmission disequilibrium of complex traits’ polygenic risk between healthy parents and ASD probands and shed important light on the shared genetics between ASD and other genetically correlated phenotypes [3].…”

Section: Introductionmentioning

confidence: 99%

Gamete simulation improves polygenic transmission disequilibrium analysis

Chen

You

Zhao

et al. 2020

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Polygenic risk scores (PRS) derived from summary statistics of genome-wide association studies (GWAS) have enjoyed great popularity in human genetics research. Applied to population cohorts, PRS can effectively stratify individuals by risk group and has promising applications in early diagnosis and clinical intervention. However, our understanding of within-family polygenic risk is incomplete, in part because the small samples per family significantly limits power. Here, to address this challenge, we introduce ORIGAMI, a computational framework that uses parental genotype data to simulate offspring genomes. ORIGAMI uses state-of-the-art genetic maps to simulate realistic recombination events on phased parental genomes and allows quantifying the prospective PRS variability within each family. We quantify and showcase the substantially reduced yet highly heterogeneous PRS variation within families for numerous complex traits. Further, we incorporate within-family PRS variability to improve polygenic transmission disequilibrium test (pTDT). Through simulations, we demonstrate that modeling within-family risk substantially improves the statistical power of pTDT. Applied to 7,805 trios of autism spectrum disorder (ASD) probands and healthy parents, we successfully replicated previously reported over-transmission of ASD, educational attainment, and schizophrenia risk, and identified multiple novel traits with significant transmission disequilibrium. These results provided novel etiologic insights into the shared genetic basis of various complex traits and ASD.

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Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Cited by 22 publications

References 110 publications

Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies

Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

Gamete simulation improves polygenic transmission disequilibrium analysis

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