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1.
Int J Epidemiol ; 2021 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-33393617

RESUMO

BACKGROUND: Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. METHODS: We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). RESULTS: Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. CONCLUSION: The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

2.
Circulation ; 2020 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-33249881

RESUMO

Background: Recent clinical guidelines support intensive blood pressure (BP) treatment targets. However, observational data suggest that excessive diastolic BP (DBP) lowering might increase the risk of myocardial infarction (MI); reflecting a J- or U-shaped relationship. Methods: We analyzed 47,407 participants from 5 cohorts (median age 60 years). First, to corroborate prior observational analyses, we used traditional statistical methods to test the shape of association between DBP and CVD. Second, we created polygenic risk scores (PRS) of DBP and SBP and generated linear Mendelian randomization (MR) estimates for the effect of DBP on CVD. Third, using novel non-linear MR approaches, we evaluated for non-linearity in the genetic relationship between DBP and CVD. Comprehensive MR interrogation of DBP required us to also model SBP, given the two are strongly correlated. Results: Traditional observational analysis of our cohorts suggested a J-shaped association between DBP and MI. By contrast, linear MR analyses demonstrated an adverse effect of increasing DBP increments on CVD outcomes, including MI (MI Hazard ratio = 1.07 per unit mmHg increase in DBP, p<0.001). Furthermore, non-linear MR analyses found no evidence for a J-shaped relationship, instead confirming that MI risk decreases consistently per unit decrease in DBP, even among individuals with low values of baseline DBP. Conclusions: In this analysis of the genetic effect of DBP, we found no evidence for a non-linear J- or U-shaped relationship between DBP and adverse CVD outcomes; including MI.

3.
Nat Genet ; 52(6): 572-581, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32424353

RESUMO

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.


Assuntos
Neoplasias da Mama/genética , Estudo de Associação Genômica Ampla , Proteína BRCA1/genética , Neoplasias da Mama/patologia , Estudos de Casos e Controles , Feminino , Predisposição Genética para Doença , Humanos , Desequilíbrio de Ligação , Mutação , Neoplasias de Mama Triplo Negativas/genética , Neoplasias de Mama Triplo Negativas/patologia
4.
Kidney Int ; 98(3): 708-716, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32454124

RESUMO

Blood pressure and kidney function have a bidirectional relation. Hypertension has long been considered as a risk factor for kidney function decline. However, whether intensive blood pressure control could promote kidney health has been uncertain. The kidney is known to have a major role in affecting blood pressure through sodium extraction and regulating electrolyte balance. This bidirectional relation makes causal inference between these two traits difficult. Therefore, to examine the causal relations between these two traits, we performed two-sample Mendelian randomization analyses using summary statistics of large-scale genome-wide association studies. We selected genetic instruments more likely to be specific for kidney function using meta-analyses of complementary kidney function biomarkers (glomerular filtration rate estimated from serum creatinine [eGFRcr], and blood urea nitrogen from the CKDGen Consortium). Systolic and diastolic blood pressure summary statistics were from the International Consortium for Blood Pressure and UK Biobank. Significant evidence supported the causal effects of higher kidney function on lower blood pressure. Based on the mode-based Mendelian randomization method, the effect estimates for one standard deviation (SD) higher in log-transformed eGFRcr was -0.17 SD unit (95 % confidence interval: -0.09 to -0.24) in systolic blood pressure and -0.15 SD unit (95% confidence interval: -0.07 to -0.22) in diastolic blood pressure. In contrast, the causal effects of blood pressure on kidney function were not statistically significant. Thus, our results support causal effects of higher kidney function on lower blood pressure and suggest preventing kidney function decline can reduce the public health burden of hypertension.

5.
Nat Commun ; 10(1): 1941, 2019 04 26.
Artigo em Inglês | MEDLINE | ID: mdl-31028273

RESUMO

Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder.


Assuntos
Algoritmos , Neoplasias da Mama/genética , Doença da Artéria Coronariana/genética , Transtorno Depressivo Maior/genética , Genoma Humano , Análise da Randomização Mendeliana/estatística & dados numéricos , Fatores Etários , Índice de Massa Corporal , Neoplasias da Mama/sangue , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/etiologia , HDL-Colesterol/sangue , Doença da Artéria Coronariana/sangue , Doença da Artéria Coronariana/diagnóstico , Doença da Artéria Coronariana/etiologia , Conjuntos de Dados como Assunto , Transtorno Depressivo Maior/sangue , Transtorno Depressivo Maior/diagnóstico , Transtorno Depressivo Maior/etiologia , Feminino , Estudo de Associação Genômica Ampla , Humanos , Menarca/sangue , Menarca/genética , Característica Quantitativa Herdável , Fatores de Risco , Triglicerídeos/sangue
6.
PLoS Genet ; 14(10): e1007549, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30289880

RESUMO

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.


Assuntos
Estudos de Associação Genética/métodos , Pleiotropia Genética/genética , Estudo de Associação Genômica Ampla/métodos , Padrões de Herança/genética , Algoritmos , Predisposição Genética para Doença/genética , Humanos , Modelos Genéticos , Polimorfismo de Nucleotídeo Único
7.
Nat Genet ; 50(9): 1318-1326, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30104760

RESUMO

We developed a likelihood-based approach for analyzing summary-level statistics and external linkage disequilibrium information to estimate effect-size distributions of common variants, characterized by the proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of results available across 32 genome-wide association studies showed that, while all traits are highly polygenic, there is wide diversity in the degree and nature of polygenicity. Psychiatric diseases and traits related to mental health and ability appear to be most polygenic, involving a continuum of small effects. Most other traits, including major chronic diseases, involve clusters of SNPs that have distinct magnitudes of effects. We predict that the sample sizes needed to identify SNPs that explain most heritability found in genome-wide association studies will range from a few hundred thousand to multiple millions, depending on the underlying effect-size distributions of the traits. Accordingly, we project the risk-prediction ability of polygenic risk scores across a wide variety of diseases.


Assuntos
Predisposição Genética para Doença/genética , Genoma/genética , Estudo de Associação Genômica Ampla/métodos , Humanos , Funções Verossimilhança , Desequilíbrio de Ligação/genética , Transtornos Mentais/genética , Modelos Genéticos , Herança Multifatorial/genética , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Fatores de Risco
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