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Artigo em Inglês | MEDLINE | ID: mdl-31669095


BACKGROUND: Clinical and epidemiological studies have shown that obesity is associated with asthma and that these associations differ by asthma subtypes. Little is known about the shared genetic components between obesity and asthma. OBJECTIVE: To identify shared genetic associations between obesity-related traits and asthma subtypes in adults. METHODS: A cross-trait genome-wide association study (GWAS) was performed using 457,822 individuals of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma via GWAS was sought using results from obese vs. lean mouse RNA-seq and RT-PCR experiments. RESULTS: We found a substantial positive genetic correlation between BMI and later-onset asthma defined by asthma age of onset at 16 years of age or older (Rg =0.25, P=9.56×10-22). Mendelian Randomization analysis provided strong evidence in support of BMI causally increasing the risk of asthma. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and GTEx tissue eQTLs, shared immune- and cell differentiation-related pathways between obesity and asthma. Finally, RNA-seq data from lungs of obese versus control mice found that two genes (ACOXL and MYL6) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by RT-PCR in an independent set of mice. CONCLUSIONS: Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma, and identifying molecular pathways that may underlie both obesity and asthma.

Nat Genet ; 2019 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-31578528


Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Am J Hum Genet ; 105(3): 456-476, 2019 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-31402091


Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (Me), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate Me, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread ∼4× more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.

Nat Commun ; 10(1): 790, 2019 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-30770844


Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

Bancos de Espécimes Biológicos , Estudo de Associação Genômica Ampla/métodos , Polimorfismo de Nucleotídeo Único , Característica Quantitativa Herdável , Seleção Genética , Algoritmos , Alelos , Frequência do Gene , Genótipo , Humanos , Modelos Genéticos , Reino Unido
Nat Genet ; 50(12): 1753, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30401984


In the version of this article originally published, there were errors in equations. In the HTML and PDF, the initial term of equation 10 was estimated GCP but should have been estimated standard error, while a 'hat' was missing from the first alpha in the second term of the expression at the end of the paragraph following equation (6) in the Methods. In addition, in the abstract in the PDF, a subscript 1 was used instead of a subscript 2 for the final term of the first fourth-moment expression. These errors have been corrected in the HTML, PDF and print versions of the paper.

Nat Genet ; 50(12): 1728-1734, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30374074


Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments [Formula: see text] and [Formula: see text] of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large [Formula: see text]) will have correlated effects on trait 2 (large α1α2), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.