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1.
Hum Mol Genet ; 2019 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-31595288

RESUMO

Regulatory variation plays a major role in complex disease and that cell-type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF binding sites to disease heritability is challenging, as binding is often cell-type-specific and annotations from directly measured TF binding are not currently available for most cell-type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF binding annotations constructed by intersecting sequence-based TF binding predictions with cell-type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell-types; this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context, and the limitation that sequence-based predictions are generally not cell-type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified LD score regression with the baseline-LD model (which is not cell-type-specific) plus the new annotations. We determined that 100bp windows around MotifMap sequenced-based TF binding predictions intersected with a union of six cell-type-specific chromatin marks (imputed using ChromImpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6x vs 7.3x; P = 9 x 10-14 for difference) and a 20% increase in cell-type-specific signal conditional on annotations from the baseline-LD model (P = 8 x 10-11 for difference). Our results show that TF binding annotations explain substantial disease heritability and can help refine genome-wide association signals.

3.
Nat Genet ; 51(8): 1295, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31273336

RESUMO

In the version of the paper initially published, information on competing interests for author Benjamin M. Neale was missing. The 'Competing interests' statement should have included the sentence 'B.M.N. is on the Scientific Advisory Board of Deep Genomics'.

5.
Am J Hum Genet ; 104(5): 896-913, 2019 May 02.
Artigo em Inglês | MEDLINE | ID: mdl-31051114

RESUMO

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.

6.
Nat Genet ; 51(4): 683-693, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30858613

RESUMO

Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait-cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations.


Assuntos
Hematopoese/genética , Polimorfismo de Nucleotídeo Único/genética , Linhagem da Célula/genética , Cromatina/genética , Mapeamento Cromossômico/métodos , Epigenômica/métodos , Estudo de Associação Genômica Ampla/métodos , Humanos , Desequilíbrio de Ligação/genética , Fenótipo , Locos de Características Quantitativas/genética , Sequências Reguladoras de Ácido Nucleico/genética
7.
Nat Commun ; 10(1): 790, 2019 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-30770844

RESUMO

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.


Assuntos
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
8.
Nat Commun ; 10(1): 431, 2019 01 25.
Artigo em Inglês | MEDLINE | ID: mdl-30683880

RESUMO

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10-8), breast and ovarian cancer (rg = 0.24, p = 7 × 10-5), breast and lung cancer (rg = 0.18, p =1.5 × 10-6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.


Assuntos
Neoplasias da Mama/genética , Neoplasias Colorretais/genética , Neoplasias de Cabeça e Pescoço/genética , Padrões de Herança , Neoplasias Pulmonares/genética , Neoplasias Ovarianas/genética , Neoplasias da Próstata/genética , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/etnologia , Neoplasias da Mama/patologia , Estudos de Casos e Controles , Neoplasias Colorretais/diagnóstico , Neoplasias Colorretais/etnologia , Neoplasias Colorretais/patologia , Grupo com Ancestrais do Continente Europeu , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Neoplasias de Cabeça e Pescoço/diagnóstico , Neoplasias de Cabeça e Pescoço/etnologia , Neoplasias de Cabeça e Pescoço/patologia , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/etnologia , Neoplasias Pulmonares/patologia , Masculino , Transtornos Mentais/etnologia , Transtornos Mentais/genética , Transtornos Mentais/fisiopatologia , Proteínas de Neoplasias/genética , Neoplasias Ovarianas/diagnóstico , Neoplasias Ovarianas/etnologia , Neoplasias Ovarianas/patologia , Fenótipo , Polimorfismo de Nucleotídeo Único , Neoplasias da Próstata/diagnóstico , Neoplasias da Próstata/etnologia , Neoplasias da Próstata/patologia , Fumar/etnologia , Fumar/genética , Fumar/fisiopatologia
9.
Am J Med Genet B Neuropsychiatr Genet ; 180(6): 428-438, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30593698

RESUMO

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.

10.
Genet Epidemiol ; 2018 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-30474154

RESUMO

Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross-population correlation of true causal effect sizes ( ρ b ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g , we can use this ratio to estimate ρ b . Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

11.
Nat Genet ; 50(11): 1600-1607, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30297966

RESUMO

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).

12.
Nat Genet ; 50(10): 1483-1493, 2018 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-30177862

RESUMO

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.

13.
Nature ; 559(7714): 350-355, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29995854

RESUMO

The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6-9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3-TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5-50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.

14.
Nat Genet ; 50(7): 1041-1047, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29942083

RESUMO

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.

15.
Science ; 360(6395)2018 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-29930110

RESUMO

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.


Assuntos
Encefalopatias/genética , Transtornos Mentais/genética , Encefalopatias/classificação , Encefalopatias/diagnóstico , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Transtornos Mentais/classificação , Transtornos Mentais/diagnóstico , Fenótipo , Característica Quantitativa Herdável , Fatores de Risco
16.
Nat Genet ; 50(5): 668-681, 2018 May.
Artigo em Inglês | MEDLINE | ID: mdl-29700475

RESUMO

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

17.
Nat Genet ; 50(4): 621-629, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29632380

RESUMO

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.

18.
Nat Genet ; 50(4): 538-548, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29632383

RESUMO

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.

19.
Nature ; 551(7678): 92-94, 2017 11 02.
Artigo em Inglês | MEDLINE | ID: mdl-29059683

RESUMO

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.


Assuntos
Neoplasias da Mama/genética , Loci Gênicos , Predisposição Genética para Doença/genética , Estudo de Associação Genômica Ampla , Ásia/etnologia , Grupo com Ancestrais do Continente Asiático/genética , Sítios de Ligação/genética , Neoplasias da Mama/diagnóstico , Simulação por Computador , Europa (Continente)/etnologia , Grupo com Ancestrais do Continente Europeu/genética , Feminino , Humanos , Herança Multifatorial/genética , Polimorfismo de Nucleotídeo Único/genética , Sequências Reguladoras de Ácido Nucleico , Medição de Risco , Fatores de Transcrição/metabolismo
20.
Nat Genet ; 49(12): 1767-1778, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29058716

RESUMO

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.


Assuntos
Proteína BRCA1/genética , Neoplasias da Mama/genética , Predisposição Genética para Doença/genética , Mutação , Polimorfismo de Nucleotídeo Único , Neoplasias da Mama/etnologia , Neoplasias da Mama/metabolismo , Grupo com Ancestrais do Continente Europeu/genética , Feminino , Predisposição Genética para Doença/etnologia , Estudo de Associação Genômica Ampla/métodos , Heterozigoto , Humanos , Receptores Estrogênicos/metabolismo , Fatores de Risco
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