Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at P < 5 x 10(-7) between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (P(follow-up) 相似文献   

Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies

To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10???) and 9q31.2 (P = 2.2 × 10?33), we identified 30 new menarche loci (all P < 5 × 10??) and found suggestive evidence for a further 10 loci (P < 1.9 × 10??). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.     

A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21

Much of the variation in inherited risk of colorectal cancer (CRC) is probably due to combinations of common low risk variants. We conducted a genome-wide association study of 550,000 tag SNPs in 930 familial colorectal tumor cases and 960 controls. The most strongly associated SNP (P = 1.72 x 10(-7), allelic test) was rs6983267 at 8q24.21. To validate this finding, we genotyped rs6983267 in three additional CRC case-control series (4,361 affected individuals and 3,752 controls; 1,901 affected individuals and 1,079 controls; 1,072 affected individuals and 415 controls) and replicated the association, providing P = 1.27 x 10(-14) (allelic test) overall, with odds ratios (ORs) of 1.27 (95% confidence interval (c.i.): 1.16-1.39) and 1.47 (95% c.i.: 1.34-1.62) for heterozygotes and rare homozygotes, respectively. Analyses based on 1,477 individuals with colorectal adenoma and 2,136 controls suggest that susceptibility to CRC is mediated through development of adenomas (OR = 1.21, 95% c.i.: 1.10-1.34; P = 6.89 x 10(-5)). These data show that common, low-penetrance susceptibility alleles predispose to colorectal neoplasia.     

A genome-wide association study identifies new susceptibility loci for non-cardia gastric cancer at 3q13.31 and 5p13.1

Gastric cancer, including the cardia and non-cardia types, is the second leading cause of cancer-related deaths worldwide. To identify genetic risk variants for non-cardia gastric cancer, we performed a genome-wide association study in 3,279 individuals (1,006 with non-cardia gastric cancer and 2,273 controls) of Chinese descent. We replicated significant associations in an additional 6,897 subjects (3,288 with non-cardia gastric cancer and 3,609 controls). We identified two new susceptibility loci for non-cardia gastric cancer at 5p13.1 (rs13361707 in the region including PTGER4 and PRKAA1; odds ratio (OR) = 1.41; P = 7.6 × 10(-29)) and 3q13.31 (rs9841504 in ZBTB20; OR = 0.76; P = 1.7 × 10(-9)). Imputation analyses also confirmed previously reported associations of rs2294008 and rs2976392 on 8q24, rs4072037 on 1q22 and rs13042395 on 20p13 with non-cardia gastric cancer susceptibility in the Han Chinese population.     

Principal components analysis corrects for stratification in genome-wide association studies

Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.     

A genome-wide association study identifies susceptibility loci for Wilms tumor

Wilms tumor is the most common renal malignancy of childhood. To identify common variants that confer susceptibility to Wilms tumor, we conducted a genome-wide association study in 757 individuals with Wilms tumor (cases) and 1,879 controls. We evaluated ten SNPs in regions significantly associated at P < 5 × 10(-5) in two independent replication series from the UK (769 cases and 2,814 controls) and the United States (719 cases and 1,037 controls). We identified clear significant associations at 2p24 (rs3755132, P = 1.03 × 10(-14); rs807624, P = 1.32 × 10(-14)) and 11q14 (rs790356, P = 4.25 × 10(-15)). Both regions contain genes that are plausibly related to Wilms tumorigenesis. We also identified candidate association signals at 5q14, 22q12 and Xp22.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.     

Assembly of microarrays for genome-wide measurement of DNA copy number.

We have assembled arrays of approximately 2,400 BAC clones for measurement of DNA copy number across the human genome. The arrays provide precise measurement (s.d. of log2 ratios=0.05-0.10) in cell lines and clinical material, so that we can reliably detect and quantify high-level amplifications and single-copy alterations in diploid, polyploid and heterogeneous backgrounds.     

Evidence for at least four Fanconi anaemia genes including FACC on chromosome 9.

Fanconi anaemia (FA) is a DNA repair disorder characterized by cellular hypersensitivity to DNA cross-linking agents and extensive phenotypic heterogeneity. To determine the extent of genetic heterogeneity present in FA, a panel of somatic cell hybrids was constructed using polyethylene glycol-mediated cell fusion. Three new complementation groups were identified, designated FA(B), FA(C) and FA(D), and the gene defective in FA(C) which we have recently cloned was localized to chromosome 9q22.3 through in situ hybridization. These results suggest that mutations in at least four different genes lead to FA, a degree of genetic heterogeneity comparable to that of other DNA repair disorders.     

Two new susceptibility loci for Kawasaki disease identified through genome-wide association analysis

To find new candidate loci predisposing individuals to Kawasaki disease, an acute vasculitis that affects children, we conducted a genome-wide association study in 622 individuals with Kawasaki disease (cases) and 1,107 controls in a Han Chinese population residing in Taiwan, with replication in an independent Han Chinese sample of 261 cases and 550 controls. We report two new loci, one at BLK (encoding B-lymphoid tyrosine kinase) and one at CD40, that are associated with Kawasaki disease at genome-wide significance (P < 5 × 10(-8)). Our findings may lead to a better understanding of the role of immune activation and inflammation in Kawasaki disease pathogenesis.     

A genome-wide association study identifies three new risk loci for Kawasaki disease

We performed a genome-wide association study (GWAS) of Kawasaki disease in Japanese subjects using data from 428 individuals with Kawasaki disease (cases) and 3,379 controls genotyped at 473,803 SNPs. We validated the association results in two independent replication panels totaling 754 cases and 947 controls. We observed significant associations in the FAM167A-BLK region at 8p22-23 (rs2254546, P = 8.2 × 10(-21)), in the human leukocyte antigen (HLA) region at 6p21.3 (rs2857151, P = 4.6 × 10(-11)) and in the CD40 region at 20q13 (rs4813003, P = 4.8 × 10(-8)). We also replicated the association of a functional SNP of FCGR2A (rs1801274, P = 1.6 × 10(-6)) identified in a recently reported GWAS of Kawasaki disease. Our findings provide new insights into the pathogenesis and pathophysiology of Kawasaki disease.     

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.     

A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia

We conducted a genome-wide association study of 299,983 tagging SNPs for chronic lymphocytic leukemia (CLL) and performed validation in two additional series totaling 1,529 cases and 3,115 controls. We identified six previously unreported CLL risk loci at 2q13 (rs17483466; P = 2.36 x 10(-10)), 2q37.1 (rs13397985, SP140; P = 5.40 x 10(-10)), 6p25.3 (rs872071, IRF4; P = 1.91 x 10(-20)), 11q24.1 (rs735665; P = 3.78 x 10(-12)), 15q23 (rs7176508; P = 4.54 x 10(-12)) and 19q13.32 (rs11083846, PRKD2; P = 3.96 x 10(-9)). These data provide the first evidence for the existence of common, low-penetrance susceptibility to a hematological malignancy and new insights into disease causation in CLL.     

A genome-wide association study identifies two new risk loci for Graves' disease

Graves' disease is a common autoimmune disorder characterized by thyroid stimulating hormone receptor autoantibodies (TRAb) and hyperthyroidism. To investigate the genetic architecture of Graves' disease, we conducted a genome-wide association study in 1,536 individuals with Graves' disease (cases) and 1,516 controls. We further evaluated a group of associated SNPs in a second set of 3,994 cases and 3,510 controls. We confirmed four previously reported loci (in the major histocompatibility complex, TSHR, CTLA4 and FCRL3) and identified two new susceptibility loci (the RNASET2-FGFR1OP-CCR6 region at 6q27 (P(combined) = 6.85 × 10(-10) for rs9355610) and an intergenic region at 4p14 (P(combined) = 1.08 × 10(-13) for rs6832151)). These newly associated SNPs were correlated with the expression levels of RNASET2 at 6q27, of CHRNA9 and of a previously uncharacterized gene at 4p14, respectively. Moreover, we identified strong associations of TSHR and major histocompatibility complex class II variants with persistently TRAb-positive Graves' disease.     

A new multipoint method for genome-wide association studies by imputation of genotypes

Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.     

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).     

Meta-analysis of genome-wide association studies identifies three new risk loci for atopic dermatitis

Atopic dermatitis (AD) is a commonly occurring chronic skin disease with high heritability. Apart from filaggrin (FLG), the genes influencing atopic dermatitis are largely unknown. We conducted a genome-wide association meta-analysis of 5,606 affected individuals and 20,565 controls from 16 population-based cohorts and then examined the ten most strongly associated new susceptibility loci in an additional 5,419 affected individuals and 19,833 controls from 14 studies. Three SNPs reached genome-wide significance in the discovery and replication cohorts combined, including rs479844 upstream of OVOL1 (odds ratio (OR) = 0.88, P = 1.1 × 10(-13)) and rs2164983 near ACTL9 (OR = 1.16, P = 7.1 × 10(-9)), both of which are near genes that have been implicated in epidermal proliferation and differentiation, as well as rs2897442 in KIF3A within the cytokine cluster at 5q31.1 (OR = 1.11, P = 3.8 × 10(-8)). We also replicated association with the FLG locus and with two recently identified association signals at 11q13.5 (rs7927894; P = 0.008) and 20q13.33 (rs6010620; P = 0.002). Our results underline the importance of both epidermal barrier function and immune dysregulation in atopic dermatitis pathogenesis.     

A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis

To identify susceptibility loci for ankylosing spondylitis, we performed a two-stage genome-wide association study in Han Chinese. In the discovery stage, we analyzed 1,356,350 autosomal SNPs in 1,837 individuals with ankylosing spondylitis and 4,231 controls; in the validation stage, we analyzed 30 suggestive SNPs in an additional 2,100 affected individuals and 3,496 controls. We identified two new susceptibility loci between EDIL3 and HAPLN1 at 5q14.3 (rs4552569; P = 8.77 × 10(-10)) and within ANO6 at 12q12 (rs17095830; P = 1.63 × 10(-8)). We also confirmed previously reported associations in Europeans within the major histocompatibility complex (MHC) region (top SNP, rs13202464; P < 5 × 10(-324)) and at 2p15 (rs10865331; P = 1.98 × 10(-8)). We show that rs13202464 within the MHC region mainly represents the risk effect of HLA-B*27 variants (including HLA-B*2704, HLA-B*2705 and HLA-B*2715) in Chinese. The two newly discovered loci implicate genes related to bone formation and cartilage development, suggesting their potential involvement in the etiology of ankylosing spondylitis.