McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9: 356-369

Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Nature Reviews Genetics (Impact Factor: 36.98). 06/2008; 9(5):356-69. DOI: 10.1038/nrg2344
Source: PubMed


The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

Download full-text


Available from: Lon Cardon, Apr 27, 2015
  • Source
    • "Another fundamental problem in statistical genetics is associating genetic loci (single nucleotide polymorphisms or SNPs) to phenotypes—or mapping traits to genetic loci. Genome wide association studies (GWAS) [67] [68] are an important example of mapping traits to better understand the genetic architecture of "
    [Show abstract] [Hide abstract]
    ABSTRACT: Nonlinear kernels are used extensively in regression models in statistics and machine learning since they often improve predictive accuracy. Variable selection is a challenge in the context of kernel based regression models. In linear regression the concept of an effect size for the regression coefficients is very useful for variable selection. In this paper we provide an analog for the effect size of each explanatory variable for Bayesian kernel regression models when the kernel is shift-invariant---for example the Gaussian kernel. The key idea that allows for the extraction of effect sizes is a random Fourier expansion for shift-invariant kernel functions. These random Fourier bases serve as a linear vector space in which a linear model can be defined and regression coefficients in this vector space can be projected onto the original explanatory variables. This projection serves as the analog for effect sizes. We apply this idea to specify a class of scalable Bayesian kernel regression models (SBKMs) for both nonparametric regression and binary classification. We also demonstrate how this framework encompasses both fixed and mixed effects modeling characteristics. We illustrate the utility of our approach on simulated and real data.
    • "To date, despite many efforts and many QTL mapped, only a few genes have been identified that are unequivocally involved in variations in milk yield and composition (Grisart et al. 2002; Blott et al. 2003; Cohen-Zinder et al. 2005). In this context, genomewide association studies (GWAS) are the most common method for dissecting the biology that underlies complex traits (McCarthy et al. 2008). GWAS identify marker–trait associations by exploiting the LD that exists between the causative mutation and one or more genetic markers to pinpoint genomic regions carrying causal variants for the trait being considered. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genome-wide association studies (GWAS) have been widely applied to disentangle the genetic basis of complex traits. In cattle breeds, classical GWAS approaches with medium-density marker panels are far from conclusive, especially for complex traits. This is due to the intrinsic limitations of GWAS and the assumptions that are made to step from the association signals to the functional variations. Here, we applied a gene-based strategy to prioritize genotype-phenotype associations found for milk production and quality traits with classical approaches in three Italian dairy cattle breeds with different sample sizes (Italian Brown n = 745; Italian Holstein n = 2058; Italian Simmental n = 477). Although classical regression on single markers revealed only a single genome-wide significant genotype-phenotype association, for Italian Holstein, the gene-based approach identified specific genes in each breed that are associated with milk physiology and mammary gland development. As no standard method has yet been established to step from variation to functional units (i.e., genes), the strategy proposed here may contribute to revealing new genes that play significant roles in complex traits, such as those investigated here, amplifying low association signals using a gene-centric approach. © 2015 Stichting International Foundation for Animal Genetics.
    Animal Genetics 05/2015; 46(4). DOI:10.1111/age.12303 · 2.21 Impact Factor
    • "Over the last few years, genetic variants that contribute to complex human traits like T2D have been identified. Linkage analysis, candidate gene, association studies and more recently genome wide association studies (GWAs) (Frazer et al., 2009; McCarthy et al., 2008) have been useful strategies in these efforts. Some common variants in HHEX, HNF4α, KCNJ11, PPARγ, CDKN2A/2B, SLC30A8, CDC123/CAMK1D and TCF7L2 genes have been associated with T2D in several European and Asian populations, nevertheless we do not know if the information gained from these investigations can be extrapolated to other populations , besides only a few of them have been replicated in Mexican Gene 565 (2015) 68–75 Abbreviations:ABCA1, ATP-binding cassette,sub-family A,member 1;AIMs, ancestryinformative markers; CDC123/CAMK1D, cell division cycle 123/calcium/calmodulin- dependent protein kinase 1D; CDKN2A/2B, cyclin-dependent kinase inhibitor 2A/2B; GWAs, genome wide association studies; HDL, high density lipoprotein; HHEX, hematopoietically expressed homeobox; HNF4α, hepatocyte nuclear factor 4, alpha; HOMA-β, homeostasis model assessment β-cell function; HOMA-IR, homeostasis model assessment insulin resistance; KCNJ11, potassium inwardly rectifying channel, subfamily J, member 11; LDL, low density lipoprotein; PPARγ, peroxisome proliferator-activated receptorgamma ;sdLDL-C, small dense low-density lipoprotein cholesterol;SLC16A11,solute carrier family 16 member 11; SLC30A8, solute carrier family 30 member 8; SNPs, single nucleotide polymorphisms; TCF7L2, transcription factor 7-like 2; T2D, type 2 diabetes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Association of type 2 diabetes (T2D) with common variants in HHEX, HNF4α, KCNJ11, PPARγ, CDKN2A/2B, SLC30A8, CDC123/CAMK1D, TCF7L2, ABCA1 and SLC16A11 genes have been reported, mainly in populations of European and Asian ancestry and to a lesser extent in Latin Americans. Thus, we aimed to investigate the contribution of rs1111875 (HHEX), rs1800961 (HNF4α), rs5219 (KCNJ11), rs1801282 (PPARγ), rs10811661 (CDKN2A/2B), rs13266634 (SLC30A8), rs12779790 (CDC123/CAMK1D), rs7903146 (TCF7L2), rs9282541 (ABCA1) and rs13342692 (SLC16A11) polymorphisms in the genetic background of Maya population to associate their susceptibility to develop T2D. This is one of the first studies designed specifically to investigate the inherited component of T2D in indigenous population of Mexico. SNPs were genotyped by allelic discrimination method in 575 unrelated Maya individuals. Two SNPs rs10811661 and rs928254 were significantly associated with T2D after adjusting for BMI; rs10811661 in a recessive and rs9282541 in a dominant model. Additionally, we found phenotypical alterations associated with genetic variants: HDL to rs9282541 and insulin to rs13342692. In conclusion, these findings support an association of genetic polymorphisms to develop T2D in Maya population. Copyright © 2015. Published by Elsevier B.V.
    Gene 03/2015; 565(1). DOI:10.1016/j.gene.2015.03.065 · 2.14 Impact Factor
Show more