Problems with Genome-Wide Association Studies

Shriner, Daniel; Vaughan, Laura K.; Padilla, Miguel A.; Tiwari, Hemant K.

doi:10.1126/science.316.5833.1840c

Cited by 89 publications

(47 citation statements)

References 20 publications

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“…Lack of replication or conflicting result is still a problem in some GWA studies of diseases such as Parkinson's disease 83 and INSIG2 gene for obesity. 35 These and other problems in GWA studies have been well addressed by Shriner et al 99 and Williams et al 100 in their Letters to Science.…”

Section: Limitations Of Gwa Studiesmentioning

confidence: 99%

The success of the genome-wide association approach: a brief story of a long struggle

Seng

2008

Eur J Hum Genet

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The genome-wide association approach has been the most powerful and efficient study design thus far in identifying genetic variants that are associated with complex human diseases. This approach became feasible as the result of several key advancements in genetic knowledge, genotyping technologies, statistical analysis algorithms and the availability of large collections of cases and controls. With all these necessary tools in hand, many genome-wide association studies were recently completed, and many more studies which will explore the genetic basis of various complex diseases and quantitative traits are soon to come. This approach has started to reap the fruits of its labor over the past several months. Publications of genome-wide association studies in several complex diseases such as inflammatory bowel disease, type-2 diabetes, breast cancer and prostate cancer have been abundant in the first half of this year. The aims of this review are firstly, to provide a timely summary for most of the genome-wide association studies that have been published until June/July 2007 and secondly, to evaluate to what extent these results have been validated in subsequent replication studies.

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Section: Limitations Of Gwa Studiesmentioning

confidence: 99%

The success of the genome-wide association approach: a brief story of a long struggle

Seng

2008

Eur J Hum Genet

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“…Significant reductions in the costs of sequencing and genotyping make it feasible to conduct association mapping studies in many other species (Thornsberry et al 2001;Aranzana et al 2005;Borevitz et al 2007;Clark et al 2007). However, one obstacle inherent to drawing causal inferences from earlier association studies is the confounding effect of population structure (Lander and Kruglyak 1995;Marchini et al 2004;Hirschhorn and Daly 2005;Wang et al 2005;Shriner et al 2007). Because this effect generally increases in proportion to population size (Reich and Lander 2001), population structure remains a major concern in large-scale association analyses (McCarthy et al 2008).…”

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confidence: 99%

Nonmetric Multidimensional Scaling Corrects for Population Structure in Association Mapping With Different Sample Types

Zhu

2009

Genetics

114

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Recent research has developed various promising methods to control for population structure in genomewide association mapping of complex traits, but systematic examination of how well these methods perform under different genetic scenarios is still lacking. Appropriate methods for controlling genetic relationships among individuals need to balance the concern of false positives and statistical power, which can vary for different association sample types. We used a series of simulated samples and empirical data sets from cross-and self-pollinated species to demonstrate the performance of several contemporary methods in correcting for different types of genetic relationships encountered in association analysis. We proposed a two-stage dimension determination approach for both principal component analysis and nonmetric multidimensional scaling (nMDS) to capture the major structure pattern in association mapping samples. Our results showed that by exploiting both genotypic and phenotypic information, this two-stage dimension determination approach balances the trade-off between data fit and model complexity, resulting in an effective reduction in false positive rate with minimum loss in statistical power. Further, the nMDS technique of correcting for genetic relationship proved to be a powerful complement to other existing methods. Our findings highlight the significance of appropriate application of different statistical methods for dealing with complex genetic relationships in various genomewide association studies.A SSOCIATION mapping of genetic factors underlying major human diseases has yielded very promising results, demonstrating the power of both genomewide and candidate-gene association mapping approaches (Newport et al. 2007;Samani et al. 2007;Scott et al. 2007; Wellcome Trust Case Control Consortium 2007;Yeager et al. 2007). Significant reductions in the costs of sequencing and genotyping make it feasible to conduct association mapping studies in many other species (Thornsberry et al. 2001;Aranzana et al. 2005;Borevitz et al. 2007;Clark et al. 2007). However, one obstacle inherent to drawing causal inferences from earlier association studies is the confounding effect of population structure (Lander and Kruglyak 1995;Marchini et al. 2004;Hirschhorn and Daly 2005;Wang et al. 2005;Shriner et al. 2007). Because this effect generally increases in proportion to population size (Reich and Lander 2001), population structure remains a major concern in large-scale association analyses (McCarthy et al. 2008).Several methods have been proposed for using association mapping in populations with complex genetic structure. Besides the earlier methods of genomic control (Devlin and Roeder 1999;Devlin et al. 2004) and structured association based on Bayesian clustering Falush et al. 2003Falush et al. , 2007, principal component analysis (PCA) was recently suggested as a fast and effective way to diagnose population structure (Price et al. 2006;Patterson et al. 2007). The PCA method summarizes variation observed a...

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“…Modes of selection of predisposing SNPs on the basis of the most extreme P values (as often done in GWAS) has also been criticized, as P values indicate the probability of finding such an event, not its biological importance. As an example of this, PPARG would probably have been discarded in the later GWAS if its association with T2DM had not been previously shown by candidate-gene studies [15,16].…”

Section: Methodological Aspectsmentioning

confidence: 99%

“…But some criticisms have also been raised against GWAS [15,16]. The GWAS for T2DM have resulted in a small number of genetic associations (overall 5 GWAS on T2DM have resulted in 9-10 confirmed risk loci), often associated with a modest risk, within a context of ''complex'' multifactorial diseases.…”

Section: Methodological Aspectsmentioning

confidence: 99%

Sideways Glance: Genome wide association studies for type 2 diabetes mellitus

Sambuy

2007

Genes Nutr

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Problems with Genome-Wide Association Studies

Cited by 89 publications

References 20 publications

The success of the genome-wide association approach: a brief story of a long struggle

The success of the genome-wide association approach: a brief story of a long struggle

Nonmetric Multidimensional Scaling Corrects for Population Structure in Association Mapping With Different Sample Types

Sideways Glance: Genome wide association studies for type 2 diabetes mellitus

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