In the analysis of genome-wide association (GWA) data, the aim is to detect statistical associations between single nucleotide polymorphisms (SNPs) and the disease or trait of interest. These SNPs, or the particular regions of the genome they implicate, are then considered for further study. We demonstrate through a comprehensive simulation study that the inclusion of additional, biologically r...

The heading date is a vital factor in achieving a full rice yield. Cultivars with particular flowering behaviors have been artificially selected to survive in the long-day and low-temperature conditions of Northeast China. To dissect the genetic mechanism responsible for heading date in rice populations from Northeast China, association mapping was performed to identify major controlling loci. ...

The genetic basis of sporadic colorectal cancer (CRC) is not well explained by known risk polymorphisms. Here we perform a meta-analysis of two genome-wide association studies in 2,627 cases and 3,797 controls of Japanese ancestry and 1,894 cases and 4,703 controls of African ancestry, to identify genetic variants that contribute to CRC susceptibility. We replicate genome-wide statistically sig...

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Over the last few years, many new genetic associations have been identified by genome-wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction u...

In this chapter, we will attempt to outline the current state of genetic knowledge for bipolar disorder and briefly summarize the main findings from genetic epidemiology studies. We then review the most recent original literature, based largely on genome-wide association study methods. We conclude with some ideas about future directions.

The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies w...

Genome-wide association studies (GWAS) have detected many disease associations. However, the reported variants tend to explain small fractions of risk, and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease. Studying the degree of sharing of disease-ass...

Pathway-based analysis is rapidly emerging as an alternative but powerful approach for searching for disease causal genes from genomic datasets and has been applied to many complex diseases recently, but it is only now beginning to be applied in psychiatry. Here, we discuss critical issues in the pathway-based approach by specifically comparing the first pathway analysis of genome-wide associat...

In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are t...