You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Genome-Wide Association of Bipolar Disorder Suggests an Enrichment of Replicable Associations in Regions near Genes}
Article:
Abstract:
Although a highly heritable and disabling disease, bipolar disorder's( BD) genetic variants have been challenging to identify. We present new genotype data for 1, 190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2, 191 cases and 1, 434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication( P = 1. 5×10−7). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons( ±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies.
Introduction:
Genome-wide association studies( GWAS) have been responsible for the elucidation of hundreds of loci associated with common human diseases, in some cases aiding in the prediction of individual disease susceptibility, but primarily allowing for a better biological understanding of disease[1],[2]. Although effect sizes of associated variants identified in these studies have been small to modest, it has been suggested that many more loci of even smaller effect may be detected with larger datasets[3] based on the distribution of associated variant frequencies and effect sizes and, in the case of height, these non-significant effects can add up to a large proportion of the variance explained[4]. This has been borne out with recent GWAS using hundreds of thousands of individuals[5]–[7]. A challenge is determining whether additional samples are worth genotyping for common variation when initial results with modest sample sizes do not result in genome-wide significant effects. Here, we analyze effects across multiple GWAS with sub-significant P-values to determine whether there is a true underlying genetic signal tagged by common variation present across studies. Since true effects will tend to replicate across studies as a function of power, we can test the hypothesis that there is an underlying genetic signal for a trait by testing whether replication of an association with a variant across studies is a function of the power to detect that variant based on that variant's frequency and effect size as determined from a single study. It has been suggested as an alternative to a Bonferroni-based approach to genome-wide significance that P-values be interpreted in the context of power[8]. We assume that the association statistics in a previous study provide prior information about the potential for replicability of associations, by estimating the power to detect SNP effects from the frequency and effect sizes determined in an initial data set on a SNP-by-SNP basis and then testing associations with those SNPs in a test data set, and can thereby focus on the variation that is most likely to be truly associated. Bipolar disorder( BD) is a major psychiatric disorder affecting approximately 1% of the population. Patients with BD suffer extreme mood swings between mania and depression, and 17% suicide. BD is highly heritable, but has not easily yielded genetic loci from family and population based mapping strategies[9],[10]. Multiple genome-wide association studies[11]–[14] have highlighted compelling candidates for BD without reaching genome-wide significance. ANK3 and CACNAC1 were identified at genome-wide significance through the combination of multiple GWAS[15], and a meta-analysis identified a region at 3p21. 1 associated with a combined sample of individuals with BD or major depressive disorder[16]. Intriguing, however, are results that suggest a shared polygenic basis with schizophrenia, with effects over many loci, each contributing a small effect[17]. By considering the development and application of a multilocus schizophrenia-based genetic risk ‘score’ across many SNPs – many that were not significant in a single locus analysis – Purcell et al.[17] were able to predict BD case-control status with a non-zero probability, indicating a probable polygenic basis for Schizophrenia. In this work, we assess the relationship between the power to detect a SNP based on association statistics observed in one study and replication in another study. We assess this relationship in a way that allows for the assessment of collections of SNPs in defined genomic regions in order to test hypotheses about the nature of genetic variation mediating BD susceptibility. We apply this strategy to new BD GWAS data to show that it is possible to identify replicable genetic signals in circumscribed regions of the genome that would not be captured by single locus analyses in a single GWAS data set at genome-wide significance levels. Essentially, we assess the consistency of effects at different loci across studies by calculating the power to detect an effect in a “discovery” study and comparing the results to observed associations in a “test” study. Power encompasses both allele frequency and penetrance or effect size, and therefore is a single measure of the likelihood of replication. If there is a true underlying signal common to the two datasets that is tagged by common variation interrogated in the genotyping chips used in the studies, then one would expect to see greater evidence for replication at loci for which there is greater power to detect an effect. If there were no true underlying signal, one would expect to see no association between power calculated from an initial study's findings and replication in an independent data set. Because we characterize trends across many SNPs without identifying individually significant effects, this approach has similarities to the false discovery rate[18] approach, but uses an unrelated study to prioritize markers.
Discussion:
We report a complementary approach to standard meta-analysis when there is an existing, unrelated study that can be leveraged to assess the consistency of effects across studies. This analysis does not specify which SNPs are associated, but investigates trends among the SNPs and their association strengths. By analyzing results in the context of what one study suggests is the power to detect effects in another study, association signals likely to be of functional significance can be better partitioned. Using an analysis of height GWAS data as a proof-of-principle, we showed that with enough samples in the discovery data set there was an almost linear relationship between replication P-values and power based on variant and effect size information obtained in a separate sample with a slope of 1. However, we did not see as strong of an effect with BD. This could be due to the small sample size in WTCCC-BD, relative to the height data we had at our disposal; the association may increase with sample size. In this analysis, we assume that the same variation that is likely to be causal in one study is likely to be causal in the other, and that both studies have similar linkage disequilibrium structure. Population-specific variation or variation that has population-dependent effects would not be expected to replicate to the same extent. Phenotypic heterogeneity across populations, which may be more of a challenge for psychiatric diseases than it is for height, will also contribute to low levels of replication. In the case of heterogeneity, one would expect that the relationship between replication and power would not approach a slope of 1 as sample size increases. Although we use common variation to tag these associations, the underlying functional variation may be rare or common, as collections of rare variants of stronger effect can produce an association signal consistent with observed effect sizes[21]. Further studies that include deep sequencing would be required to identify these variants. As applied to BD, this analysis supports the presence of replicable and potential functional variation associated with BD that is enriched in regions near genes. However, the enrichment signal was not present within exons but rather was observed only when regions up to 10 kb around exons were included in the analysis. This suggests that sequencing of individuals with BD should include noncoding regions near genes. The model of polygenic inheritance suggests that there are many loci throughout the genome, each with small effect, that influence phenotypic expression. Our result for BD does not rule out the model of many loci, but suggests that for this disease, truly associated variation may be enriched near genes. Thus, the genetic architecture of BD that is tagged by common variation does not appear to be evenly distributed throughout the genome, but may reside in circumscribed regions. Many recent studies have been pursued to better understand if non-genome-wide significant variation can still be considered to harbor phenotypically-relevant information. For example, Yang et al.[4] quantified variation explained by all SNPs by fitting a single regression model that included thousands of variants in order to assess the collective effect of these variants on height and estimated the variation in height explained by these variants. Park et al.[3] took a different approach and used the distribution of effect sizes at genome-wide significant loci in conjunction with the power to detect those effects to extrapolate the distribution of undetected genetic loci. Our approach differs from these methods in a number of ways. First, we do not require individual level data and only require summary level results, which can obviate the need for individual data use restrictions. Second, we do not require genome-wide significant results to estimate a true effect size distribution, which is helpful when there are few to no significant associations. Third, because the replication vs. power relationship approaches a slope of 1 when the effect sizes are real and reproducible, this approach provides and alternative framework for understanding the extent of the reliability of signals even when they are not genome-wide significant. Given the number of inconclusive GWAS with marginal results in need of interpretation, we feel that the approach described in this paper provides an important tool for assessing whether there is an underlying genetic basis for a phenotype and/or whether additional samples might be needed to detect genetic associations. Studies that are too underpowered to detect any replication signal, however, may require additional samples before applying this approach. Additionally, in the context of sequence data, specific groups of variants can be tested for stronger associations between power and replication. The method is easy to apply in the context of a meta-analysis where association results are present for large numbers of SNPs.
Bipolar disorder ( BD ) is a highly heritable disease that has been difficult to characterize genetically . We have genotyped 1 , 190 BD cases and 401 controls to find regions of the genome associated with BD . After combining these data with previously existing genotyped samples , we did not find any genome-wide significant associations . However , when we used an additional study to prioritize loci for replication and meta-analysis purposes , we found that we were more likely to see an association in our sample with variants for which we had the highest power . We quantified this effect using logistic regression and saw a strong association between power to detect an effect based on an initial study's results and replication P-value in a second study ( P = 1 . 5×10−7 ) , supporting the presence of shared genetic risk factors across the studies . Moreover , this association was stronger when we restricted analysis to SNPs near coding regions , and it was further enriched when SNPs had the same direction of effect in both studies . This result supports the presence of genetic factors underlying BD near exons whose collective effect results in a detectable signal and provides a framework for assessing the potential for replication when combining results from multiple studies .