Tag: GWAS

Incorporating prior information into association studies

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Genome-wide association studies (GWAS) seek to identify genetic variants involved in specific traits. GWAS are advantageous for linking variants with traits, because they interrogate the genome in a uniform way. In other words, they examine the whole genome without a preconceived notion of where the associations may lie.

However, we now know a lot about the putative function of genetic variants due to tremendous progress in functional genomics. In many cases, we even know which variants are more likely to be involved in disease when compared to others. Advancements in our understanding of functional genomics motivate the strategic incorporation of prior information in GWAS.

Our group has been interested in this problem for many years. One challenge to addressing this problem is that the widely utilized approach for GWAS involves evaluating an association statistic at each single nucleotide polymorphism (SNP), and these methods take into account only one SNP at a time. The results are then adjusted for multiple testing, and an association is identified if a statistic exceeds a certain threshold. This approach can be described as a frequentist approach. On the other hand, one can incorporate prior information on which SNPs are likely to be the causal variants affecting the trait. This approach is inherently a Bayesian concept. Reconciling these two approaches is not straightforward.

Average power under varying relative risks. For more information, see our paper.

In a 2008 paper published in Genome Research, our group proposed a modification of the multiple testing framework to address this problem. Instead of using the same specific threshold for all of the association statistics, we use a different threshold for each association statistic, where the thresholds are adjusted based on the prior information. Our method takes advantage of the correlation structure by considering multiple markers within a region. In our paper, we demonstrate how to set the thresholds in order to optimally utilize prior information and maximize statistical power.

Using prior information in genetic association studies increases power over traditional association studies while maintaining the same overall false-positive rate. Compared to standard methods, our approach is equally simple to apply to association studies, produces interpretable results as p-values, and is optimal in its use of prior information in regards to statistical power.

In 2012, we extended this work to use only tag SNPs for the putative causal variant. This project was developed by Gregory Darnell (then UCLA undergraduate, now PhD student at Princeton University), Dat Duong (then UCLA undergraduate, now UCLA PhD student), and Buhm Han.

More recently, we have applied this framework to incorporate functional information in analysis of eQTL data. In this case, incorporating genomic annotation of variants significantly increases the statistical power of existing eQTL methods and detects more eGenes in comparison to standard approaches. Read the blog post on this paper, and download the full article.

For more information on our general approach, see our paper, which is available for download through Bioinformatics:
https://academic.oup.com/bioinformatics/article/28/12/i147/269880/Incorporating-prior-information-into-association
In addition, the open source implementation of our 2012 paper, MASA, which was developed by Greg Darnell and Dat Duong, is freely available for download at http://masa.cs.ucla.edu/.

The full citations to our papers on this topic are:

Sorry, no publications matched your criteria.


Eleazar Eskin. “Increasing Power in Association Studies by using Linkage Disequilibrium
Structure and Molecular Function as Prior Information.” Genome Research.
18(4):653-60 Special Issue Proceedings of the 12th Annual Conference on Research
in Computational Biology (RECOMB-2008), 2008.

Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder

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Variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits. Genome-wide characterization of gene expression is an important tool in genetic mapping studies of complex disorders, including many psychiatric disorders. Further, implicating eQTL to specific tissue types is key to understanding functional variation in disease development. Our group, in collaboration with Chiara Sabatti (Statistics, Stanford) and Nelson B. Freimer (David Geffen School of Medicine, UCLA), developed a novel approach for analyzing eQTL and applied the method to a dataset from a bipolar disorder study.

Current approaches to implicating eQTL specific to tissues lack sufficient power in large-scale studies of human brain related traits, such as bipolar disorder. Together with the University of California San Francisco, Universidad de Costa Rica, Universidad de Antioquia, Medellín, Colombia, and Tel Aviv University, our group adopted a novel approach to assess the heritability and genetic regulation of gene expression related to bipolar disorder in populations from Costa Rica and Colombia.

This project examines 786 genotyped subjects originally recruited in a study of bipolar disorder, all related within 26 extended families. While the subjects in this study were originally recruited as part of an investigation for severe bipolar disorder (BP1), we found no relationship between the observed gene expression data and BP1. Instead, we use this unique Latin American population to explore the architecture of genetic regulation. Specifically, we estimate heritability, evaluate the relative importance of local vs. distal genomic variation, identify variants with regulatory effects, and analyze the role of multiple associated SNPs in the same region.

Our group adopted a novel hierarchical testing procedure that leads to the analysis of eQTL data in a stage-wise manner with increasing levels of detail. This design allows us to compare estimates of the heritability of gene expression obtained using both traditional and genotype-based methods. First, we apply a multiscale testing strategy to identify SNPs that have regulatory effects (eSNPs) on BP1. Second, we investigate which specific probes are influenced by these eSNPs. This hierarchical testing procedure effectively controls error rates and leverages the heterogeneity across genetic variants to preserve computational power.

We use this approach to measure gene expression in lymphoblastoid cell lines (LCLs) in subjects from extended families, segregating for BP1. Our results suggest that variation in expression values is heritable and that, at least in samples including related individuals, relying on theoretical kinship coefficients or on realized genotype correlation for estimation of heritability leads to similar results.

Expression heritability and proportion of genetic variance due to local effects. For more information, see our paper. For more information, see our paper.

Variance decomposition approaches suggest that on average 30% of the genetic variance is due to local regulation. In the majority of probes under local regulation in our sample, more than one typed SNP is required to account for expression variation. This finding can be interpreted as the result of heterogeneity, but also could reflect un-typed causal variants that are tracked by more than one typed SNP.

The knowledge we acquired by studying the genetic regulatory network within these pedigrees, instead, can be used to inform our mapping studies: eSNPs might receive a higher prior probability of association, or be assigned a larger portion of the allowed global error rate when using a weighted approach to testing. We will report elsewhere on the results of these investigations.

For more information, see our paper, which is available for download through PLoS Genetics: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006046.

The full citation to our paper is: 

Sorry, no publications matched your criteria.

Peterson, C.B., Jasinska, A.J., Gao, F., Zelaya, I., Teshiba, T.M., Bearden, C.E., Cantor, R.M., Reus, V.I., Macaya, G., López-Jaramillo, C. and Bogomolov, M., 2016. Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder. PLoS Genet, 12(5), p.e1006046.

 

Simultaneous modeling of disease status and clinical phenotypes to increase power in GWAS

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Michael Bilow and Eleazar Eskin, together with Fernando Crespo, Zhicheng Pan, and Susana Eyheramendy, recently released a novel method for accurate joint modeling of clinical phenotype and disease status. This approach incorporates a clinical phenotype into case/control studies under the assumption that the genetic variant can affect both.

Genetic case-control association studies have found thousands of associations between genetic variants and disease. Most studies collect data from individuals with and without disease, and they often search for variants with different frequencies between the groups. Jointly modelling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this method increases potential for discovering genetic variants that are associated with both a clinical phenotype and a disease.

However, standard multivariate techniques fail to effectively solve this problem because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case/control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate.

In our paper, we show the utility of our method using data from the North Finland Birth Cohort (NFBC) dataset. NFBC enrolled almost everyone born in 1966 in Finland’s two most northern provinces. The NFBC dataset consists of 10 phenotypes and genotypes at 331,476 genetic variants measured in 5,327 individuals. We focus our study on the LDL cholesterol and triglyceride levels phenotypes.

Our evaluation strategy analyzes a subset of the NFBC data and compares what we discover here to what was discovered in the full NFBC dataset—which we treat as the gold standard. We compare the performance of our novel approach to three other methods: (1) the single univariate test applied to the disease status, (2) the multivariate approach applied to the disease status and the clinical phenotype modeled as a multivariate normal distribution, and (3) the liability threshold model treating the clinical phenotype as a covariate.

Using the univariate approach, the p-values are much weaker in comparison to those observed in the full NFBC dataset. Running the multivariate approaches, incorporating the triglyceride levels phenotypes, increased power (i.e., more significant p-values than SNPs).

Our method has the highest power in all scenarios. The advantage of our method is greater when there are substantial amounts of selection bias compared to lower amounts of selection bias. Our method is even more powerful when the correlation between the clinical covariate and the disease liability is lower, because we explicitly estimate the underlying liability using all of the data.

For more information, see our paper in Genetics: http://www.genetics.org/content/early/2017/01/27/genetics.116.198473

The software implementing the methods described in this paper was developed by Fernando Crespo and is available at: http://genetics.cs.ucla.edu/multipheno/ and
https://github.com/facrespo/BivariateProbitContinueEM

An illustration of the distribution of liability in a case-control study under selection bias. For more information, read our paper.

The full citation to our paper is:
Bilow, M., Crespo, F., Pan, Z., Eskin, E. and Eyheramendy, S., 2017. Simultaneous Modeling of Disease Status and Clinical Phenotypes to Increase Power in GWAS. Genetics, pp.genetics-116.