Selection in Europeans on Fatty Acid Desaturases Associated with Dietary Changes

Farhad Hormozdiari and Eleazar Eskin recently applied an extension of CAVIAR to assess signal selection in European ancestry. CAVIAR is a probabilistic method for detecting a confidence set of SNPs containing all the causal variants in a locus that are within a predefined probability (e.g., 90% or 95%)—while taking into account biases generated by linkage disequilibrium. Farhad, now a post-doctoral scholar at Boston University, developed CAVIAR while a PhD student at UCLA.

This project was led by Matthew T. Buckley and Fernando Racimo at the University of California, Berkeley, and Morten E. Allentoft at the University of Copenhagen. Alleles with strong selection signals have been recently selected for and are thought to carry an evolutionary advantage for individuals in the population. Identifying these alleles helps expand our understanding of the selective pressures that shaped historic populations.

Allele frequency changes across FADS region. For more information, see our full paper.

In order to analyze the selective processes in Europeans across space and time, the project compared sequencing data from FADS genes obtained from present-day and Bronze Age (5000 to 3000 years ago) Europeans. We focused on FADS genes because prior studies indicate they are subjected to strong positive selection in Africa, South Asia, Greenland, and Europe. FADS genes encode fatty acid desaturases that are important for the conversion of short chain polyunsaturated fatty acids (PUFAs) to long chain fatty acids. In other words, selective pressure in the FADS genes may be linked to dietary adaptations.

Other analyses conducted by the project show that alleles in the FAD2 gene display the strongest changes in allele frequency since the Bronze Age, and this change shows associations with expression changes and multiple lipid-related phenotypes. Farhad and Eleazar used CAVIAR to look for presence of allelic heterogeneity, an adaptive process in which different mutations at the same locus cause the same phenotype. In an evolutionary context, presence suggests that a strong pressure selective pressure likely acted upon the population.

Application of CAVIAR to genomic data from the 1000 Genomes Project and 54 Bronze Age Europeans revealed that specific causal variants within the FADS2 gene have been subjected to selective pressure. In particular, FADS2 shows evidence of allelic heterogeneity in three tissue types: transformed fibroblast cells (Pr(2 causal variants) = 0.72), left heart ventricle (Pr(2 causal variants) = 0.74), and whole blood (Pr(3 causal variants) = 0.74).

The project’s comparison of modern to Bronze Age European genomic data show that selection has indeed strongly acted on the FADS gene cluster over the past 3000 years. The selective patterns observed in European data may be driven by a change in the dietary composition of fatty acids following the human transition from hunting-and-gathering to agriculture. As Europeans obtained more lipids from plants, rather than from fish and mammals, their genes adapted to optimize metabolism of these cereal-based lipids.

For more information, see our paper, which is available for download through Molecular Biology and Evolution:

The full citation to our paper is: 

Buckley, M.T., Racimo, F., Allentoft, M.E., Jensen, M.K., Jonsson, A., Huang, H., Hormozdiari, F., Sikora, M., Marnetto, D., Eskin, E. and Jørgensen, M.E., 2017. Selection in Europeans on fatty acid desaturases associated with dietary changes. Molecular biology and evolution.

This project used a method introduced in a previous publication: 

Hormozdiari, Farhad; Kostem, Emrah ; Kang, Eun Yong ; Pasaniuc, Bogdan ; Eskin, Eleazar

Identifying causal variants at Loci with multiple signals of association. Journal Article

In: Genetics, 198 (2), pp. 497-508, 2014, ISSN: 1943-2631.

Abstract | Links | BibTeX

CAVIAR was created by Farhad HormozdiariEmrah KostemEun Yong KangBogdan Pasaniuc, and Eleazar Eskin. Visit the following page to download CAVIAR and eCAVIAR:

Incorporating prior information into association studies

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:
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

The full citations to our papers on this topic are:

Darnell, Gregory; Duong, Dat ; Han, Buhm ; Eskin, Eleazar

Incorporating prior information into association studies. Journal Article

In: Bioinformatics, 28 (12), pp. i147-i153, 2012, ISSN: 1367-4811.

Abstract | Links | BibTeX

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

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:

The full citation to our paper is: 

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.