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:

Colocalization of GWAS and eQTL Signals Detects Target Genes

Farhad Hormozdiari recently developed a method for combining genome-wide association studies (GWASs) and quantitative trait loci (eQTL) studies in a statistical framework that quantifies the probability of each variant to be causal while allowing an arbitrary number of causal variants. Together with collaborators at the University of Oxford and Broad Institute of MIT and Harvard, we present a paper in The American Journal of Human Genetics. Here, we describe eQTL and GWAS CAusal Variants Identification in Associated Regions (eCAVIAR). We apply our approach to datasets from several GWASs and eQTL studies in order to assess its accuracy and potential contributions to colocalization and fine-mapping.

Integrating GWASs and eQTL studies is a promising way to explore the mechanism of non-coding variants on diseases. Integration of GWAS and eQTL data is challenging due to the uncertainty induced by linkage disequilibrium (LD), the non-random association of alleles at different loci, and presence of loci that harbor multiple causal variants (allelic heterogeneity). Current methods assume that each locus contains a single causal variant and expect loci to be independent and associated randomly.

eCAVIAR is a novel probabilistic model for integrating GWAS and eQTL data that extends the CAVIAR (Hormozdiari et al. 2014) framework to explicitly estimate the posterior probability of the same variant being causal in both GWAS and eQTL studies, while accounting for allelic heterogeneity and LD. Our approach can quantify the strength between a causal variant and its associated signals in both studies, and it can be used to colocalize variants that pass the genome-wide significance threshold in GWAS. For any given peak variant identified in GWAS, eCAVIAR considers a collection of variants around that peak variant as one single locus.

We apply eCAVIAR to the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) dataset and GTEx dataset to detect the target gene and most relevant tissue for each GWAS risk locus. When applied to the MAGIC dataset’s 2 phenotypes, eCAVIAR identifies genetic variants that are causal in both eQTL and GWAS. Further, eCAVIAR detects a large number of loci where the GWAS causal variants are clearly distinct from the causal variants in the eQTL data. Interestingly, eCAVIAR also identifies genes that colocalize in one tissue yet can be excluded in others. For the majority of loci in which we identify a single variant causal for both GWAS and eQTL, eCAVIAR implicates more than one causal variant across the 45 tissues.

We observe that eCAVIAR outperforms existing methods even when there are different values of non-colocalization. Using simulated datasets, we compared accuracy, precision, and recall rate of eCAVIAR to RTC (Nica et al. 2010) and COLOC (Giambartolomei et al. 2014), two current methods for eQTL and GWAS colocalization. Our results show that eCAVIAR has high confidence for selecting loci to be colocalized between the GWAS and eQTL data and is conservative in selecting a locus to be colocalized.

We hope that future applications of eCAVIAR will advance identification of specific GWAS loci that share a causal variant with eQTL studies in a tissue, thus providing insight into presently unclear disease mechanisms.


Overview of eCAVIAR.


eCAVIAR was created by Farhad Hormozdiari, Ayellet V. Segre, Martijn van de Bunt, Xiao Li, Jong Wha J Joo, Michael Bilow, Jae Hoon Sul, Bogdan Pasaniuc and Eleazar Eskin. The article is available at:

Visit the following page to download CAVIAR and eCAVIAR:

The full citation to our paper is:

Hormozdiari, Farhad; van de Bunt, Martijn; Segrè, Ayellet V; Li, Xiao; Joo, Jong Wha J; Bilow, Michael; Sul, Jae Hoon; Sankararaman, Sriram; Pasaniuc, Bogdan; Eskin, Eleazar

Colocalization of GWAS and eQTL Signals Detects Target Genes. Journal Article

In: Am J Hum Genet, 2016, ISSN: 1537-6605.

Abstract | Links | BibTeX

Our paper builds upon 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

Chromosome conformation elucidates regulatory relationships in developing human brain

Farhad Hormozdiari, a recent ZarLab alumni, contributed to a paper published this week in Nature. Our paper reports new findings on genetic factors related to human cognition and neurodevelopmental disorders, the result of a collaboration with UCLA’s David Geffen School of Medicine and the School of Biotechnology and Biomolecular Sciences at University of New South Wales. Farhad implemented the software package CAVIAR which was utilized to identify the causal variants and interpretation of data.

Neurodevelopmental disorders such as autism and schizophrenia are thought to originate during embryonic development of the cerebral cortex. The project focused on the 3D interactions of genome-wide chromatin contacts, the areas of a cell’s nucleus that package chromosomes into DNA and influence cell replication. Chromatin contacts regulate gene expression in specific tissues, and mapping their interactions within chromosomes provides important biological insights into the malfunctioning gene regulatory mechanisms that drive these disorders.

The project generated high-resolution 3D maps of chromatin contacts active during development of the cortex region of the human brain. These maps enabled a large-scale annotation of previously uncharacterized regulatory mechanisms tied to the evolution of human cognition and disease. Using this data, the paper identified hundreds of genes involved with human cognitive function. Next, the paper integrated chromatin contacts with noncoding variants previously identified in schizophrenia genome-wide association studies (GWAS) and performed several analyses to explore the relationships of interactions between chromatin and biological function.  One of the uses of CAVIAR in the paper was to verify that the causal variants involved in schizophrenia GWAS are in fact compatible with the 3D maps of chromatin contacts.

The paper also found several highly interacting chromatin regions that correlate with levels of gene expression and are associated with promoters, positive transcriptional regulators, and enhances—areas of the genome that shape cell replication and neurological development. The paper identified specific sets of genes enriched in known intellectual disability risk genes, including mutations known to cause autosomal recessive primary microcephaly. The GWAS results identified approximately 500 genome-wide significant schizophrenia-associated loci, about 30% of which interact with schizophrenia SNPs exclusively in developing brain tissue. Genome editing in human neural progenitors suggests that one of these distal schizophrenia GWAS loci regulates FOXG1 expression, supporting its potential role as a schizophrenia risk gene.

This work provides a framework for understanding the effect of non-coding regulatory elements on human brain development and the evolution of cognition, and highlights novel mechanisms underlying neuropsychiatric disorders. Read the paper for a detailed account of our data, methods, and results:

The CAVIAR program was developed by Farhad Hormozdiari and is freely available for download on the following webpage:

The full citation to our paper is:

Won, Hyejung; de la Torre-Ubieta, Luis; Stein, Jason L; Parikshak, Neelroop N; Huang, Jerry; Opland, Carli K; Gandal, Michael J; Sutton, Gavin J; Hormozdiari, Farhad; Lu, Daning; Lee, Changhoon; Eskin, Eleazar; Voineagu, Irina; Ernst, Jason; Geschwind, Daniel H

Chromosome conformation elucidates regulatory relationships in developing human brain. Journal Article

In: Nature, 538 (7626), pp. 523-527, 2016, ISSN: 1476-4687.

Abstract | Links | BibTeX



Annotation of schizophrenia-associated loci identified by a GWAS of chromatin contact data.