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:

Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes

In a recent project, Farhad Hormozdiari and Eleazar Eskin contributed data analysis and interpretation to a project identifying new genes and genomic regions associated with metabolic function in mice. Our paper presents a comprehensive picture of the transcriptome of the mouse hypothalamus and its genetic variation and regulation. This project, which was published in eLife, was led by fellow UCLA researchers Yehudit Hasin-Brumshtein, Jake Lusis, and Desmond Smith.

Mice and humans share virtually the same set of genes; thus, mapping the mouse genome is an important step toward understanding genetic factors in common, complex human diseases such as obesity, heart disease, and diabetes. In metabolic tissues, the integration of genome-wide expression profiles with genetic and phenotypic variance can provide valuable insight into a disease’s underlying molecular mechanism. Measuring gene activity can reveal new molecules that clinical translation efforts may target to treat metabolic disorders.

Our project uses RNA-Seq to characterize transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. Mice were fed a high, high sugar diet, and all strains were comprehensively genotyped and phenotyped for 150 metabolic traits. Our study examines tissues relevant to the hypothalmus, the brain region that controls metabolism and regulates body weight and appetite.

We sequenced 285 samples from all 99 strains of the HMDP. Using methods described in our paper, we identified thousands of new isoforms and >400 new genes. The HMDP allowed us to map Quantitative Trait Loci (eQTLs) with high resolution and power, identifying both local and trans acting variants—or, variants that affect a molecule from within and from outside, respectively.

Groups of genes are associated with multiple related phenotypes in HMDP, although not necessarily enriched for GO ontology or specific pathways. For more information, see our paper.

We report numerous novel transcripts supported by proteomic analyses, as well as novel non-coding RNAs. High resolution genetic mapping of transcript levels in HMDP reveals both local and trans expression eQTLs, identifying two trans eQTL ’hotspots’ associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. We further showed that the genes associated with trans eQTL hotspots correlate to physiological phenotypes, such as HDL and triglyceride levels. This discovery provides insight into the mechanism behind correlation of these genotypes with complex traits.

Our data capture the various non-neuronal cell types, such as microglia or astrocytes, which are often overlooked in the mostly neuron focused studies of the hypothalamus. These cells are important mediators of hypothalamic inflammation and other processes induced by a high fat diet. Regulation of gene expression in these cell types impacts every aspect of metabolism, and our data provide a robust framework recapitulating transcriptional processes affecting multiple cell populations. Our approach is thus complementary to on-going cell type-specific transcriptomic efforts.

For more information, see our paper, which is available for download through eLife:

The full citation to our paper is: 

Hasin-Brumshtein, Yehudit; Hormozdiari, Farhad ; Martin, Lisa ; van Nas, Atila ; Eskin, Eleazar ; Lusis, Aldons J; Drake, Thomas A

Allele-specific expression and eQTL analysis in mouse adipose tissue. Journal Article

In: BMC Genomics, 15 (1), pp. 471, 2014, ISSN: 1471-2164.

Abstract | Links | BibTeX

See our blog post on a recent paper reviewing the HMDP data set:

Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure

Jong Wha (Joanne) Joo developed an approach to simultaneously analyze multiple phenotypes in a genome-wide association studies (GWAS) dataset. She introduces this new methodology, referred to as GAMMA (Generalized Analysis of Molecular variance for Mixed model Analysis), in a paper recently published in Genetics.

GWASs have identified many genetic variants involved in traits and development of human diseases by examining for correlation of a single phenotype and individual genotype one phenotype at a time. Since initial development of the standard GWAS approach, GWAS data collection has become larger in scale and higher in resolution. Today’s large-scale datasets include expression data and often contain thousands of phenotypes per individual. Performing the standard single-phenotype analysis on these datasets is slow and potentially fails to detect unmeasured aspects of complex biological networks.

Analyzing many phenotypes simultaneously increases the power to detect more variants and capture previously unmeasured aspects of the genome. However, standard GWAS approaches capable of simultaneously testing multiple phenotypes fail to account for the distorting effects of population structure, a phenomenon present in large cohorts that inevitably contain individuals sharing common ancestry from multiple populations. As a result, standard GWAS approaches either fail to detect true effects or produce many false positive identifications.

GAMMA is an efficient, robust approach capable of simultaneously analyzing many phenotypes while correcting for population structure. GAMMA uses the principles behind existing linear mixed models to analyze for many phenotypes simultaneously and a multiple regression technique to correct for population structure.

Joanne’s paper presents the results of testing GAMMA for accuracy in three scenarios: a simulated dataset containing population structure, a yeast dataset containing many trans-regulatory hotspots, and a complex gut microbiome dataset. In the simulated study using data implanted with true population structure effects, GAMMA accurately identifies these true effects without producing false positives. In the simulation with yeast data, GAMMA successfully corrected for the bias of technical artifacts such as batch effects and identified significant signals on most of the putative hotspots. In the third test, Joanne and her team assesses GAMMA’s ability to perform a multiple-phenotypes analysis with microbiome data. Here, results identified nine loci likely to have true biological mechanisms in the taxa.

In each scenario, results of GAMMA were compared to those of the standard t-test, EMMA, and MDMR. The standard t-test and EMMA failed to identify true variants, because the phenotypic effects in each example is smaller than the amount these methods are powered to detect. MDMR produced no significant signals in the yeast dataset and identified many false associations in the simulated and gut microbiome datasets. Both GAMMA and MDMR have sufficient power to detect small association signals in these complex datasets, but only GAMMA successfully corrects for population structure.

This project was led by Joanne Joo and involved Eun Yong Kang and Farhad Hormozdiari. The article is available at:

GAMMA was developed by Joanne Joo, Eun Yong Kang, Elin Org, Nick Furlotte, Brian Parks, Aldons J. Lusis, and Eleazar Eskin. Visit the following page to download GAMMA:

The full citation to our paper is:

Joo, Jong Wha J; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Hormozdiari, Farhad; Lusis, Aldons J; Eskin, Eleazar

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Journal Article

In: Genetics, 204 (4), pp. 1379-1390, 2016, ISSN: 1943-2631.

Abstract | Links | BibTeX

The results of GAMMA and three standard GWAS methods applied to a simulated dataset. The x-axis shows SNP locations and the y-axis shows log10p-value of associations between each SNP and all the genes. Blue arrows show the location of the true trans-regulatory hotspots.