Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

Hae Kyung Im and colleagues report a method for predicting gene expression perturbations from genotype data after training on reference transcriptome data sets. Association of predicted gene expression with disease traits identifies known and new candidate disease genes. Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10⁻⁴–10⁻⁸), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits. We show here that single nucleotide polymorphisms (SNPs) associated with complex traits (as identified in the catalog of results from genome-wide association studies http://www.genome.gov/gwastudies/) are more likely than other SNPs chosen from high-throughput genotyping platforms to predict expression levels of genes. These observations confirm that genetic risk factors for complex traits will often affect phenotype by altering the amount or timing of protein production, rather than by changing the type of protein produced. This knowledge can be used to improve our ability to discover genetic risk factors for complex traits and to improve our understanding of their underlying biology.

Obesity-associated noncoding sequences within FTO are functionally connected with IRX3, and long-range enhancers in this region recapitulate aspects of IRX3 expression, suggesting that the obesity-associated interval is part of IRX3 regulation; Irx3-deficient mice have lower body weight and are resistant to diet-induced obesity, suggesting IRX3 as a novel determinant of body mass and composition. The search for genetic correlates of obesity has highlighted a noncoding region in the FTO gene: variations within this intron are associated with increased risk for obesity and type 2 diabetes. Although the biological actions of FTO have been intensely scrutinized, it is still not clear how these genetic variants influence FTO expression and biology. This paper shows that these noncoding sequences are functionally connected — at megabase distances — with the homeobox gene IRX3. The obesity-associated interval appears to belong to the regulatory functions of IRX3, rather than FTO. In addition, mice lacking Irx3 have reduced body weight and are resistant to diet-induced obesity. Taken together, the data suggest that IRX3 is an important metabolic regulator associated with human obesity and type 2 diabetes. Genome-wide association studies (GWAS) have reproducibly associated variants within introns of FTO with increased risk for obesity and type 2 diabetes (T2D)1,2,3. Although the molecular mechanisms linking these noncoding variants with obesity are not immediately obvious, subsequent studies in mice demonstrated that FTO expression levels influence body mass and composition phenotypes4,5,6. However, no direct connection between the obesity-associated variants and FTO expression or function has been made7,8,9. Here we show that the obesity-associated noncoding sequences within FTO are functionally connected, at megabase distances, with the homeobox gene IRX3. The obesity-associated FTO region directly interacts with the promoters of IRX3 as well as FTO in the human, mouse and zebrafish genomes. Furthermore, long-range enhancers within this region recapitulate aspects of IRX3 expression, suggesting that the obesity-associated interval belongs to the regulatory landscape of IRX3. Consistent with this, obesity-associated single nucleotide polymorphisms are associated with expression of IRX3, but not FTO, in human brains. A direct link between IRX3 expression and regulation of body mass and composition is demonstrated by a reduction in body weight of 25 to 30% in Irx3-deficient mice, primarily through the loss of fat mass and increase in basal metabolic rate with browning of white adipose tissue. Finally, hypothalamic expression of a dominant-negative form of Irx3 reproduces the metabolic phenotypes of Irx3-deficient mice. Our data suggest that IRX3 is a functional long-range target of obesity-associated variants within FTO and represents a novel determinant of body mass and composition.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Detangling gene-disease connections: Many diseases are at least partially due to genetic causes that are not always understood or targetable with specific treatments. To provide insight into the biology of various human diseases as well as potential leads for therapeutic development, Pietzneret al. undertook detailed, genome-wide proteogenomic mapping. The authors analyzed thousands of connections between potential disease-associated mutations, specific proteins, and medical conditions, thereby providing a detailed map for use by future researchers. They also supplied some examples in which they applied their approach to medical contexts as varied as connective tissue disorders, gallstones, and COVID-19 infections, sometimes even identifying single genes that play roles in multiple clinical scenarios. —YN