Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues

Personal exome and genome sequencing provides access to loss-of-function and rare deleterious alleles whose interpretation is expected to provide insight into individual disease burden. However, for each allele, accurate interpretation of its effect will depend on both its penetrance and the trait's expressivity. In this regard, an important factor that can modify the effect of a pathogenic coding allele is its level of expression; a factor which itself characteristically changes across tissues. To better inform the degree to which pathogenic alleles can be modified by expression level across multiple tissues, we have conducted exome, RNA and deep, targeted allele-specific expression (ASE) sequencing in ten tissues obtained from a single individual. By combining such data, we report the impact of rare and common loss-of-function variants on allelic expression exposing stronger allelic bias for rare stop-gain variants and informing the extent to which rare deleterious coding alleles are consistently expressed across tissues. This study demonstrates the potential importance of transcriptome data to the interpretation of pathogenic protein-coding variants

Population- and individual-specific regulatory variation in Sardinia

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.M.P. is supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement 633964 (ImmunoAgeing). Z.Z. is supported by the National Science Foundation (NSF) GRFP (DGE- 114747) and by the Stanford Center for Computational, Evolutionary, and Human Genomics (CEHG). Z.Z., J.R.D., and G.T.H. also acknowledge support from the Stanford Genome Training Program (SGTP; NIH/NHGRI T32HG000044). J.R.D. is supported by the Stanford Graduate Fellowship. K.R.K. is supported by Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEQ) Fellowship 32 CFR 168a. S.J.S. is supported by the NIHR Cambridge Biomedical Research Centre. The SardiNIA project is supported in part by the intramural program of the National Institute on Aging through contract HHSN271201100005C to the Consiglio Nazionale delle Ricerche of Italy. The RNA sequencing was supported by the PB05 InterOmics MIUR Flagship grant; by the FaReBio2011 “Farmaci e Reti Biotecnologiche di Qualità” grant; and by Sardinian Autonomous Region (L.R. no. 7/2009) grant cRP3-154 to F. Cucca, who is also supported by the Italian Foundation for Multiple Sclerosis (FISM 2015/R/09) and by the Fondazione di Sardegna (ex Fondazione Banco di Sardegna, Prot. U1301.2015/AI.1157.BE Prat. 2015-1651). S.B.M. is supported by the US National Institutes of Health through R01HG008150, R01MH101814, U01HG007436, and U01HG009080. All of the authors would like to thank the CRS4 and the SCGPM for the computational infrastructure supporting this project

Systematic functional regulatory assessment of disease-associated variants.

Genome-wide association studies have discovered many genetic loci associated with disease traits, but the functional molecular basis of these associations is often unresolved. Genome-wide regulatory and gene expression profiles measured across individuals and diseases reflect downstream effects of genetic variation and may allow for functional assessment of disease-associated loci. Here, we present a unique approach for systematic integration of genetic disease associations, transcription factor binding among individuals, and gene expression data to assess the functional consequences of variants associated with hundreds of human diseases. In an analysis of genome-wide binding profiles of NFκB, we find that disease-associated SNPs are enriched in NFκB binding regions overall, and specifically for inflammatory-mediated diseases, such as asthma, rheumatoid arthritis, and coronary artery disease. Using genome-wide variation in transcription factor-binding data, we find that NFκB binding is often correlated with disease-associated variants in a genotype-specific and allele-specific manner. Furthermore, we show that this binding variation is often related to expression of nearby genes, which are also found to have altered expression in independent profiling of the variant-associated disease condition. Thus, using this integrative approach, we provide a unique means to assign putative function to many disease-associated SNPs

Systematic functional regulatory assessment of disease-associated variants

Genome-wide association studies have discovered many genetic loci associated with disease traits, but the functional molecular basis of these associations is often unresolved. Genome-wide regulatory and gene expression profiles measured across individuals and diseases reflect downstream effects of genetic variation and may allow for functional assessment of disease-associated loci. Here, we present a unique approach for systematic integration of genetic disease associations, transcription factor binding among individuals, and gene expression data to assess the functional consequences of variants associated with hundreds of human diseases. In an analysis of genome-wide binding profiles of NFκB, we find that disease-associated SNPs are enriched in NFκB binding regions overall, and specifically for inflammatory-mediated diseases, such as asthma, rheumatoid arthritis, and coronary artery disease. Using genome-wide variation in transcription factor-binding data, we find that NFκB binding is often correlated with disease-associated variants in a genotype-specific and allele-specific manner. Furthermore, we show that this binding variation is often related to expression of nearby genes, which are also found to have altered expression in independent profiling of the variant-associated disease condition. Thus, using this integrative approach, we provide a unique means to assign putative function to many disease-associated SNPs

Impact Of The X Chromosome And Sex On Regulatory Variation

The unique mode of inheritance and regulatory mechanisms of the X chromosome has resulted in distinct patterns of evolution that shape its genetic architecture and the impact of genetic variation between the sexes. Due to these characteristics, however, the X chromosome has often been excluded from genetic analyses. We characterize the impact of the X chromosome and sex on human regulatory variation through analysis of genetic and gene expression data in a cohort of 922 individuals (whole blood RNA-sequencing from 274 males and 648 females). We identify higher variance in gene expression on the X chromosome compared to the autosomes and that differences in variance are more likely to be sex-specific on X due to the hemizygous exposure of cis regulatory variation in males. Furthermore, we identify that cis-expression quantitative trait loci (e@QTL) have weaker effects and influence fewer genes on the X chromosome compared to the autosomes, especially among genes with strong purifying selection. Despite this, we discover a higher proportion of sex-specific eQTLs on the X chromosome compared to autosomes. To subsequently identify the molecular mechanisms underlying discovered sex-specific eQTLs, we generate and connect sex-specific chromatin accessibility (ATAC-seq) to sex-specific expression and eQTL. Furthermore, as sex-specific eQTL can inform sex-specific effects of genetic variation on disease, we integrate eQTL with genome-wide association study data for multiple immune traits to identify sex-specific effect sizes for multiple trait loci. Together, our study provides a genome-wide understanding of how the X chromosome and sex shape human gene regulation and disease

Correlation of gene expression and allelic ratios across ten somatic tissues.

<p>(<b>A</b>) Shared patterns of gene expression were detected for tissues with shared functional roles or embryonic origins. For example, the small intestine and colon are both digestive system organs derived from the endoderm and have a high degree of pairwise correlation (Spearman Correlation, <i>R</i> = 0.92). Likewise, the frontal lobe and cerebellum, which are both vital tissues nervous system derived from the ectoderm, have a high degree of shared expression (<i>R</i> = 0.91). The hierarchical clustering was generated using pairwise Spearman correlation coefficients of FPKM expression values for all genes. (<b>B</b>) Shared patterns of ASE were detected by mmPCR-Seq. The concordance of ASE between tissues does not as strongly reflect the relationships seen for shared gene expression or shared embryonic origin. The allelic ratio is calculated as the alternate allele reads divided by the total reads. Each data point represents a single heterozygous site tested for ASE with a total read depth greater than 200. The plots are colored by the degree of correlation of allelic bias between the pairwise tissues. These results indicate that relationships of allelic expression across tissues are much more complex than those of total expression level.</p

ASE analysis of rare deleterious nsSNPs and nonsense variants by mmPCR-Seq.

<p>(<b>A</b>) ASE analysis of nonsense variants (red), rare deleterious nsSNPs (blue), and control sites (green) tested by mmPCR-Seq in different tissues. The control sites are random heterozygous sites in the individual's genome. Rare, deleterious nsSNPs and nonsense alleles have significantly reduced expression compared to controls. This observation is most significant for loss-of-function variants where the nonsense allele is likely removed through nonsense-mediated decay (student's t-test, p<0.05, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004304#pgen.1004304.s025" target="_blank">Table S5</a>). (<b>B</b>) ASE analysis of rare (red) and common (pink) nonsense variants tested by mmPCR-Seq data across different tissues. Common nonsense variants are defined as those with a minor allele frequency greater than 5% across the 1000 Genomes population data. Rare nonsense alleles show significantly reduced expression compared to common nonsense alleles (student's t-test, p<0.05).</p

Effect of predicted protein-truncating genetic variants on the human transcriptome

Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants