Katherine Hartmann

Missing heritability of common diseases and treatments outside the protein-coding exome

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the ‘missing heritability’. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an ‘off-well’ state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common ‘normal’ variants remain hidden in the noise. Second, epistasis (dynamic gene–gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene–gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs)

BackgroundOver the past 50,000 years, shifts in human-environmental or human-human interactions shaped genetic differences within and among human populations, including variants under positive selection. Shaped by environmental factors, such variants influence the genetics of modern health, disease, and treatment outcome. Because evolutionary processes tend to act on gene regulation, we test whether regulatory variants are under positive selection. We introduce a new approach to enhance detection of genetic markers undergoing positive selection, using conditional entropy to capture recent local selection signals. Results We use conditional logistic regression to compare our Adjusted Haplotype Conditional Entropy (H|H) measure of positive selection to existing positive selection measures. H|H and existing measures were applied to published regulatory variants acting in cis (cis-eQTLs), with conditional logistic regression testing whether regulatory variants undergo stronger positive selection than the surrounding gene.These cis-eQTLs were drawn from six independent studies of genotype and RNA expression. The conditional logistic regression shows that, overall, H|H is substantially more powerful than existing positive-selection methods in identifying cis-eQTLs against other Single Nucleotide Polymorphisms (SNPs) in the same genes. When broken down by Gene Ontology, H|H predictions are particularly strong in some biological process categories, where regulatory variants are under strong positive selection compared to the bulk of the gene, distinct from those GO categories under overall positive selection. . However, cis-eQTLs in a second group of genes lack positive selection signatures detectable by H|H, consistent with ancient short haplotypes compared to the surrounding gene (for example, in innate immunity GO:0042742); under such other modes of selection, H|H would not be expected to be a strong predictor.. These conditional logistic regression models are adjusted for Minor allele frequency(MAF); otherwise, ascertainment bias is a huge factor in all eQTL data sets. Relationships between Gene Ontology categories, positive selection and eQTL specificity were replicated with H|H in a single larger data set. Our measure, Adjusted Haplotype Conditional Entropy (H|H), was essential in generating all of the results above because it: 1) is a stronger overall predictor for eQTLs than comparable existing approaches, and 2) shows low sequential auto-correlation, overcoming problems with convergence of these conditional regression statistical models.ConclusionsOur new method, H|H, provides a consistently more robust signal associated with cis-eQTLs compared to existing methods. We interpret this to indicate that some cis-eQTLs are under positive selection compared to their surrounding genes. Conditional entropy indicative of a selective sweep is an especially strong predictor of eQTLs for genes in several biological processes of medical interest. Where conditional entropy is a weak or negative predictor of eQTLs, such as innate immune genes, this would be consistent with balancing selection acting on such eQTLs over long time periods. Different measures of selection may be needed for variant prioritization under other modes of evolutionary selection.

Regulation of cholesteryl ester transfer protein expression by upstream polymorphisms: reduced expression associated with rs247616.

Background Cholesteryl ester transfer protein (CETP) is involved in reverse cholesterol transport by exchanging cholesteryl esters for triglycerides between high-density lipoprotein and low-density lipoprotein particles, effectively decreasing high-density lipoprotein cholesterol levels. Variants within a large haplotype block upstream of CETP (rs247616, rs173539) have been shown to be significantly associated with reduced expression; however, the underlying mechanism has not been identified. Methods We analyzed the linkage structure of our top candidate single-nucleotide polymorphism (SNP), rs247616, and assessed each SNP of the haplotype block for potential interactions with transcription factor binding sites. We then used a reporter gene assay to assess the effect of three SNPs (rs247616, rs173539, and rs1723150) on expression in vitro. Results Several variants in the upstream haplotype, including rs247616, rs173539, and rs1723150, disrupt or generate transcription factor binding sites. In reporter gene assays, rs247616 and rs173539 were found to significantly affected expression in HepG2 cells, whereas rs17231506 had no effect. rs247616 decreased expression by 1.7-fold (P<0.0001), whereas rs173539 increased expression by 2.2-fold (P=0.0006). Conclusion SNPs rs247616 and rs173539 are in high linkage disequilibrium (R2=0.96, D′=1.00) and have the potential to regulate CETP expression. Although opposing effects suggest that regulation of CETP expression could vary between tissues, the minor allele of rs247616 and SNPs in high linkage with it were found to be associated with reduced expression across all tissues.

The CHRNA5/CHRNA3/CHRNB4 Nicotinic Receptor Regulome: Genomic Architecture, Regulatory Variants, and Clinical Associations

Functionally related genes often cluster into a genome region under coordinated regulation, forming a local regulome. To understand regulation of the CHRNA5/CHRNA3/CHRNB4 nicotinic receptor gene cluster, we integrate large‐scale RNA expression data (brain and peripheral) from GTEx (Genotype Tissue Expression), clinical associations (GRASP), and linkage disequilibrium data (1000 Genomes) to find candidate SNPs representing independent regulatory variants. CHRNA3, CHRNA5, CHRNB4 mRNAs, and a well‐expressed CHRNA5 antisense RNA (RP11‐650L12.2) are co‐expressed in many human tissues, suggesting common regulatory elements. The CHRNA5 enhancer haplotype tagged by rs880395 not only increases CHRNA5 mRNA expression in all tissues, but also enhances RP11‐650L12.2 and CHRNA3 expression, suggesting DNA looping to multiple promoters. However, in nucleus accumbens and putamen, but not other brain regions, CHRNA3 expression associates uniquely with a haplotype tagged by rs1948 (located in the CHRNB4 3′UTR). Haplotype/diplotype analysis of rs880395 and rs1948 plus rs16969968 (a nonsynonymous CHRNA5 risk variant) in GWAS (COGEND, UW‐TTURC, SAGE) yields a nicotine dependence risk profile only partially captured by rs16969968 alone. An example of local gene clusters, this nicotinic regulome is controlled by complex genetic variation, with broad implications for interpreting GWAS.

Genetic variants affecting alternative splicing of human cholesteryl ester transfer protein.

Cholesteryl ester transfer protein (CETP) plays an important role in reverse cholesterol transport, with decreased CETP activity increasing HDL levels. Formation of an alternative splice form lacking exon 9 (Δ9-CETP) has been associated with two single nucleotide polymorphisms (SNPs) in high linkage disequilibrium with each other, namely rs9930761 T>C located in intron 8 in a putative splicing branch site and rs5883 C>T in a possible exonic splicing enhancer (ESE) site in exon 9. To assess the relative effect of rs9930761 and rs5883 on splicing, mini-gene constructs spanning CETP exons 8 to 10, carrying all four possible allele combinations, were transfected into HEK293 and HepG2 cells. The minor T allele of rs5883 enhanced splicing significantly in both cell lines whereas the minor C allele of rs9930761 did not. In combination, the two alleles did not yield greater splicing than the rs5883 T allele alone in HepG2 cells. These results indicate that the genetic effect on CETP splicing is largely attributable to rs5883. We also confirm that Δ9-CETP protein is expressed in the liver but fails to circulate in the blood.

Analyzing allele specific RNA expression using mixture models.

BackgroundMeasuring allele-specific RNA expression provides valuable insights into cis-acting genetic and epigenetic regulation of gene expression. Widespread adoption of high-throughput sequencing technologies for studying RNA expression (RNA-Seq) permits measurement of allelic RNA expression imbalance (AEI) at heterozygous single nucleotide polymorphisms (SNPs) across the entire transcriptome, and this approach has become especially popular with the emergence of large databases, such as GTEx. However, the existing binomial-type methods used to model allelic expression from RNA-seq assume a strong negative correlation between reference and variant allele reads, which may not be reasonable biologically.ResultsHere we propose a new strategy for AEI analysis using RNA-seq data. Under the null hypothesis of no AEI, a group of SNPs (possibly across multiple genes) is considered comparable if their respective total sums of the allelic reads are of similar magnitude. Within each group of “comparable” SNPs, we identify SNPs with AEI signal by fitting a mixture of folded Skellam distributions to the absolute values of read differences. By applying this methodology to RNA-Seq data from human autopsy brain tissues, we identified numerous instances of moderate to strong imbalanced allelic RNA expression at heterozygous SNPs. Findings with SLC1A3 mRNA exhibiting known expression differences are discussed as examples.ConclusionThe folded Skellam mixture model searches for SNPs with significant difference between reference and variant allele reads (adjusted for different library sizes), using information from a group of “comparable” SNPs across multiple genes. This model is particularly suitable for performing AEI analysis on genes with few heterozygous SNPs available from RNA-seq, and it can fit over-dispersed read counts without specifying the direction of the correlation between reference and variant alleles.

AmpliSeq transcriptome analysis of human alveolar and monocyte-derived macrophages over time in response to Mycobacterium tuberculosis infection

Human alveolar macrophages (HAM) are primary bacterial niche and immune response cells during Mycobacterium tuberculosis (M.tb) infection, and human blood monocyte-derived macrophages (MDM) are a model for investigating M.tb-macrophage interactions. Here, we use a targeted RNA-Seq method to measure transcriptome-wide changes in RNA expression patterns of freshly obtained HAM (used within 6 h) and 6 day cultured MDM upon M.tb infection over time (2, 24 and 72 h), in both uninfected and infected cells from three donors each. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit (AmpliSeq) uses primers targeting 18,574 mRNAs and 2,228 non-coding RNAs (ncRNAs) for a total of 20,802 transcripts. AmpliSeqTM yields highly precise and reproducible gene expression profiles (R2 >0.99). Taking advantage of AmpliSeq’s reproducibility, we establish well-defined quantitative RNA expression patterns of HAM versus MDM, including significant M.tb-inducible genes, in networks and pathways that differ in part between MDM and HAM. A similar number of expressed genes are detected at all time-points between uninfected MDM and HAM, in common pathways including inflammatory and immune functions, but canonical pathway differences also exist. In particular, at 2 h, multiple genes relevant to the immune response are preferentially expressed in either uninfected HAM or MDM, while the HAM RNA profiles approximate MDM profiles over time in culture, highlighting the unique RNA expression profile of freshly obtained HAM. MDM demonstrate a greater transcriptional response than HAM upon M.tb infection, with 2 to >10 times more genes up- or down-regulated. The results identify key genes involved in cellular responses to M.tb in two different human macrophage types. Follow-up bioinformatics analysis indicates that approximately 30% of response genes have expression quantitative trait loci (eQTLs in GTEx), common DNA variants that can influence host gene expression susceptibility or resistance to M.tb, illustrated with the TREM1 gene cluster and IL-10.