Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-map Regulatory Intervals

Association between lipid-lowering drugs and allergic diseases: A Mendelian randomization study

Background

Several observational studies suggest a possible link between lipid-lowering drugs and allergic diseases. However, inferring causality from these studies can be challenging due to issues such as bias, reverse causation, and residual confounding. To investigate the potential causal effect of lipid-lowering drugs, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) inhibitors, on allergic diseases (allergic asthma, allergic conjunctivitis, atopic dermatitis, allergic rhinitis, and allergic urticaria), we performed a Mendelian randomization (MR)-based study.

Methods

We employed MR and summary-data-based MR (SMR), analyzing genome-wide association study (GWAS) data from people of European descent. Single nucleotide polymorphisms (SNPs) were employed as instrumental variables. We selected 2 types of genetic measures to represent the impact of lipid-lowering drugs, including genetic variants near or within drug target genes correlated with low-density lipoprotein cholesterol (LDL-C), and expression quantitative trait loci of drug target genes. The inverse-variance weighted (IVW)-MR approach was the primary utilized MR method, while sensitivity analyses were used to test the robustness of the results. We used SMR analysis as a supplementary analytical method, applying the heterogeneity in dependent instruments (HEIDI) test to assess if the observed correlation between gene expression and outcome was due to a linkage situation.

Results

The IVW-MR analysis revealed significant evidence for an association between PCSK9-mediated LDL-C reduction and a decrease in the risk of allergic asthma (odds ratio [OR] = 1.31, 95% confidence interval [CI] = 1.11-1.56; P < 0.01). Likewise, SMR analysis discovered an augmented expression of PCSK9 being linked with a heightened susceptibility to allergic asthma (OR = 1.21, 95% CI = 1.03-1.43; P = 0.02). No consistent evidence was found for other associations in either analysis.

Conclusion

Our findings support a potential causal relationship between PCSK9 activity and an increased risk of allergic asthma. Thus, PCSK9 inhibitors, which reduce PCSK9 activity, might be considered a priority in future clinical trials investigating drugs for allergic asthma prevention or treatment.

A cis-regulatory element regulates ERAP2 expression through autoimmune disease risk SNPs

Single-nucleotide polymorphisms (SNPs) near the ERAP2 gene are associated with various autoimmune conditions, as well as protection against lethal infections. Due to high linkage disequilibrium, numerous trait-associated SNPs are correlated with ERAP2 expression; however, their functional mechanisms remain unidentified. We show by reciprocal allelic replacement that ERAP2 expression is directly controlled by the splice region variant rs2248374. However, disease-associated variants in the downstream LNPEP gene promoter are independently associated with ERAP2 expression. Allele-specific conformation capture assays revealed long-range chromatin contacts between the gene promoters of LNPEP and ERAP2 and showed that interactions were stronger in patients carrying the alleles that increase susceptibility to autoimmune diseases. Replacing the SNPs in the LNPEP promoter by reference sequences lowered ERAP2 expression. These findings show that multiple SNPs act in concert to regulate ERAP2 expression and that disease-associated variants can convert a gene promoter region into a potent enhancer of a distal gene.

Analyses of allele age and fitness impact reveal human beneficial alleles to be older than neutral controls

A classic population genetic prediction is that alleles experiencing directional selection should swiftly traverse allele frequency space, leaving detectable reductions in genetic variation in linked regions. However, despite this expectation, identifying clear footprints of beneficial allele passage has proven to be surprisingly challenging. We addressed the basic premise underlying this expectation by estimating the ages of large numbers of beneficial and deleterious alleles in a human population genomic data set. Deleterious alleles were found to be young, on average, given their allele frequency. However, beneficial alleles were older on average than non-coding, non-regulatory alleles of the same frequency. This finding is not consistent with directional selection and instead indicates some type of balancing selection. Among derived beneficial alleles, those fixed in the population show higher local recombination rates than those still segregating, consistent with a model in which new beneficial alleles experience an initial period of balancing selection due to linkage disequilibrium with deleterious recessive alleles. Alleles that ultimately fix following a period of balancing selection will leave a modest 'soft' sweep impact on the local variation, consistent with the overall paucity of species-wide 'hard' sweeps in human genomes.

Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants' effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.