Biology subjects

O'Connor, L.

Publications and source records attributed to O'Connor, L..

6 recordsLinked to original sources

bioRxiv2018

Genes with high network connectivity are enriched for disease heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. First, we constructed annotations from 18,119 biological pathways, including 100kb windows around each gene. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and on annotations from the baseline-LD model, a stringent step that greatly reduced the number of pathways detected; most of the significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity using closeness centrality, a measure of how close a gene is to other genes in the network. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.

genetics↗

bioRxiv2017

Distinguishing genetic correlation from causation across 52 diseases and complex traits

Mendelian randomization (MR) is widely used to identify causal relationships among heritable traits, but it can be confounded by genetic correlations reflecting shared etiology. We propose a model in which a latent causal variable mediates the genetic correlation between two traits. Under the latent causal variable (LCV) model, trait 1 is fully genetically causal for trait 2 if it is perfectly genetically correlated with the latent causal variable, implying that the entire genetic component of trait 1 is causal for trait 2; it is partially genetically causal for trait 2 if it has a high genetic correlation with the latent variable, implying that part of the genetic component of trait 1 is causal for trait 2. To quantify the degree of partial genetic causality, we define the genetic causality proportion (gcp). We fit this model using mixed fourth moments E([Formula]12) and E([Formula]12) of marginal effect sizes for each trait, exploiting the fact that if trait 1 is causal for trait 2 then SNPs affecting trait 1 (large [Formula]) will have correlated effects on trait 2 (large 12), but not vice versa. We performed simulations under a wide range of genetic architectures and determined that LCV, unlike state-of-the-art MR methods, produced well-calibrated false positive rates and reliable gcp estimates in the presence of genetic correlations and asymmetric genetic architectures; we also determined that LCV is well-powered to detect a causal effect. We applied LCV to GWAS summary statistics for 52 traits (average N=331k), identifying partially or fully genetically causal effects (1% FDR) for 59 pairs of traits, including 30 pairs of traits with high gcp estimates (g[c]p > 0.6). Results consistent with the published literature included genetically causal effects on myocardial infarction (MI) for LDL, triglycerides and BMI. Novel findings included a genetically causal effect of LDL on bone mineral density, consistent with clinical trials of statins in osteoporosis. These results demonstrate that it is possible to distinguish between genetic correlation and causation using genetic data.

genetics↗

bioRxiv2017

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We further validate our method by showing that it recovers known transcriptional regulators when applied to molecular QTL in blood. We then apply our method to eQTL in 48 GTEx tissues, identifying 651 distinct TF-tissue expression associations at per-tissue FDR < 5%, including 30 associations with robust evidence of tissue specificity. Finally, we apply our method to 46 diseases and complex traits (average N = 289,617) and identify 77 annotation-trait associations at per-trait FDR < 5% representing 12 independent TF-trait associations, and we conduct gene-set enrichment analyses to characterize the underlying transcriptional programs. Our results implicate new causal disease genes (including causal genes at known GWAS loci), and in some cases suggest a detailed mechanism for a causal genes effect on disease. Our method provides a new way to leverage functional data to draw inferences about disease etiology.

genetics↗

bioRxiv2017

Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits

There is increasing evidence that many GWAS risk loci are molecular QTL for gene ex-pression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior prob-abilities (CPP) of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are very strongly enriched for disease heritability across 41 independent diseases and complex traits (average N = 320K): 5.84x for GTEx eQTL, and 5.44x for eQTL, 4.27-4.28x for hQTL (H3K27ac and H3K4me1), 3.61x for sQTL and 2.81x for meQTL in BLUEPRINT (all P [≤] 1.39e-10), far higher than enrichments obtained using stan-dard functional annotations that include all significant molecular cis-QTL (1.17-1.80x). eQTL annotations that were obtained by meta-analyzing all 44 GTEx tissues generally performed best, but tissue-specific blood eQTL annotations produced stronger enrichments for autoimmune dis-eases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrich-ments for brain-related diseases and traits, despite high cis-genetic correlations of eQTL effect sizes across tissues. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for disease heritability (17.09x; vs. 5.84x for all genes; P = 4.90e-17 for difference). All molecular QTL except sQTL remained significantly enriched for disease heritability in a joint analysis conditioned on each other and on a broad set of functional annotations from previous studies, implying that each of these annotations is uniquely informative for disease and complex trait architectures.

genetics↗

bioRxiv2017

Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1-p)], where p is the MAF and negative values of imply larger effect sizes for rare variants. We estimate by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (N = 113,851). All traits produced negative estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true values across traits had mean -0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically significant heterogeneity across traits (P = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10-4 or stronger is necessary to explain the values that we inferred.

genetics↗

bioRxiv2017

Development Of Amplicon Deep Sequencing Markers And Data Analysis Pipeline For Genotyping Multi-Clonal Malaria Infections

Amplicon deep sequencing permits sensitive detection of minority clones and improves discriminatory power for genotyping multi-clone Plasmodium falciparum infections. Such high resolution is needed for molecular monitoring of drug efficacy trials. Targeted sequencing of molecular marker csp and novel marker cpmp was conducted in duplicate on mixtures of parasite culture strains and 37 field samples. A protocol to multiplex up to 384 samples in a single sequencing run was applied. Software \"HaplotypR\" was developed for data analysis. Cpmp was highly diverse (He=0.96) in contrast to csp (He=0.57). Minority clones were robustly detected if their frequency was >1%. False haplotype calls owing to sequencing errors were observed below that threshold. To reliably detect haplotypes at very low frequencies, experiments are best performed in duplicate and should aim for coverage of >10000 reads/amplicon. When compared to length polymorphic marker msp2, highly multiplexed amplicon sequencing displayed greater sensitivity in detecting minority clones.

microbiology↗