Biology subjects

Kim, S. S.

Publications and source records attributed to Kim, S. S..

24 records · Page 2Linked to original sources

bioRxiv2020

Improving the informativeness of Mendelian disease pathogenicity scores for common disease

Despite considerable progress on pathogenicity scores prioritizing both coding and noncoding variants for Mendelian disease, little is known about the utility of these pathogenicity scores for common disease. Here, we sought to assess the informativeness of Mendelian diseasederived pathogenicity scores for common disease, and to improve upon existing scores. We first applied stratified LD score regression to assess the informativeness of annotations defined by top variants from published Mendelian disease-derived pathogenicity scores across 41 independent common diseases and complex traits (average N = 320K). Several of the resulting annotations were informative for common disease, even after conditioning on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model. We then improved upon the published pathogenicity scores by developing AnnotBoost, a gradient boosting-based framework to impute and denoise pathogenicity scores using functional annotations from the baseline-LD model. AnnotBoost substantially increased the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying pervasive variant-level overlap between Mendelian disease and common disease. The boosted scores also produced significant improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores have high potential to improve candidate gene discovery and fine-mapping for common disease.

genetics↗

bioRxiv2019

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Many diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82x (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

genetics↗

bioRxiv2019

Evaluating the informativeness of deep learning annotations for human complex diseases

Deep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotations informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results - only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

genetics↗

bioRxiv2019

Rho-kinase mediates the anorexigenic action of melanocortin by suppressing AMPK

ObjectiveMelanocortin action is essential for the maintenance of energy homeostasis. However, knowledge of the signaling mechanism(s) that mediates the effect of melanocortin remains incomplete.\n\nMethodsROCK1 is a key regulator of energy balance in the hypothalamus. To explore the role of ROCK1 in the anorexigenic action of melanocortin, we deleted ROCK1 in MC4R neurons in mice. Next, we studied the metabolic effects of MC4R neuron-specific ROCK1-deficiency and following treatment with -melanocyte-stimulating hormone (MSH).\n\nResultsHere we show that -MSH increases Rho-kinase 1 (ROCK1) activity in the hypothalamus. Deficiency of ROCK1 in MC4R-expressing neurons results in increased body weight in mice fed normal chow diet. This is likely due to increased food intake and decreased energy expenditure. Importantly, we find that ROCK1 activation in MC4R expressing neurons is required for melanocortin action, as evidenced by the fact that - MSHs ability to suppress food intake is impaired in MC4R neuron-specific ROCK1-deficient mice. To elucidate the mechanism by which ROCK1 mediates melanocortin action, we performed in vitro studies in hypothalamic cells expressing MC4R. We demonstrate that -MSH promotes the physical interaction of ROCK1 and G12, and this results in suppression of AMPK activity.\n\nConclusionsOur study identifies ROCK1 as a novel mediator of melanocortins anorexigenic action and uncover a new MC4R[->]G12[->]ROCK1[->]AMPK signaling pathway. Targeting Rho-kinase in MC4R-expressing neurons could provide a new strategy to combat obesity and its related complications.

neuroscience↗

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↗

bioRxiv2018

Parp3 promotes long-range end-joining in murine cells

Chromosomal rearrangements, including translocations, are early and essential events in the formation of many tumors. Previous studies that defined the genetic requirements for rearrangement formation have identified differences between murine and human cells, most notably in the role of classical- and alternative-nonhomologous end joining factors (NHEJ). We reported that poly(ADP)ribose polymerase 3 (PARP3) promotes chromosomal rearrangements induced by endonucleases in multiple human cell types. In contrast to c-NHEJ factors, we show here that Parp3 also promotes rearrangements in murine cells, including translocations in murine embryonic stem cells (mESCs), class switch recombination in primary B cells and inversions in tail fibroblasts that generate Eml4-Alk fusions. In mESCs, Parp3-deficient cells had shorter deletion lengths at translocation junctions. This was corroborated using next-generation sequencing of Eml4-Alk junctions in tail fibroblasts and is consistent with a role for Parp3 in promoting the processing of DNA double-strand breaks. We confirmed a previous report that Parp1 also promotes rearrangement formation. In contrast with Parp3, rearrangement junctions in the absence of Parp1 had longer deletion lengths, suggesting Parp1 may suppress DSB processing. Together, these data indicate that Parp3 and Parp1 promote rearrangements with distinct phenotypes.

cancer biology↗