Biology subjects
Publications and source records attributed to Knoblauch, N..
Withdraw StatementThe authors have withdrawn this manuscript due to a duplicate submission and request that it not be cited as a reference for ongoing projects. For further inquiries, please contact the corresponding author. Details regarding this project are available at https://www.biorxiv.org/content/10.1101/828061v1.article-info.
While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWAS), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of comprehensive functional genomic annotations in human cell types relevant to pregnancy and PTB. Here, we generated extensive transcriptional and chromatin annotations of cultured primary decidua-derived mesenchymal stromal/stem cells (MSCs) and in vitro differentiated decidual stromal cells (DSCs) and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. This resulted in a significant enrichment of heritability estimates in functional noncoding regions in stromal cells, as well as in the discovery of additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how systematic functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.
Rare variant analysis is commonly used in whole-exome or genome sequencing studies. Compared to common variants, rare variants tend to have larger effect sizes and often directly point out causal genes. These potential benefits make association analysis with rare variants a priority for human genetics researchers. To improve the power of such studies, numerous methods have been developed to aggregate information of all variants of a gene. However, these gene-based methods often make unrealistic assumptions, e.g. the commonly used burden test effectively assumes that all variants chosen in the analysis have the same effects. In practice, current methods are often underpowered. We propose a Bayesian method: MIxture model based Rare variant Analysis on GEnes (MIRAGE). MIRAGE analyzes summary statistics, i.e. variant counts from inherited variants in trio-sequencing or from ancestry-matched case-control studies. MIRAGE captures the heterogeneity of variant effects by treating all variants of a gene as a mixture of risk and non-risk variants, and uses external information of variants to model the prior probabilities of being risk variants. We demonstrate in both simulations and analysis of an exome-sequencing dataset of autism, that MIRAGE significantly outperforms current methods for rare variant analysis. The top genes identified by MIRAGE are highly enriched with known or plausible autism risk genes. MIRAGE is available at https://xinhe-lab.github.io/mirage.
Mendelian randomization (MR) is a valuable tool for detecting evidence of causal relationships using genetic variant associations. Opportunities to apply MR are growing rapidly with the number of genome-wide association studies (GWAS) with publicly available results. However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Many methods have been proposed loosening these assumptions. However, it has remained challenging to account for correlated pleiotropy, which arises when variants affect both traits through a heritable shared factor. We propose a new MR method, Causal Analysis Using Summary Effect Estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate in simulations that CAUSE is more robust to correlated pleiotropy than other methods. Applied to traits studied in recent GWAS, we find that CAUSE detects causal relationships with strong literature support and avoids identifying most unlikely relationships. Our results suggest that many pairs of traits identified as causal using alternative methods may be false positives due to horizontal pleiotropy.
Identifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.