@article {113, title = {Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 12}, pages = {1346-1354}, abstract = {

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9\% mean relative increase in R). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

}, keywords = {Asian Continental Ancestry Group, Base Sequence, Computational Biology, Enhancer Elements, Genetic, European Continental Ancestry Group, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {1546-1718}, doi = {10.1038/s41588-020-00740-8}, author = {Amariuta, Tiffany and Ishigaki, Kazuyoshi and Sugishita, Hiroki and Ohta, Tazro and Koido, Masaru and Dey, Kushal K and Matsuda, Koichi and Murakami, Yoshinori and Price, Alkes L and Kawakami, Eiryo and Terao, Chikashi and Raychaudhuri, Soumya} } @article {88, title = {Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.}, journal = {Cell Rep}, volume = {31}, year = {2020}, month = {2020 04 07}, pages = {107489}, abstract = {

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

}, keywords = {Base Sequence, Brain, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Phenotype, Polymorphism, Single Nucleotide, Prefrontal Cortex, Quantitative Trait Loci, Sequence Analysis, RNA, Transcriptome, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {2211-1247}, doi = {10.1016/j.celrep.2020.03.053}, author = {Werling, Donna M and Pochareddy, Sirisha and Choi, Jinmyung and An, Joon-Yong and Sheppard, Brooke and Peng, Minshi and Li, Zhen and Dastmalchi, Claudia and Santpere, Gabriel and Sousa, Andr{\'e} M M and Tebbenkamp, Andrew T N and Kaur, Navjot and Gulden, Forrest O and Breen, Michael S and Liang, Lindsay and Gilson, Michael C and Zhao, Xuefang and Dong, Shan and Klei, Lambertus and Cicek, A Ercument and Buxbaum, Joseph D and Adle-Biassette, Homa and Thomas, Jean-Leon and Aldinger, Kimberly A and O{\textquoteright}Day, Diana R and Glass, Ian A and Zaitlen, Noah A and Talkowski, Michael E and Roeder, Kathryn and State, Matthew W and Devlin, Bernie and Sanders, Stephan J and Sestan, Nenad} } @article {51, title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Mar 01}, pages = {998}, abstract = {

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15\%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53\% F1-score on 1KP common variants, and 98.65, 92.57, 87.26\% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

}, keywords = {Base Sequence, Computational Biology, DNA Mutational Analysis, Genome, Human, Genome-Wide Association Study, Genomics, Genotype, Genotyping Techniques, Humans, INDEL Mutation, Nanopores, Neural Networks, Computer, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {2041-1723}, doi = {10.1038/s41467-019-09025-z}, author = {Luo, Ruibang and Sedlazeck, Fritz J and Lam, Tak-Wah and Schatz, Michael C} }