@article {151, title = {Association of structural variation with cardiometabolic traits in Finns.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 04 01}, pages = {583-596}, abstract = {

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47~{\texttimes} 10) and is also associated with increased levels of total cholesterol (p = 1.22~{\texttimes} 10) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81~{\texttimes} 10) and alanine (p = 6.14~{\texttimes} 10) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24~{\texttimes} 10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53~{\texttimes} 10). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

}, keywords = {Alleles, Cardiovascular Diseases, Cholesterol, DNA Copy Number Variations, Female, Finland, Genome, Human, Genomic Structural Variation, Genotype, High-Throughput Nucleotide Sequencing, Humans, Male, Mitochondrial Proteins, Promoter Regions, Genetic, Pyruvate Dehydrogenase (Lipoamide)-Phosphatase, Pyruvic Acid, Serum Albumin, Human}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.03.008}, author = {Chen, Lei and Abel, Haley J and Das, Indraniel and Larson, David E and Ganel, Liron and Kanchi, Krishna L and Regier, Allison A and Young, Erica P and Kang, Chul Joo and Scott, Alexandra J and Chiang, Colby and Wang, Xinxin and Lu, Shuangjia and Christ, Ryan and Service, Susan K and Chiang, Charleston W K and Havulinna, Aki S and Kuusisto, Johanna and Boehnke, Michael and Laakso, Markku and Palotie, Aarno and Ripatti, Samuli and Freimer, Nelson B and Locke, Adam E and Stitziel, Nathan O and Hall, Ira M} } @article {124, title = {Chromosome-scale, haplotype-resolved assembly of human genomes.}, journal = {Nat Biotechnol}, volume = {39}, year = {2021}, month = {2021 03}, pages = {309-312}, abstract = {

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50\% of the known genome (NG50) up to 25 Mb and phased ~99.5\% of heterozygous sites at 98-99\% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

}, keywords = {Algorithms, Chromosomes, Human, Genome, Human, Haplotypes, Heterozygote, Humans, Polymorphism, Single Nucleotide}, issn = {1546-1696}, doi = {10.1038/s41587-020-0711-0}, author = {Garg, Shilpa and Fungtammasan, Arkarachai and Carroll, Andrew and Chou, Mike and Schmitt, Anthony and Zhou, Xiang and Mac, Stephen and Peluso, Paul and Hatas, Emily and Ghurye, Jay and Maguire, Jared and Mahmoud, Medhat and Cheng, Haoyu and Heller, David and Zook, Justin M and Moemke, Tobias and Marschall, Tobias and Sedlazeck, Fritz J and Aach, John and Chin, Chen-Shan and Church, George M and Li, Heng} } @article {150, title = {Efficient phasing and imputation of low-coverage sequencing data using large reference panels.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 01}, pages = {120-126}, abstract = {

Low-coverage whole-genome sequencing followed by imputation has been proposed as a cost-effective genotyping approach for disease and population genetics studies. However, its competitiveness against SNP arrays is undermined because current imputation methods are computationally expensive and unable to leverage large reference panels. Here, we describe a method, GLIMPSE, for phasing and imputation of low-coverage sequencing datasets from modern reference panels. We demonstrate its remarkable performance across different coverages and human populations. GLIMPSE achieves imputation of a genome for less than US$1 in computational cost, considerably outperforming other methods and improving imputation accuracy over the full allele frequency range. As a proof of concept, we show that 1{\texttimes} coverage enables effective gene expression association studies and outperforms dense SNP arrays in rare variant burden tests. Overall, this study illustrates the promising potential of low-coverage imputation and suggests a paradigm shift in the design of future genomic studies.

}, keywords = {Genome, Human, Genotype, Humans, Likelihood Functions, Polymorphism, Single Nucleotide, Reference Standards, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/s41588-020-00756-0}, author = {Rubinacci, Simone and Ribeiro, Diogo M and Hofmeister, Robin J and Delaneau, Olivier} } @article {156, title = {Haplotype-resolved diverse human genomes and integrated analysis of structural variation.}, journal = {Science}, volume = {372}, year = {2021}, month = {2021 04 02}, abstract = {

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50\% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68\% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63\% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.

}, keywords = {Female, Genetic Variation, Genome, Human, Genotype, Haplotypes, High-Throughput Nucleotide Sequencing, Humans, INDEL Mutation, Interspersed Repetitive Sequences, Male, Population Groups, Quantitative Trait Loci, Retroelements, Sequence Analysis, DNA, Sequence Inversion, Whole Genome Sequencing}, issn = {1095-9203}, doi = {10.1126/science.abf7117}, author = {Ebert, Peter and Audano, Peter A and Zhu, Qihui and Rodriguez-Martin, Bernardo and Porubsky, David and Bonder, Marc Jan and Sulovari, Arvis and Ebler, Jana and Zhou, Weichen and Serra Mari, Rebecca and Yilmaz, Feyza and Zhao, Xuefang and Hsieh, PingHsun and Lee, Joyce and Kumar, Sushant and Lin, Jiadong and Rausch, Tobias and Chen, Yu and Ren, Jingwen and Santamarina, Martin and H{\"o}ps, Wolfram and Ashraf, Hufsah and Chuang, Nelson T and Yang, Xiaofei and Munson, Katherine M and Lewis, Alexandra P and Fairley, Susan and Tallon, Luke J and Clarke, Wayne E and Basile, Anna O and Byrska-Bishop, Marta and Corvelo, Andr{\'e} and Evani, Uday S and Lu, Tsung-Yu and Chaisson, Mark J P and Chen, Junjie and Li, Chong and Brand, Harrison and Wenger, Aaron M and Ghareghani, Maryam and Harvey, William T and Raeder, Benjamin and Hasenfeld, Patrick and Regier, Allison A and Abel, Haley J and Hall, Ira M and Flicek, Paul and Stegle, Oliver and Gerstein, Mark B and Tubio, Jose M C and Mu, Zepeng and Li, Yang I and Shi, Xinghua and Hastie, Alex R and Ye, Kai and Chong, Zechen and Sanders, Ashley D and Zody, Michael C and Talkowski, Michael E and Mills, Ryan E and Devine, Scott E and Lee, Charles and Korbel, Jan O and Marschall, Tobias and Eichler, Evan E} } @article {147, title = {Towards population-scale long-read sequencing.}, journal = {Nat Rev Genet}, volume = {22}, year = {2021}, month = {2021 09}, pages = {572-587}, abstract = {

Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale.

}, keywords = {Computational Biology, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Industrial Development, Sequence Analysis, DNA}, issn = {1471-0064}, doi = {10.1038/s41576-021-00367-3}, author = {De Coster, Wouter and Weissensteiner, Matthias H and Sedlazeck, Fritz J} } @article {120, title = {Inherited causes of clonal haematopoiesis in 97,691 whole genomes.}, journal = {Nature}, volume = {586}, year = {2020}, month = {2020 10}, pages = {763-768}, abstract = {

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is~termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP~driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

}, keywords = {Adult, Africa, African Continental Ancestry Group, Aged, Aged, 80 and over, alpha Karyopherins, Cell Self Renewal, Clonal Hematopoiesis, DNA-Binding Proteins, Female, Genetic Predisposition to Disease, Genome, Human, Germ-Line Mutation, Hematopoietic Stem Cells, Humans, Intracellular Signaling Peptides and Proteins, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Precision Medicine, Proto-Oncogene Proteins, Tripartite Motif Proteins, United States, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2819-2}, author = {Bick, Alexander G and Weinstock, Joshua S and Nandakumar, Satish K and Fulco, Charles P and Bao, Erik L and Zekavat, Seyedeh M and Szeto, Mindy D and Liao, Xiaotian and Leventhal, Matthew J and Nasser, Joseph and Chang, Kyle and Laurie, Cecelia and Burugula, Bala Bharathi and Gibson, Christopher J and Lin, Amy E and Taub, Margaret A and Aguet, Fran{\c c}ois and Ardlie, Kristin and Mitchell, Braxton D and Barnes, Kathleen C and Moscati, Arden and Fornage, Myriam and Redline, Susan and Psaty, Bruce M and Silverman, Edwin K and Weiss, Scott T and Palmer, Nicholette D and Vasan, Ramachandran S and Burchard, Esteban G and Kardia, Sharon L R and He, Jiang and Kaplan, Robert C and Smith, Nicholas L and Arnett, Donna K and Schwartz, David A and Correa, Adolfo and de Andrade, Mariza and Guo, Xiuqing and Konkle, Barbara A and Custer, Brian and Peralta, Juan M and Gui, Hongsheng and Meyers, Deborah A and McGarvey, Stephen T and Chen, Ida Yii-Der and Shoemaker, M Benjamin and Peyser, Patricia A and Broome, Jai G and Gogarten, Stephanie M and Wang, Fei Fei and Wong, Quenna and Montasser, May E and Daya, Michelle and Kenny, Eimear E and North, Kari E and Launer, Lenore J and Cade, Brian E and Bis, Joshua C and Cho, Michael H and Lasky-Su, Jessica and Bowden, Donald W and Cupples, L Adrienne and Mak, Angel C Y and Becker, Lewis C and Smith, Jennifer A and Kelly, Tanika N and Aslibekyan, Stella and Heckbert, Susan R and Tiwari, Hemant K and Yang, Ivana V and Heit, John A and Lubitz, Steven A and Johnsen, Jill M and Curran, Joanne E and Wenzel, Sally E and Weeks, Daniel E and Rao, Dabeeru C and Darbar, Dawood and Moon, Jee-Young and Tracy, Russell P and Buth, Erin J and Rafaels, Nicholas and Loos, Ruth J F and Durda, Peter and Liu, Yongmei and Hou, Lifang and Lee, Jiwon and Kachroo, Priyadarshini and Freedman, Barry I and Levy, Daniel and Bielak, Lawrence F and Hixson, James E and Floyd, James S and Whitsel, Eric A and Ellinor, Patrick T and Irvin, Marguerite R and Fingerlin, Tasha E and Raffield, Laura M and Armasu, Sebastian M and Wheeler, Marsha M and Sabino, Ester C and Blangero, John and Williams, L Keoki and Levy, Bruce D and Sheu, Wayne Huey-Herng and Roden, Dan M and Boerwinkle, Eric and Manson, JoAnn E and Mathias, Rasika A and Desai, Pinkal and Taylor, Kent D and Johnson, Andrew D and Auer, Paul L and Kooperberg, Charles and Laurie, Cathy C and Blackwell, Thomas W and Smith, Albert V and Zhao, Hongyu and Lange, Ethan and Lange, Leslie and Rich, Stephen S and Rotter, Jerome I and Wilson, James G and Scheet, Paul and Kitzman, Jacob O and Lander, Eric S and Engreitz, Jesse M and Ebert, Benjamin L and Reiner, Alexander P and Jaiswal, Siddhartha and Abecasis, Gon{\c c}alo and Sankaran, Vijay G and Kathiresan, Sekar and Natarajan, Pradeep} } @article {122, title = {Mapping and characterization of structural variation in 17,795 human genomes.}, journal = {Nature}, volume = {583}, year = {2020}, month = {2020 07}, pages = {83-89}, abstract = {

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2\% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2\% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90\% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2\% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

}, keywords = {Alleles, Case-Control Studies, Continental Population Groups, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Software, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2371-0}, author = {Abel, Haley J and Larson, David E and Regier, Allison A and Chiang, Colby and Das, Indraniel and Kanchi, Krishna L and Layer, Ryan M and Neale, Benjamin M and Salerno, William J and Reeves, Catherine and Buyske, Steven and Matise, Tara C and Muzny, Donna M and Zody, Michael C and Lander, Eric S and Dutcher, Susan K and Stitziel, Nathan O and Hall, Ira M} } @article {105, title = {Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 08 20}, pages = {3635}, abstract = {

Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17\% to 78\% for coronary artery disease, 13\% to 76\% for breast cancer, and 11\% to 80\% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

}, keywords = {Aged, Breast Neoplasms, Case-Control Studies, Colorectal Neoplasms, Coronary Artery Disease, Female, Genetic Predisposition to Disease, Genome, Human, Humans, Male, Middle Aged, Multifactorial Inheritance, Odds Ratio, Penetrance, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-17374-3}, author = {Fahed, Akl C and Wang, Minxian and Homburger, Julian R and Patel, Aniruddh P and Bick, Alexander G and Neben, Cynthia L and Lai, Carmen and Brockman, Deanna and Philippakis, Anthony and Ellinor, Patrick T and Cassa, Christopher A and Lebo, Matthew and Ng, Kenney and Lander, Eric S and Zhou, Alicia Y and Kathiresan, Sekar and Khera, Amit V} } @article {91, title = {Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2.}, journal = {Genes (Basel)}, volume = {11}, year = {2020}, month = {2020 01 29}, abstract = {

Gene duplication is a major mechanism for the evolution of gene novelty, and copy-number variation makes a major contribution to inter-individual genetic diversity. However, most approaches for studying copy-number variation rely upon uniquely mapping reads to a genome reference and are unable to distinguish among duplicated sequences. Specialized approaches to interrogate specific paralogs are comparatively slow and have a high degree of computational complexity, limiting their effective application to emerging population-scale data sets. We present QuicK-mer2, a self-contained, mapping-free approach that enables the rapid construction of paralog-specific copy-number maps from short-read sequence data. This approach is based on the tabulation of unique k-mer sequences from short-read data sets, and is able to analyze a 20X coverage human genome in approximately 20 min. We applied our approach to newly released sequence data from the 1000 Genomes Project, constructed paralog-specific copy-number maps from 2457 unrelated individuals, and uncovered copy-number variation of paralogous genes. We identify nine genes where none of the analyzed samples have a copy number of two, 92 genes where the majority of samples have a copy number other than two, and describe rare copy number variation effecting multiple genes at the APOBEC3 locus.

}, keywords = {Algorithms, Computational Biology, DNA Copy Number Variations, Evolution, Molecular, Gene Duplication, Genome, Human, Humans, Sequence Analysis, DNA}, issn = {2073-4425}, doi = {10.3390/genes11020141}, author = {Shen, Feichen and Kidd, Jeffrey M} } @article {114, title = {A structural variation reference for medical and population genetics.}, journal = {Nature}, volume = {581}, year = {2020}, month = {2020 05}, pages = {444-451}, abstract = {

Structural variants (SVs) rearrange large segments of DNA and can have profound consequences in evolution and human disease. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral in the interpretation of single-nucleotide variants (SNVs). However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54\% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29\% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9\% of samples, and estimate that 0.13\% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings. This SV resource is freely distributed via the gnomAD browser and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

}, keywords = {Continental Population Groups, Disease, Female, Genetic Testing, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genotyping Techniques, Humans, Male, Middle Aged, Mutation, Polymorphism, Single Nucleotide, Reference Standards, Selection, Genetic, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2287-8}, author = {Collins, Ryan L and Brand, Harrison and Karczewski, Konrad J and Zhao, Xuefang and Alf{\"o}ldi, Jessica and Francioli, Laurent C and Khera, Amit V and Lowther, Chelsea and Gauthier, Laura D and Wang, Harold and Watts, Nicholas A and Solomonson, Matthew and O{\textquoteright}Donnell-Luria, Anne and Baumann, Alexander and Munshi, Ruchi and Walker, Mark and Whelan, Christopher W and Huang, Yongqing and Brookings, Ted and Sharpe, Ted and Stone, Matthew R and Valkanas, Elise and Fu, Jack and Tiao, Grace and Laricchia, Kristen M and Ruano-Rubio, Valentin and Stevens, Christine and Gupta, Namrata and Cusick, Caroline and Margolin, Lauren and Taylor, Kent D and Lin, Henry J and Rich, Stephen S and Post, Wendy S and Chen, Yii-Der Ida and Rotter, Jerome I and Nusbaum, Chad and Philippakis, Anthony and Lander, Eric and Gabriel, Stacey and Neale, Benjamin M and Kathiresan, Sekar and Daly, Mark J and Banks, Eric and MacArthur, Daniel G and Talkowski, Michael E} } @article {101, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

}, keywords = {Genetic Variation, Genome, Human, Humans, Multifactorial Inheritance, Organ Specificity, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Fran{\c c}ois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {52, title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 03 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} } @article {37, title = {Accurate detection of complex structural variations using single-molecule sequencing.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 Jun}, pages = {461-468}, abstract = {

Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

}, keywords = {DNA Mutational Analysis, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA}, issn = {1548-7105}, doi = {10.1038/s41592-018-0001-7}, author = {Sedlazeck, Fritz J and Rescheneder, Philipp and Smolka, Moritz and Fang, Han and Nattestad, Maria and von Haeseler, Arndt and Schatz, Michael C} } @article {36, title = {Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms.}, journal = {BMC Genomics}, volume = {19}, year = {2018}, month = {2018 May 08}, pages = {332}, abstract = {

BACKGROUND: Here we present an in-depth characterization of the mechanism of sequencer-induced sample contamination due to the phenomenon of index swapping that impacts Illumina sequencers employing patterned flow cells with Exclusion Amplification (ExAmp) chemistry (HiSeqX, HiSeq4000, and NovaSeq). We also present a remediation method that minimizes the impact of such swaps.

RESULTS: Leveraging data collected over a two-year period, we demonstrate the widespread prevalence of index swapping in patterned flow cell data. We calculate mean swap rates across multiple sample preparation methods and sequencer models, demonstrating that different library methods can have vastly different swapping rates and that even non-ExAmp chemistry instruments display trace levels of index swapping. We provide methods for eliminating sample data cross contamination by utilizing non-redundant dual indexing for complete filtering of index swapped reads, and share the sequences for 96 non-combinatorial dual indexes we have validated across various library preparation methods and sequencer models. Finally, using computational methods we provide a greater insight into the mechanism of index swapping.

CONCLUSIONS: Index swapping in pooled libraries is a prevalent phenomenon that we observe at a rate of 0.2 to 6\% in all sequencing runs on HiSeqX, HiSeq 4000/3000, and NovaSeq. Utilizing non-redundant dual indexing allows for the removal (flagging/filtering) of these swapped reads and eliminates swapping induced sample contamination, which is critical for sensitive applications such as RNA-seq, single cell, blood biopsy using circulating tumor DNA, or clinical sequencing.

}, keywords = {DNA, Gene Library, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, Sequence Analysis, DNA}, issn = {1471-2164}, doi = {10.1186/s12864-018-4703-0}, author = {Costello, Maura and Fleharty, Mark and Abreu, Justin and Farjoun, Yossi and Ferriera, Steven and Holmes, Laurie and Granger, Brian and Green, Lisa and Howd, Tom and Mason, Tamara and Vicente, Gina and Dasilva, Michael and Brodeur, Wendy and DeSmet, Timothy and Dodge, Sheila and Lennon, Niall J and Gabriel, Stacey} } @article {49, title = {Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers.}, journal = {Genet Med}, volume = {20}, year = {2018}, month = {2018 08}, pages = {855-866}, abstract = {

PURPOSE: As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.

METHODS: To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.

RESULTS: This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.

CONCLUSION: This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.

}, keywords = {Base Sequence, Chromosome Mapping, Exome, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Software, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1530-0366}, doi = {10.1038/gim.2017.192}, author = {Sanghvi, Rashesh V and Buhay, Christian J and Powell, Bradford C and Tsai, Ellen A and Dorschner, Michael O and Hong, Celine S and Lebo, Matthew S and Sasson, Ariella and Hanna, David S and McGee, Sean and Bowling, Kevin M and Cooper, Gregory M and Gray, David E and Lonigro, Robert J and Dunford, Andrew and Brennan, Christine A and Cibulskis, Carrie and Walker, Kimberly and Carneiro, Mauricio O and Sailsbery, Joshua and Hindorff, Lucia A and Robinson, Dan R and Santani, Avni and Sarmady, Mahdi and Rehm, Heidi L and Biesecker, Leslie G and Nickerson, Deborah A and Hutter, Carolyn M and Garraway, Levi and Muzny, Donna M and Wagle, Nikhil} } @article {40, title = {Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line.}, journal = {Genome Res}, volume = {28}, year = {2018}, month = {2018 08}, pages = {1126-1135}, abstract = {

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged, although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available, with nearly 20,000 variants present, most of which were missed by short-read sequencing. Surrounding the important oncogene (also known as ), we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the genome and sheds new light on the complex mechanisms involved in cancer genome evolution.

}, keywords = {Breast Neoplasms, Female, Gene Amplification, Gene Rearrangement, Genome, Human, Genomic Structural Variation, High-Throughput Nucleotide Sequencing, Humans, MCF-7 Cells, Oncogenes, Receptor, ErbB-2, Repetitive Sequences, Nucleic Acid, Transcriptome}, issn = {1549-5469}, doi = {10.1101/gr.231100.117}, author = {Nattestad, Maria and Goodwin, Sara and Ng, Karen and Baslan, Timour and Sedlazeck, Fritz J and Rescheneder, Philipp and Garvin, Tyler and Fang, Han and Gurtowski, James and Hutton, Elizabeth and Tseng, Elizabeth and Chin, Chen-Shan and Beck, Timothy and Sundaravadanam, Yogi and Kramer, Melissa and Antoniou, Eric and McPherson, John D and Hicks, James and McCombie, W Richard and Schatz, Michael C} } @article {45, title = {Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 10 02}, pages = {4038}, abstract = {

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

}, keywords = {Genome, Human, Human Genetics, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-018-06159-4}, author = {Regier, Allison A and Farjoun, Yossi and Larson, David E and Krasheninina, Olga and Kang, Hyun Min and Howrigan, Daniel P and Chen, Bo-Juen and Kher, Manisha and Banks, Eric and Ames, Darren C and English, Adam C and Li, Heng and Xing, Jinchuan and Zhang, Yeting and Matise, Tara and Abecasis, Goncalo R and Salerno, Will and Zody, Michael C and Neale, Benjamin M and Hall, Ira M} } @article {32, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {19, title = {Genome-wide profiling of heritable and de novo STR variations.}, journal = {Nat Methods}, volume = {14}, year = {2017}, month = {2017 Jun}, pages = {590-592}, abstract = {

Short tandem repeats (STRs) are highly variable elements that play a pivotal role in multiple genetic diseases, population genetics applications, and forensic casework. However, it has proven problematic to genotype STRs from high-throughput sequencing data. Here, we describe HipSTR, a novel haplotype-based method for robustly genotyping and phasing STRs from Illumina sequencing data, and we report a genome-wide analysis and validation of de novo STR mutations. HipSTR is freely available at https://hipstr-tool.github.io/HipSTR.

}, keywords = {Algorithms, Chromosome Mapping, DNA Fingerprinting, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Microsatellite Repeats, Sequence Alignment, Sequence Analysis, DNA, Software}, issn = {1548-7105}, doi = {10.1038/nmeth.4267}, author = {Willems, Thomas and Zielinski, Dina and Yuan, Jie and Gordon, Assaf and Gymrek, Melissa and Erlich, Yaniv} } @article {16, title = {The impact of structural variation on human gene expression.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {692-699}, abstract = {

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8\% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3\%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.

}, keywords = {Algorithms, Chromosome Mapping, Gene Expression Regulation, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, INDEL Mutation, Linear Models, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3834}, author = {Chiang, Colby and Scott, Alexandra J and Davis, Joe R and Tsang, Emily K and Li, Xin and Kim, Yungil and Hadzic, Tarik and Damani, Farhan N and Ganel, Liron and Montgomery, Stephen B and Battle, Alexis and Conrad, Donald F and Hall, Ira M} } @article {7, title = {Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 Feb 02}, pages = {205-215}, abstract = {

Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.

}, keywords = {Black or African American, C-Reactive Protein, Cholesterol, HDL, Cholesterol, LDL, Chromosomes, Human, Pair 9, Gene Frequency, Genome, Human, Genome-Wide Association Study, Genomics, Hemoglobins, Humans, Introns, Leukocyte Count, Lipoprotein(a), Magnesium, Natriuretic Peptide, Brain, Neutrophils, Peptide Fragments, Phosphorus, Platelet Count, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Troponin T, White People}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.12.009}, author = {Morrison, Alanna C and Huang, Zhuoyi and Yu, Bing and Metcalf, Ginger and Liu, Xiaoming and Ballantyne, Christie and Coresh, Josef and Yu, Fuli and Muzny, Donna and Feofanova, Elena and Rustagi, Navin and Gibbs, Richard and Boerwinkle, Eric} } @article {10, title = {Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA.}, journal = {Am J Hum Genet}, volume = {98}, year = {2016}, month = {2016 Jan 07}, pages = {58-74}, abstract = {

We performed whole-genome sequencing (WGS) of 208 genomes from 53 families affected by simplex autism. For the majority of these families, no copy-number variant (CNV) or candidate de novo gene-disruptive single-nucleotide variant (SNV) had been detected by microarray or whole-exome sequencing (WES). We integrated multiple CNV and SNV analyses and extensive experimental validation to identify additional candidate mutations in eight families. We report that compared to control individuals, probands showed a significant (p = 0.03) enrichment of de novo and private disruptive mutations within fetal CNS DNase I hypersensitive sites (i.e., putative regulatory regions). This effect was only observed within 50 kb of genes that have been previously associated with autism risk, including genes where dosage sensitivity has already been established by recurrent disruptive de novo protein-coding mutations (ARID1B, SCN2A, NR3C2, PRKCA, and DSCAM). In addition, we provide evidence of gene-disruptive CNVs (in DISC1, WNT7A, RBFOX1, and MBD5), as well as smaller de novo CNVs and exon-specific SNVs missed by exome sequencing in neurodevelopmental genes (e.g., CANX, SAE1, and PIK3CA). Our results suggest that the detection of smaller, often multiple CNVs affecting putative regulatory elements might help explain additional risk of simplex autism.

}, keywords = {Autistic Disorder, DNA, Exome, Female, Genome, Human, Humans, Male, Pedigree, Polymorphism, Single Nucleotide}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2015.11.023}, author = {Turner, Tychele N and Hormozdiari, Fereydoun and Duyzend, Michael H and McClymont, Sarah A and Hook, Paul W and Iossifov, Ivan and Raja, Archana and Baker, Carl and Hoekzema, Kendra and Stessman, Holly A and Zody, Michael C and Nelson, Bradley J and Huddleston, John and Sandstrom, Richard and Smith, Joshua D and Hanna, David and Swanson, James M and Faustman, Elaine M and Bamshad, Michael J and Stamatoyannopoulos, John and Nickerson, Deborah A and McCallion, Andrew S and Darnell, Robert and Eichler, Evan E} }