@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 {116, title = {Copy number variants~and fixed duplications among 198 rhesus macaques (Macaca mulatta).}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 05}, pages = {e1008742}, abstract = {

The rhesus macaque is an abundant species of Old World monkeys and a valuable model organism for biomedical research due to its close phylogenetic relationship to humans. Copy number variation is one of the main sources of genomic diversity within and between species and a widely recognized cause of inter-individual differences in disease risk. However, copy number differences among rhesus macaques and between the human and macaque genomes, as well as the relevance of this diversity to research involving this nonhuman primate, remain understudied. Here we present a high-resolution map of sequence copy number for the rhesus macaque genome constructed from a dataset of 198 individuals. Our results show that about one-eighth of the rhesus macaque reference genome is composed of recently duplicated regions, either copy number variable regions or fixed duplications. Comparison with human genomic copy number maps based on previously published data shows that, despite overall similarities in the genome-wide distribution of these regions, there are specific differences at the chromosome level. Some of these create differences in the copy number profile between human disease genes and their rhesus macaque orthologs. Our results highlight the importance of addressing the number of copies of target genes in the design of experiments and cautions against human-centered assumptions in research conducted with model organisms. Overall, we present a genome-wide copy number map from a large sample of rhesus macaque individuals representing an important novel contribution concerning the evolution of copy number in primate genomes.

}, keywords = {Animals, Chromosome Mapping, DNA Copy Number Variations, Female, Gene Duplication, Genetics, Population, Genome, High-Throughput Nucleotide Sequencing, Humans, Macaca mulatta, Male, Open Reading Frames, Phylogeny, Sequence Analysis, DNA, Species Specificity}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008742}, author = {Bras{\'o}-Vives, Marina and Povolotskaya, Inna S and Hartas{\'a}nchez, Diego A and Farr{\'e}, Xavier and Fernandez-Callejo, Marcos and Raveendran, Muthuswamy and Harris, R Alan and Rosene, Douglas L and Lorente-Galdos, Belen and Navarro, Arcadi and Marques-Bonet, Tomas and Rogers, Jeffrey and Juan, David} } @article {110, title = {Discovery and population genomics of structural variation in a songbird genus.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 07 07}, pages = {3403}, abstract = {

Structural variation~(SV) constitutes an important type of genetic mutations providing the raw material for evolution. Here, we uncover the genome-wide spectrum of intra- and interspecific SV segregating in natural populations of seven songbird species in the genus Corvus. Combining short-read (N = 127) and long-read re-sequencing (N = 31), as well as optical mapping (N = 16), we apply both assembly- and read mapping approaches to detect SV and characterize a total of 220,452 insertions, deletions and inversions. We exploit sampling across wide phylogenetic timescales to validate SV genotypes and assess the contribution of SV to evolutionary processes in an avian model of incipient speciation. We reveal an evolutionary young (~530,000 years) cis-acting 2.25-kb LTR retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth and evolutionary significance of SV segregating in natural populations and highlight the need for reliable SV genotyping.

}, keywords = {Animals, Chromosome Inversion, Gene Deletion, Genetic Variation, Genetics, Population, Genome, Genomic Structural Variation, Genotype, Phylogeny, Polymorphism, Single Nucleotide, Retroelements, Sequence Analysis, DNA, Songbirds}, issn = {2041-1723}, doi = {10.1038/s41467-020-17195-4}, author = {Weissensteiner, Matthias H and Bunikis, Ignas and Catal{\'a}n, Ana and Francoijs, Kees-Jan and Knief, Ulrich and Heim, Wieland and Peona, Valentina and Pophaly, Saurabh D and Sedlazeck, Fritz J and Suh, Alexander and Warmuth, Vera M and Wolf, Jochen B W} } @article {111, title = {Evaluation of the VISAGE Basic Tool for Appearance and Ancestry Prediction Using PowerSeq Chemistry on the MiSeq FGx System.}, journal = {Genes (Basel)}, volume = {11}, year = {2020}, month = {2020 06 26}, abstract = {

The study of DNA to predict externally visible characteristics (EVCs) and the biogeographical ancestry (BGA) from unknown samples is gaining relevance in forensic genetics. Technical developments in Massively Parallel Sequencing (MPS) enable the simultaneous analysis of hundreds of DNA markers, which improves successful Forensic DNA Phenotyping (FDP). The EU-funded VISAGE (VISible Attributes through GEnomics) Consortium has developed various targeted MPS-based lab tools to apply FDP in routine forensic analyses. Here, we present an evaluation of the VISAGE Basic tool for appearance and ancestry prediction based on PowerSeq chemistry (Promega) on a MiSeq FGx System (Illumina). The panel consists of 153 single nucleotide polymorphisms (SNPs) that provide information about EVCs (41 SNPs for eye, hair and skin color from HIrisPlex-S) and continental BGA (115 SNPs; three overlap with the EVCs SNP set). The assay was evaluated for sensitivity, repeatability and genotyping concordance, as well as its performance with casework-type samples. This targeted MPS assay provided complete genotypes at all 153 SNPs down to 125 pg of input DNA and 99.67\% correct genotypes at 50 pg. It was robust in terms of repeatability and concordance and provided useful results with casework-type samples. The results suggest that this MPS assay is a useful tool for basic appearance and ancestry prediction in forensic genetics for users interested in applying PowerSeq chemistry and MiSeq for this purpose.

}, keywords = {DNA Fingerprinting, Eye Color, Forensic Genetics, Genetic Markers, Genotype, Hair Color, High-Throughput Nucleotide Sequencing, Humans, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Skin Pigmentation, Software}, issn = {2073-4425}, doi = {10.3390/genes11060708}, author = {Palencia-Madrid, Leire and Xavier, Catarina and de la Puente, Mar{\'\i}a and Hohoff, Carsten and Phillips, Christopher and Kayser, Manfred and Parson, Walther} } @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 {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 {28, title = {Exome sequencing reveals novel genetic loci influencing obesity-related traits in Hispanic children.}, journal = {Obesity (Silver Spring)}, volume = {25}, year = {2017}, month = {2017 Jul}, pages = {1270-1276}, abstract = {

OBJECTIVE: To perform whole exome sequencing in 928 Hispanic children and identify variants and genes associated with childhood obesity.

METHODS: Single-nucleotide variants (SNVs) were identified from Illumina whole exome sequencing data using integrated read mapping, variant calling, and an annotation pipeline (Mercury). Association analyses of 74 obesity-related traits and exonic variants were performed using SeqMeta software. Rare autosomal variants were analyzed using gene-based association analyses, and common autosomal variants were analyzed at the SNV level.

RESULTS: (1) Rare exonic variants in 10 genes and 16 common SNVs in 11 genes that were associated with obesity traits in a cohort of Hispanic children were identified, (2) novel rare variants in peroxisome biogenesis factor 1 (PEX1) associated with several obesity traits (weight, weight z score, BMI, BMI z score, waist circumference, fat mass, trunk fat mass) were discovered, and (3) previously reported SNVs associated with childhood obesity were replicated.

CONCLUSIONS: Convergence of whole exome sequencing, a family-based design, and extensive phenotyping discovered novel rare and common variants associated with childhood obesity. Linking PEX1 to obesity phenotypes poses a novel mechanism of peroxisomal biogenesis and metabolism underlying the development of childhood obesity.

}, keywords = {Adolescent, ATPases Associated with Diverse Cellular Activities, Body Mass Index, Body Weight, Child, Child, Preschool, Cohort Studies, Exome, Genetic Loci, Genome-Wide Association Study, Hispanic or Latino, Humans, Membrane Proteins, Pediatric Obesity, Polymorphism, Single Nucleotide, Risk Factors, Sequence Analysis, DNA, Software, Waist Circumference, Young Adult}, issn = {1930-739X}, doi = {10.1002/oby.21869}, author = {Sabo, Aniko and Mishra, Pamela and Dugan-Perez, Shannon and Voruganti, V Saroja and Kent, Jack W and Kalra, Divya and Cole, Shelley A and Comuzzie, Anthony G and Muzny, Donna M and Gibbs, Richard A and Butte, Nancy F} } @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 {9, title = {Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.}, journal = {N Engl J Med}, volume = {376}, year = {2017}, month = {2017 Jan 05}, pages = {21-31}, abstract = {

BACKGROUND: Whole-exome sequencing can provide insight into the relationship between observed clinical phenotypes and underlying genotypes.

METHODS: We conducted a retrospective analysis of data from a series of 7374 consecutive unrelated patients who had been referred to a clinical diagnostic laboratory for whole-exome sequencing; our goal was to determine the frequency and clinical characteristics of patients for whom more than one molecular diagnosis was reported. The phenotypic similarity between molecularly diagnosed pairs of diseases was calculated with the use of terms from the Human Phenotype Ontology.

RESULTS: A molecular diagnosis was rendered for 2076 of 7374 patients (28.2\%); among these patients, 101 (4.9\%) had diagnoses that involved two or more disease loci. We also analyzed parental samples, when available, and found that de novo variants accounted for 67.8\% (61 of 90) of pathogenic variants in autosomal dominant disease genes and 51.7\% (15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7\% (17 of 38) of patients with two monoallelic variants. Causal copy-number variants were found in 12 patients (11.9\%) with multiple diagnoses. Phenotypic similarity scores were significantly lower among patients in whom the phenotype resulted from two distinct mendelian disorders that affected different organ systems (50 patients) than among patients with disorders that had overlapping phenotypic features (30 patients) (median score, 0.21 vs. 0.36; P=1.77{\texttimes}10).

CONCLUSIONS: In our study, we found multiple molecular diagnoses in 4.9\% of cases in which whole-exome sequencing was informative. Our results show that structured clinical ontologies can be used to determine the degree of overlap between two mendelian diseases in the same patient; the diseases can be distinct or overlapping. Distinct disease phenotypes affect different organ systems, whereas overlapping disease phenotypes are more likely to be caused by two genes encoding proteins that interact within the same pathway. (Funded by the National Institutes of Health and the Ting Tsung and Wei Fong Chao Foundation.).

}, keywords = {Exome, Genetic Diseases, Inborn, Genetic Variation, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Phenotype, Retrospective Studies, Sequence Analysis, DNA}, issn = {1533-4406}, doi = {10.1056/NEJMoa1516767}, author = {Posey, Jennifer E and Harel, Tamar and Liu, Pengfei and Rosenfeld, Jill A and James, Regis A and Coban Akdemir, Zeynep H and Walkiewicz, Magdalena and Bi, Weimin and Xiao, Rui and Ding, Yan and Xia, Fan and Beaudet, Arthur L and Muzny, Donna M and Gibbs, Richard A and Boerwinkle, Eric and Eng, Christine M and Sutton, V Reid and Shaw, Chad A and Plon, Sharon E and Yang, Yaping and Lupski, James R} }