@article {89, title = {Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.}, journal = {Epilepsia}, volume = {61}, year = {2020}, month = {2020 02}, pages = {249-258}, abstract = {

OBJECTIVE: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.

METHODS: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.

RESULTS: Overall, we identified pathogenic or likely pathogenic variants in 40\% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26\% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25\%).

SIGNIFICANCE: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {129, title = {Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease.}, journal = {Arterioscler Thromb Vasc Biol}, volume = {40}, year = {2020}, month = {2020 11}, pages = {2738-2746}, abstract = {

OBJECTIVE: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPS) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPS in 28 556 middle-aged participants of the Malm{\"o} Diet and Cancer Study, of whom 4122 (14.4\%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16\% for those in the lowest GPS decile to 48\% in the highest. We evaluated the discriminative capacity of the GPS-as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPS (+0.045, <0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPS and 10-year risk defined by the PCE (=0.03), and addition of GPS improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPS, even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank.

CONCLUSIONS: GPS-a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPS may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.

}, keywords = {Adult, Aged, Coronary Artery Disease, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Heart Disease Risk Factors, Heredity, Humans, Incidence, Male, Middle Aged, Multifactorial Inheritance, Phenotype, Prognosis, Risk Assessment, Sweden, Time Factors, United Kingdom}, issn = {1524-4636}, doi = {10.1161/ATVBAHA.120.314856}, author = {Hindy, George and Aragam, Krishna G and Ng, Kenney and Chaffin, Mark and Lotta, Luca A and Baras, Aris and Drake, Isabel and Orho-Melander, Marju and Melander, Olle and Kathiresan, Sekar and Khera, Amit V} } @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 {93, title = {Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.}, journal = {Cell}, volume = {180}, year = {2020}, month = {2020 02 06}, pages = {568-584.e23}, abstract = {

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n~= 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.

}, keywords = {Autistic Disorder, Case-Control Studies, Cell Lineage, Cerebral Cortex, Cohort Studies, Exome, Female, Gene Expression Regulation, Developmental, Gene Frequency, Genetic Predisposition to Disease, Humans, Male, Mutation, Missense, Neurobiology, Neurons, Phenotype, Sex Factors, Single-Cell Analysis, Whole Exome Sequencing}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.12.036}, author = {Satterstrom, F Kyle and Kosmicki, Jack A and Wang, Jiebiao and Breen, Michael S and De Rubeis, Silvia and An, Joon-Yong and Peng, Minshi and Collins, Ryan and Grove, Jakob and Klei, Lambertus and Stevens, Christine and Reichert, Jennifer and Mulhern, Maureen S and Artomov, Mykyta and Gerges, Sherif and Sheppard, Brooke and Xu, Xinyi and Bhaduri, Aparna and Norman, Utku and Brand, Harrison and Schwartz, Grace and Nguyen, Rachel and Guerrero, Elizabeth E and Dias, Caroline and Betancur, Catalina and Cook, Edwin H and Gallagher, Louise and Gill, Michael and Sutcliffe, James S and Thurm, Audrey and Zwick, Michael E and B{\o}rglum, Anders D and State, Matthew W and Cicek, A Ercument and Talkowski, Michael E and Cutler, David J and Devlin, Bernie and Sanders, Stephan J and Roeder, Kathryn and Daly, Mark J and Buxbaum, Joseph D} } @article {112, title = {Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 07 09}, pages = {145-161.e23}, abstract = {

Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.

}, keywords = {Alleles, Crops, Agricultural, Cytochrome P-450 Enzyme System, Ecotype, Epistasis, Genetic, Fruit, Gene Duplication, Gene Expression Regulation, Plant, Genome, Plant, Genomic Structural Variation, Genotype, Inbreeding, Lycopersicon esculentum, Molecular Sequence Annotation, Phenotype, Plant Breeding, Quantitative Trait Loci}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.05.021}, author = {Alonge, Michael and Wang, Xingang and Benoit, Matthias and Soyk, Sebastian and Pereira, Lara and Zhang, Lei and Suresh, Hamsini and Ramakrishnan, Srividya and Maumus, Florian and Ciren, Danielle and Levy, Yuval and Harel, Tom Hai and Shalev-Schlosser, Gili and Amsellem, Ziva and Razifard, Hamid and Caicedo, Ana L and Tieman, Denise M and Klee, Harry and Kirsche, Melanie and Aganezov, Sergey and Ranallo-Benavidez, T Rhyker and Lemmon, Zachary H and Kim, Jennifer and Robitaille, Gina and Kramer, Melissa and Goodwin, Sara and McCombie, W Richard and Hutton, Samuel and Van Eck, Joyce and Gillis, Jesse and Eshed, Yuval and Sedlazeck, Fritz J and van der Knaap, Esther and Schatz, Michael C and Lippman, Zachary B} } @article {141, title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 07 02}, pages = {46-59}, abstract = {

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait{\textquoteright}s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.05.004}, author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R} } @article {38, title = {Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Apr 24}, pages = {1613}, abstract = {

Less than 3\% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5\%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.

}, keywords = {Databases, Genetic, Diabetes Mellitus, Type 2, Disease, Gene Frequency, Genetic Testing, Genetic Variation, Humans, Obesity, Phenotype, Proteins, Respiratory Hypersensitivity, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-018-03911-8}, author = {Emdin, Connor A and Khera, Amit V and Chaffin, Mark and Klarin, Derek and Natarajan, Pradeep and Aragam, Krishna and Haas, Mary and Bick, Alexander and Zekavat, Seyedeh M and Nomura, Akihiro and Ardissino, Diego and Wilson, James G and Schunkert, Heribert and McPherson, Ruth and Watkins, Hugh and Elosua, Roberto and Bown, Matthew J and Samani, Nilesh J and Baber, Usman and Erdmann, Jeanette and Gupta, Namrata and Danesh, John and Chasman, Daniel and Ridker, Paul and Denny, Joshua and Bastarache, Lisa and Lichtman, Judith H and D{\textquoteright}Onofrio, Gail and Mattera, Jennifer and Spertus, John A and Sheu, Wayne H-H and Taylor, Kent D and Psaty, Bruce M and Rich, Stephen S and Post, Wendy and Rotter, Jerome I and Chen, Yii-Der Ida and Krumholz, Harlan and Saleheen, Danish and Gabriel, Stacey and Kathiresan, Sekar} } @article {14, title = {Disruption of the ATXN1-CIC complex causes a spectrum of neurobehavioral phenotypes in mice and humans.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Apr}, pages = {527-536}, abstract = {

Gain-of-function mutations in some genes underlie neurodegenerative conditions, whereas loss-of-function mutations in the same genes have distinct phenotypes. This appears to be the case with the protein ataxin 1 (ATXN1), which forms a transcriptional repressor complex with capicua (CIC). Gain of function of the complex leads to neurodegeneration, but ATXN1-CIC is also essential for survival. We set out to understand the functions of the ATXN1-CIC complex in the developing forebrain and found that losing this complex results in hyperactivity, impaired learning and memory, and abnormal maturation and maintenance of upper-layer cortical neurons. We also found that CIC activity in the hypothalamus and medial amygdala modulates social interactions. Informed by these neurobehavioral features in mouse mutants, we identified five individuals with de novo heterozygous truncating mutations in CIC who share similar clinical features, including intellectual disability, attention deficit/hyperactivity disorder (ADHD), and autism spectrum disorder. Our study demonstrates that loss of ATXN1-CIC complexes causes a spectrum of neurobehavioral phenotypes.

}, keywords = {Animals, Ataxin-1, Autism Spectrum Disorder, Cerebellum, Female, Humans, Intellectual Disability, Interpersonal Relations, Male, Mice, Nerve Tissue Proteins, Neurodegenerative Diseases, Nuclear Proteins, Phenotype, Repressor Proteins}, issn = {1546-1718}, doi = {10.1038/ng.3808}, author = {Lu, Hsiang-Chih and Tan, Qiumin and Rousseaux, Maxime W C and Wang, Wei and Kim, Ji-Yoen and Richman, Ronald and Wan, Ying-Wooi and Yeh, Szu-Ying and Patel, Jay M and Liu, Xiuyun and Lin, Tao and Lee, Yoontae and Fryer, John D and Han, Jing and Chahrour, Maria and Finnell, Richard H and Lei, Yunping and Zurita-Jimenez, Maria E and Ahimaz, Priyanka and Anyane-Yeboa, Kwame and Van Maldergem, Lionel and Lehalle, Daphne and Jean-Marcais, Nolwenn and Mosca-Boidron, Anne-Laure and Thevenon, Julien and Cousin, Margot A and Bro, Della E and Lanpher, Brendan C and Klee, Eric W and Alexander, Nora and Bainbridge, Matthew N and Orr, Harry T and Sillitoe, Roy V and Ljungberg, M Cecilia and Liu, Zhandong and Schaaf, Christian P and Zoghbi, Huda Y} } @article {13, title = {Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Apr}, pages = {618-624}, abstract = {

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the {\textquoteright}big data{\textquoteright} available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

}, keywords = {Animals, Base Sequence, Computational Biology, Evolution, Molecular, Genetic Variation, Genome, Humans, Mammals, Metagenomics, Phenotype, Primates, Vertebrates}, issn = {1546-1718}, doi = {10.1038/ng.3810}, author = {Huang, Yi-Fei and Gulko, Brad and Siepel, Adam} } @article {29, title = {Genetic association studies in cardiovascular diseases: Do we have enough power?}, journal = {Trends Cardiovasc Med}, volume = {27}, year = {2017}, month = {2017 Aug}, pages = {397-404}, abstract = {

Genetic association studies have a long history of delivering insightful results for cardiovascular disease (CVD) research. Beginning with early candidate gene studies, to genome-wide association studies, and now on to newer whole-genome sequencing studies, research in human genetics has enriched our understanding of the pathobiology of CVD. As these studies continue to expand, the issue of statistical power plays an important role in study design as well as the interpretation of results. We provide an overview of the component parts that determine statistical power and preview the future of CVD genetic association studies through this lens.

}, keywords = {Cardiovascular Diseases, Data Accuracy, Data Interpretation, Statistical, Genetic Association Studies, Genetic Markers, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Reproducibility of Results, Research Design, Risk Assessment, Risk Factors}, issn = {1873-2615}, doi = {10.1016/j.tcm.2017.03.005}, author = {Auer, Paul L and Stitziel, Nathan O} } @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} } @article {15, title = {Concerted Genetic Function in Blood Traits.}, journal = {Cell}, volume = {167}, year = {2016}, month = {2016 Nov 17}, pages = {1167-1169}, abstract = {

The hematopoietic system plays a major role in human health. Two studies by Astle et~al. and Chen~et~al. published in this issue of Cell use genome-wide association and functional genomics approaches to provide deep insights into the role of genetic variants in hematological traits. We~discuss these discoveries and future strategies toward completing our understanding of the genetic basis for variation in human traits.

}, keywords = {Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Phenotype, Polymorphism, Single Nucleotide}, issn = {1097-4172}, doi = {10.1016/j.cell.2016.10.055}, author = {Kim-Hellmuth, Sarah and Lappalainen, Tuuli} }