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  • 1
    Language: English
    In: BMC Genomics, 01 September 2011, Vol.12(1), p.460
    Description: Abstract Background A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers. Method We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity. Results Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD). Conclusion Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.
    Keywords: Biology
    ISSN: 1471-2164
    E-ISSN: 1471-2164
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  • 2
    Language: English
    In: Cancer Research, 07/01/2018, Vol.78(13 Supplement), pp.2287-2287
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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  • 3
    Language: English
    In: BMC Proceedings, 01 November 2011, Vol.5(Suppl 9), p.S55
    Description: Abstract Evolutionary genetic models predict that the cumulative effect of rare deleterious mutations across the genome—known as mutational load burden—increases the susceptibility to complex disease. To test the mutational load burden hypothesis, we adopted a two-tiered approach: assessing the impact of whole-exome minor allele load burden and then conducting individual-gene screening. For our primary analysis, we examined various minor allele frequency (MAF) thresholds and weighting schemes to examine the overall effect of minor allele load on affection status. We found a consistent association between minor allele load and affection status, but this effect did not markedly increase within rare and/or functional single-nucleotide polymorphisms (SNPs). Our follow-up analysis considered minor allele load in individual genes to see whether only one or a few genes were driving the overall effect. Examining our most significant result—minor allele load of nonsynonymous SNPs with MAF 〈 2.4%—we detected no significantly associated genes after Bonferroni correction for multiple testing. After moderately significant genes (p 〈 0.05) were removed, the overall effect of rare nonsynonymous allele load remained significant. Overall, we did not find clear support for mutational load burden on affection status; however, these results are ultimately dependent on and limited by the nature of the Genetic Analysis Workshop 17 simulation.
    Keywords: Medicine
    ISSN: 1753-6561
    E-ISSN: 1753-6561
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  • 4
    Language: English
    In: BMC Genomics, Sept 23, 2011, Vol.12, p.460
    Description: Background A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers. Method We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity. Results Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD). Conclusion Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.
    Keywords: Genomes -- Physiological Aspects ; Genomes -- Research ; Homozygosity -- Research ; Phenotypes -- Physiological Aspects ; Phenotypes -- Research ; Single Nucleotide Polymorphisms -- Research
    ISSN: 1471-2164
    Source: Cengage Learning, Inc.
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  • 5
    Language: English
    In: BMC Genomics, Sept 23, 2011, Vol.12, p.460
    Description: Background A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers. Method We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity. Results Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD). Conclusion Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.
    Keywords: Genomes -- Physiological Aspects ; Genomes -- Research ; Homozygosity -- Research ; Phenotypes -- Physiological Aspects ; Phenotypes -- Research ; Single Nucleotide Polymorphisms -- Research
    ISSN: 1471-2164
    Source: Cengage Learning, Inc.
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  • 6
    In: Nature Neuroscience, 2017
    Description: The postsynaptic density (PSD) contains a collection of scaffold proteins used for assembling synaptic signaling complexes. However, it is not known how the core-scaffold machinery associates in protein-interaction networks or how proteins encoded by genes involved in complex brain disorders are distributed through spatiotemporal protein complexes. Here using immunopurification, proteomics and bioinformatics, we isolated 2,876 proteins across 41 in vivo interactomes and determined their protein domain composition, correlation to gene expression levels and developmental integration to the PSD. We defined clusters for enrichment of schizophrenia, autism spectrum disorders, developmental delay and intellectual disability risk factors at embryonic day 14 and adult PSD in mice. Mutations in highly connected nodes alter protein-protein interactions modulating macromolecular complexes enriched in disease risk candidates. These results were integrated into a software platform, Synaptic Protein/Pathways Resource (SyPPRes), enabling the prioritization of disease risk factors and their placement within synaptic protein interaction networks.
    Keywords: Brain Diseases – Genetic Aspects ; Brain Diseases – Research ; Brain Diseases – Development and Progression ; Protein-Protein Interactions – Genetic Aspects ; Protein-Protein Interactions – Research ; Protein-Protein Interactions – Physiological Aspects ; Synaptic Transmission – Physiological Aspects ; Synaptic Transmission – Genetic Aspects ; Synaptic Transmission – Research;
    ISSN: 1097-6256
    E-ISSN: 1546-1726
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  • 7
    In: Marshall, Christian R; Howrigan, Daniel P; Merico, Daniele; Thiruvahindrapuram, Bhooma; Wu, Wenting; Greer, Douglas S; Antaki, Danny; Shetty, Aniket; Holmans, Peter A; Pinto, Dalila; Gujral, Madhusudan; Brandler, William M; Malhotra, Dheeraj; Wang, Zhouzhi; Fajarado, Karin V Fuentes; Maile, Michelle S; Ripke, Stephan; Agartz, Ingrid; Albus, Margot; Alexander, Madeline; Amin, Farooq; Atkins, Joshua; Bacanu, Silviu A; Belliveau, Richard A; Bergen, Sarah E; Bertalan, Marcelo; Bevilacqua, Elizabeth; Bigdeli, Tim B; Black, Donald W; Bruggeman, Richard; Buccola, Nancy G; Buckner, Randy L; Bulik-Sullivan, Brendan; Byerley, William; Cahn, Wiepke; Cai, Guiqing; Cairns, Murray J; Campion, Dominique; Cantor, Rita M; Carr, Vaughan J; Carrera, Noa; Catts, Stanley V; Chambert, Kimberley D; Cheng, Wei; Cloninger, C Robert; Cohen, David; Cormican, Paul; Craddock, Nick; Crespo-Facorro, Benedicto; Crowley, James J; Curtis, David; Davidson, Michael; Davis, Kenneth L; Degenhardt, Franziska; Del Favero, Jurgen; DeLisi, Lynn E; Dikeos, Dimitris; Dinan, Timothy; Djurovic, Srdjan; Donohoe, Gary; Drapeau, Elodie; Duan, Jubao; Dudbridge, Frank; Eichhammer, Peter; Eriksson, Johan; Escott-Price, Valentina; Essioux, Laurent; Fanous, Ayman H; Farh, Kai-How; Farrell, Martilias S; Frank, Josef; Franke, Lude; Freedman, Robert; Freimer, Nelson B; Friedman, Joseph I; Forstner, Andreas J; Fromer, Menachem; Genovese, Giulio; Georgieva, Lyudmila; Gershon, Elliot S; Giegling, Ina; Giusti-Rodríguez, Paola; Godard, Stephanie; Goldstein, Jacqueline I; Gratten, Jacob; de Haan, Lieuwe; Hamshere, Marian L; Hansen, Mark; Hansen, Thomas; Haroutunian, Vahram; Hartmann, Annette M; Henskens, Frans A; Herms, Stefan; Hirschhorn, Joel N; Hoffmann, Per; Hofman, Andrea; Huang, Hailiang; Ikeda, Masashi; Joa, Inge; Kähler, Anna K; Kahn, René S; Kalaydjieva, Luba; Karjalainen, Juha; Kavanagh, David; Keller, Matthew C; Kelly, Brian J; Kennedy, James L; Kim, Yunjung; Knowles, James A; Konte, Bettina; Laurent, Claudine; Lee, Phil; Lee, S Hong; Legge, Sophie E; Lerer, Bernard; Levy, Deborah L; Liang, Kung-Yee; Lieberman, Jeffrey; Lönnqvist, Jouko; Loughland, Carmel M; Magnusson, Patrik K E; Maher, Brion S; Maier, Wolfgang; Mallet, Jacques; Mattheisen, Manuel; Mattingsdal, Morten; McCarley, Robert W; McDonald, Colm; McIntosh, Andrew M; Meier, Sandra; Meijer, Carin J; Melle, Ingrid; Mesholam-Gately, Raquelle I; Metspalu, Andres; Michie, Patricia T; Milani, Lili; Milanova, Vihra; Mokrab, Younes; Morris, Derek W; Müller-Myhsok, Bertram; Murphy, Kieran C; Murray, Robin M; Myin-Germeys, Inez; Nenadic, Igor; Nertney, Deborah A; Nestadt, Gerald; Nicodemus, Kristin K; Nisenbaum, Laura; Nordin, Annelie; O'Callaghan, Eadbhard; O'Dushlaine, Colm; Oh, Sang-Yun; Olincy, Ann; Olsen, Line; O'Neill, F Anthony; Van Os, Jim; Pantelis, Christos; Papadimitriou, George N; Parkhomenko, Elena; Pato, Michele T; Paunio, Tiina; Perkins, Diana O; Pers, Tune H; Pietiläinen, Olli; Pimm, Jonathan; Pocklington, Andrew J; Powell, John; Price, Alkes; Pulver, Ann E; Purcell, Shaun M; Quested, Digby; Rasmussen, Henrik B; Reichenberg, Abraham; Reimers, Mark A; Richards, Alexander L; Roffman, Joshua L; Roussos, Panos; Ruderfer, Douglas M; Salomaa, Veikko; Sanders, Alan R; Savitz, Adam; Schall, Ulrich; Schulze, Thomas G; Schwab, Sibylle G; Scolnick, Edward M; Scott, Rodney J; Seidman, Larry J; Shi, Jianxin; Silverman, Jeremy M; Smoller, Jordan W; Söderman, Erik; Spencer, Chris C A; Stahl, Eli A; Strengman, Eric; Strohmaier, Jana; Stroup, T Scott; Suvisaari, Jaana; Svrakic, Dragan M; Szatkiewicz, Jin P; Thirumalai, Srinivas; Tooney, Paul A; Veijola, Juha; Visscher, Peter M; Waddington, John; Walsh, Dermot; Webb, Bradley T; Weiser, Mark; Wildenauer, Dieter B; Williams, Nigel M; Williams, Stephanie; Witt, Stephanie H; Wolen, Aaron R; Wormley, Brandon K; Wray, Naomi R; Wu, Jing Qin; Zai, Clement C; Adolfsson, Rolf; Andreassen, Ole A; Blackwood, Douglas H R; Bramon, Elvira; Buxbaum, Joseph D; Cichon, Sven; Collier, David A; Corvin, Aiden; Daly, Mark J; Darvasi, Ariel; Domenici, Enrico; Esko, Tõnu; Gejman, Pablo V; Gill, Michael; Gurling, Hugh; Hultman, Christina M; Iwata, Nakao; Jablensky, Assen V; Jönsson, Erik G; Kendler, Kenneth S; Kirov, George; Knight, Jo; Levinson, Douglas F; Li, Qingqin S; McCarroll, Steven A; McQuillin, Andrew; Moran, Jennifer L; Mowry, Bryan J; Nöthen, Markus M; Ophoff, Roel A; Owen, Michael J; Palotie, Aarno; Pato, Carlos N; Petryshen, Tracey L; Posthuma, Danielle; Rietschel, Marcella; Riley, Brien P; Rujescu, Dan; Sklar, Pamela; St Clair, David; Walters, James T R; Werge, Thomas; Sullivan, Patrick F; O'Donovan, Michael C; Scherer, Stephen W; Neale, Benjamin M; Sebat, Jonathan (2016). Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nature Genetics 49 (1), 27-35
    Description: Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 x 10(-15)), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 x 10(-6)). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 x 10(-11)) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 x 10(-5)). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.
    Keywords: Gene ; 16p11.2 ; Autism ; Risk ; Cnvs ; Rearrangements ; Duplications ; Phenotypes ; Disorders ; Mutations
    ISSN: 10614036
    E-ISSN: 15461718
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  • 8
    In: Nature Genetics, 2017
    Description: Recent research has uncovered an important role for de novo variation in neurodevelopmental disorders. Using aggregated data from 9,246 families with autism spectrum disorder, intellectual disability, or developmental delay, we found that [similar]1/3 of de novo variants are independently present as standing variation in the Exome Aggregation Consortium's cohort of 60,706 adults, and these de novo variants do not contribute to neurodevelopmental risk. We further used a loss-of-function (LoF)-intolerance metric, pLI, to identify a subset of LoF-intolerant genes containing the observed signal of associated de novo protein-truncating variants (PTVs) in neurodevelopmental disorders. LoF-intolerant genes also carry a modest excess of inherited PTVs, although the strongest de novo-affected genes contribute little to this excess, thus suggesting that the excess of inherited risk resides in lower-penetrant genes. These findings illustrate the importance of population-based reference cohorts for the interpretation of candidate pathogenic variants, even for analyses of complex diseases and de novo variation.
    Keywords: Central Nervous System Diseases -- Genetic Aspects ; Central Nervous System Diseases -- Development And Progression ; Central Nervous System Diseases -- Care And Treatment ; Genetic Variation -- Health Aspects ; Cellular Proteins -- Health Aspects;
    ISSN: 1061-4036
    E-ISSN: 1546-1718
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  • 9
    Language: English
    In: Ganna, A., G. Genovese, D. P. Howrigan, A. Byrnes, M. Kurki, S. M. Zekavat, C. W. Whelan, et al. 2016. “Ultra-rare disruptive and damaging mutations influence educational attainment in the general population.” Nature neuroscience 19 (12): 1563-1565. doi:10.1038/nn.4404. http://dx.doi.org/10.1038/nn.4404.
    Description: Disruptive and damaging ultra-rare variants (URVs) in highly constrained (HC) genes are enriched in individuals with neurodevelopmental disorders. In the general population, this class of variants was associated with a decrease in years of education (YOE; −3.1 months; P-value=3.3×10−8). This effect was stronger among high brain-expressed genes and explained more YOE variance than pathogenic copy number variation, but less than common variants. Disruptive and damaging URVs in HC genes influence the determinants of YOE in the general population.
    Keywords: Article;
    ISSN: 10976256
    E-ISSN: 15461726
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  • 10
    Language: English
    In: PLoS Genetics, 2012, Vol.8(4), p.e1002656
    Description: Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time. ; Inbreeding occurs when genetic relatives have offspring. Because all humans are related to one another, even if very distantly, all people are inbred to various degrees. From a genetic standpoint, it is well known that inbreeding increases the risk that a child will have a rare recessive genetic disease, but there is also increasing interest in understanding whether inbreeding is a risk factor for more common, complex disorders such as schizophrenia. In this investigation, we used single-nucleotide polymorphism data to quantify the degree to which 9,388 schizophrenia cases and 12,456 controls were inbred, and we tested the hypothesis that people whose genome shows higher evidence of being inbred are at higher risk of having schizophrenia. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in inbreeding. This finding is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia, and it suggests that genetic variants that increase the risk of schizophrenia have been selected against over evolutionary time.
    Keywords: Research Article ; Biology ; Medicine ; Genetics And Genomics ; Mental Health ; Evolutionary Biology
    ISSN: 1553-7390
    E-ISSN: 1553-7404
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