@article {812, title = {A genome-wide case-only test for the detection of digenic inheritance in human exomes.}, journal = {Proc Natl Acad Sci U S A}, volume = {117}, year = {2020}, month = {2020 08 11}, pages = {19367-19375}, abstract = {

Whole-exome sequencing (WES) has facilitated the discovery of genetic lesions underlying monogenic disorders. Incomplete penetrance and variable expressivity suggest a contribution of additional genetic lesions to clinical manifestations and outcome. Some monogenic disorders may therefore actually be digenic. However, only a few digenic disorders have been reported, all discovered by candidate gene approaches applied to at least one locus. We propose here a two-locus genome-wide test for detecting digenic inheritance in WES data. This approach uses the gene as the unit of analysis and tests all pairs of genes to detect pairwise gene {\texttimes} gene interactions underlying disease. It is a case-only method, which has several advantages over classic case-control tests, in particular by avoiding recruitment of controls. Our simulation studies based on real WES data identified two major sources of type I error inflation in this case-only test: linkage disequilibrium and population stratification. Both were corrected by specific procedures. Moreover, our case-only approach is more powerful than the corresponding case-control test for detecting digenic interactions in various population stratification scenarios. Finally, we confirmed the potential of our unbiased, genome-wide approach by successfully identifying a previously reported digenic lesion in patients with craniosynostosis. Our case-only test is a powerful and timely tool for detecting digenic inheritance in WES data from patients.

}, keywords = {Craniosynostoses, Epistasis, Genetic, Exome, Genetic Linkage, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Multifactorial Inheritance, Whole Exome Sequencing}, issn = {1091-6490}, doi = {10.1073/pnas.1920650117}, author = {Kerner, Gaspard and Bouaziz, Matthieu and Cobat, Aurelie and Bigio, Benedetta and Timberlake, Andrew T and Bustamante, Jacinta and Lifton, Richard P and Casanova, Jean-Laurent and Abel, Laurent} } @article {875, 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 {628, title = {Dissecting the Causal Mechanism of X-Linked Dystonia-Parkinsonism by Integrating Genome and Transcriptome Assembly.}, journal = {Cell}, volume = {172}, year = {2018}, month = {2018 02 22}, pages = {897-909.e21}, abstract = {

X-linked Dystonia-Parkinsonism (XDP) is a Mendelian~neurodegenerative disease that is endemic to the Philippines and is associated with a founder haplotype. We integrated multiple genome and transcriptome assembly technologies to narrow the causal mutation to the TAF1 locus, which included a SINE-VNTR-Alu (SVA) retrotransposition into intron 32 of the gene. Transcriptome analyses identified decreased expression of the canonical cTAF1 transcript among XDP probands, and de novo assembly~across multiple pluripotent stem-cell-derived neuronal lineages discovered aberrant TAF1 transcription that involved alternative splicing and intron retention (IR) in proximity to the SVA that was anti-correlated with overall TAF1 expression. CRISPR/Cas9 excision of the SVA rescued this XDP-specific~transcriptional signature and normalized TAF1 expression in probands. These data suggest an SVA-mediated aberrant transcriptional mechanism associated with XDP and may provide a roadmap for layered technologies and integrated assembly-based analyses for other unsolved Mendelian disorders.

}, keywords = {Alternative Splicing, Alu Elements, Base Sequence, Cohort Studies, CRISPR-Cas Systems, Dystonic Disorders, Family, Female, Genetic Diseases, X-Linked, Genetic Loci, Genome, Human, Haplotypes, High-Throughput Nucleotide Sequencing, Histone Acetyltransferases, Humans, Induced Pluripotent Stem Cells, Introns, Male, Minisatellite Repeats, Models, Genetic, Nerve Degeneration, Neural Stem Cells, Neurons, RNA, Messenger, Short Interspersed Nucleotide Elements, TATA-Binding Protein Associated Factors, Transcription Factor TFIID, Transcriptome}, issn = {1097-4172}, doi = {10.1016/j.cell.2018.02.011}, author = {Aneichyk, Tatsiana and Hendriks, William T and Yadav, Rachita and Shin, David and Gao, Dadi and Vaine, Christine A and Collins, Ryan L and Domingo, Aloysius and Currall, Benjamin and Stortchevoi, Alexei and Multhaupt-Buell, Trisha and Penney, Ellen B and Cruz, Lilian and Dhakal, Jyotsna and Brand, Harrison and Hanscom, Carrie and Antolik, Caroline and Dy, Marisela and Ragavendran, Ashok and Underwood, Jason and Cantsilieris, Stuart and Munson, Katherine M and Eichler, Evan E and Acu{\~n}a, Patrick and Go, Criscely and Jamora, R Dominic G and Rosales, Raymond L and Church, Deanna M and Williams, Stephen R and Garcia, Sarah and Klein, Christine and M{\"u}ller, Ulrich and Wilhelmsen, Kirk C and Timmers, H T Marc and Sapir, Yechiam and Wainger, Brian J and Henderson, Daniel and Ito, Naoto and Weisenfeld, Neil and Jaffe, David and Sharma, Nutan and Breakefield, Xandra O and Ozelius, Laurie J and Bragg, D Cristopher and Talkowski, Michael E} } @article {404, title = {Gene-based segregation method for identifying rare variants in family-based sequencing studies.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 05}, pages = {309-319}, abstract = {

Whole-exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene-based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission. This test takes into account different degrees of relatedness in families, the number of functional rare variants in the gene, and their minor allele frequencies in the corresponding population. In addition, a weighted version of this test allows incorporating additional subject phenotypes to improve statistical power. We show via simulations that the GESE and weighted GESE tests maintain appropriate type I error rate, and have greater power than several commonly used region-based methods. We apply our method to whole-exome sequencing data from 49 extended pedigrees with severe, early-onset chronic obstructive pulmonary disease (COPD) in the Boston Early-Onset COPD study (BEOCOPD) and identify several promising candidate genes. Our proposed methods show great potential for identifying rare coding variants of large effect and high penetrance for family-based sequencing data. The proposed tests are implemented in an R package that is available on CRAN (https://cran.r-project.org/web/packages/GESE/).

}, keywords = {Age of Onset, Boston, Computer Simulation, Databases, Genetic, Family, Genetic Variation, Genome, Human, Humans, Models, Genetic, Penetrance, Pulmonary Disease, Chronic Obstructive, Reference Standards, Sequence Analysis, DNA}, issn = {1098-2272}, doi = {10.1002/gepi.22037}, author = {Qiao, Dandi and Lange, Christoph and Laird, Nan M and Won, Sungho and Hersh, Craig P and Morrow, Jarrett and Hobbs, Brian D and Lutz, Sharon M and Ruczinski, Ingo and Beaty, Terri H and Silverman, Edwin K and Cho, Michael H} } @article {372, title = {Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 02 28}, pages = {1633-1648}, abstract = {

We developed an algorithm, HMZDelFinder, that uses whole exome sequencing (WES) data to identify rare and intragenic homozygous and hemizygous (HMZ) deletions that may represent complete loss-of-function of the indicated gene. HMZDelFinder was applied to 4866 samples in the Baylor-Hopkins Center for Mendelian Genomics (BHCMG) cohort and detected 773 HMZ deletion calls (567 homozygous or 206 hemizygous) with an estimated sensitivity of 86.5\% (82\% for single-exonic and 88\% for multi-exonic calls) and precision of 78\% (53\% single-exonic and 96\% for multi-exonic calls). Out of 773 HMZDelFinder-detected deletion calls, 82 were subjected to array comparative genomic hybridization (aCGH) and/or breakpoint PCR and 64 were confirmed. These include 18 single-exon deletions out of which 8 were exclusively detected by HMZDelFinder and not by any of seven other CNV detection tools examined. Further investigation of the 64 validated deletion calls revealed at least 15 pathogenic HMZ deletions. Of those, 7 accounted for 17-50\% of pathogenic CNVs in different disease cohorts where 7.1-11\% of the molecular diagnosis solved rate was attributed to CNVs. In summary, we present an algorithm to detect rare, intragenic, single-exon deletion CNVs using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

}, keywords = {Algorithms, Alternative Splicing, Cohort Studies, Computational Biology, Consanguinity, Datasets as Topic, DNA Copy Number Variations, Exome, Genetic Diseases, Inborn, Hemizygote, High-Throughput Nucleotide Sequencing, Homozygote, Humans, Inheritance Patterns, Models, Genetic, Pedigree, Reproducibility of Results, Sequence Deletion, Workflow}, issn = {1362-4962}, doi = {10.1093/nar/gkw1237}, author = {Gambin, Tomasz and Akdemir, Zeynep C and Yuan, Bo and Gu, Shen and Chiang, Theodore and Carvalho, Claudia M B and Shaw, Chad and Jhangiani, Shalini and Boone, Philip M and Eldomery, Mohammad K and Karaca, Ender and Bayram, Yavuz and Stray-Pedersen, Asbj{\o}rg and Muzny, Donna and Charng, Wu-Lin and Bahrambeigi, Vahid and Belmont, John W and Boerwinkle, Eric and Beaudet, Arthur L and Gibbs, Richard A and Lupski, James R} } @article {47, title = {Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Sep}, pages = {502-11}, abstract = {

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

}, keywords = {Computer Simulation, Genetic Association Studies, Genetic Variation, Humans, Likelihood Functions, Models, Genetic, Phenotype}, issn = {1098-2272}, doi = {10.1002/gepi.21985}, author = {Wang, Longfei and Lee, Sungyoung and Gim, Jungsoo and Qiao, Dandi and Cho, Michael and Elston, Robert C and Silverman, Edwin K and Won, Sungho} } @article {45, title = {FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Sep}, pages = {475-85}, abstract = {

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

}, keywords = {Alleles, Chromosomes, Human, X, Female, Genes, X-Linked, Genetic Variation, Humans, Models, Genetic, Pedigree, Phenotype, X Chromosome Inactivation}, issn = {1098-2272}, doi = {10.1002/gepi.21979}, author = {Choi, Sungkyoung and Lee, Sungyoung and Qiao, Dandi and Hardin, Megan and Cho, Michael H and Silverman, Edwin K and Park, Taesung and Won, Sungho} } @article {415, title = {Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Oct 06}, pages = {846-859}, abstract = {

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error.

}, keywords = {Datasets as Topic, Ethnic Groups, Family, Female, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Male, Models, Genetic, Pedigree, Phenotype, Research Design}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.08.015}, author = {Sul, Jae Hoon and Cade, Brian E and Cho, Michael H and Qiao, Dandi and Silverman, Edwin K and Redline, Susan and Sunyaev, Shamil} } @article {78, title = {Mechanisms underlying structural variant formation in genomic disorders.}, journal = {Nat Rev Genet}, volume = {17}, year = {2016}, month = {2016 Apr}, pages = {224-38}, abstract = {

With the recent burst of technological developments in genomics, and the clinical implementation of genome-wide assays, our understanding of the molecular basis of genomic disorders, specifically the contribution of structural variation to disease burden, is evolving quickly. Ongoing studies have revealed a ubiquitous role for genome architecture in the formation of structural variants at a given locus, both in DNA recombination-based processes and in replication-based processes. These reports showcase the influence of repeat sequences on genomic stability and structural variant complexity and also highlight the tremendous plasticity and dynamic nature of our genome in evolution, health and disease susceptibility.

}, keywords = {Gene Rearrangement, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genomic Instability, Homologous Recombination, Humans, Models, Genetic, Mutation, Repetitive Sequences, Nucleic Acid}, issn = {1471-0064}, doi = {10.1038/nrg.2015.25}, author = {Carvalho, Claudia M B and Lupski, James R} } @article {43, title = {PADRE: Pedigree-Aware Distant-Relationship Estimation.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Jul 07}, pages = {154-62}, abstract = {

Accurate estimation of shared ancestry is an important component of many genetic studies; current prediction tools accurately estimate pairwise genetic relationships up to the ninth degree. Pedigree-aware distant-relationship estimation (PADRE) combines relationship likelihoods generated by estimation of recent shared ancestry (ERSA) with likelihoods from family networks reconstructed by pedigree reconstruction and identification of a maximum unrelated set (PRIMUS), improving the power to detect distant relationships between pedigrees. Using PADRE, we estimated relationships from simulated pedigrees and three extended pedigrees, correctly predicting 20\% more fourth- through ninth-degree simulated relationships than when using ERSA alone. By leveraging pedigree information, PADRE can even identify genealogical relationships between individuals who are genetically unrelated. For example, although 95\% of 13(th)-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50\% of 13(th)-degree relationships to within one degree of relatedness. The improvement in prediction accuracy was consistent between simulated and actual pedigrees. We also applied PADRE to the HapMap3 CEU samples and report new cryptic relationships and validation of previously described relationships between families. PADRE greatly expands the range of relationships that can be estimated by using genetic data in pedigrees.

}, keywords = {Algorithms, Female, Haplotypes, Humans, Male, Models, Genetic, Pedigree, Reproducibility of Results}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.05.020}, author = {Staples, Jeffrey and Witherspoon, David J and Jorde, Lynn B and Nickerson, Deborah A and Below, Jennifer E and Huff, Chad D} } @article {185, title = {Absence of heterozygosity due to template switching during replicative rearrangements.}, journal = {Am J Hum Genet}, volume = {96}, year = {2015}, month = {2015 Apr 02}, pages = {555-64}, abstract = {

We investigated complex genomic rearrangements (CGRs) consisting of triplication copy-number variants (CNVs) that were accompanied by extended regions of copy-number-neutral absence of heterozygosity (AOH) in subjects with multiple congenital abnormalities. Molecular analyses provided observational evidence that in humans, post-zygotically generated CGRs can lead to regional uniparental disomy (UPD) due to template switches between homologs versus sister chromatids by using microhomology to prime DNA replication-a prediction of the replicative repair model, MMBIR. Our findings suggest that replication-based mechanisms might underlie the formation of diverse types of genomic alterations (CGRs and AOH) implicated in constitutional disorders.

}, keywords = {Base Sequence, DNA Copy Number Variations, DNA Repair, DNA Replication, Gene Rearrangement, Humans, In Situ Hybridization, Fluorescence, Loss of Heterozygosity, Models, Genetic, Molecular Sequence Data, Netherlands, Polymerase Chain Reaction, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Uniparental Disomy}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2015.01.021}, author = {Carvalho, Claudia M B and Pfundt, Rolph and King, Daniel A and Lindsay, Sarah J and Zuccherato, Luciana W and Macville, Merryn V E and Liu, Pengfei and Johnson, Diana and Stankiewicz, Pawel and Brown, Chester W and Shaw, Chad A and Hurles, Matthew E and Ira, Grzegorz and Hastings, P J and Brunner, Han G and Lupski, James R} } @article {120, title = {Nonrecurrent 17p11.2p12 Rearrangement Events that Result in Two Concomitant Genomic Disorders: The PMP22-RAI1 Contiguous Gene Duplication Syndrome.}, journal = {Am J Hum Genet}, volume = {97}, year = {2015}, month = {2015 Nov 05}, pages = {691-707}, abstract = {

The genomic duplication associated with Potocki-Lupski syndrome (PTLS) maps in close proximity to the duplication associated with Charcot-Marie-Tooth disease type 1A (CMT1A). PTLS is characterized by hypotonia, failure to thrive, reduced body weight, intellectual disability, and autistic features. CMT1A is a common autosomal dominant distal symmetric peripheral polyneuropathy. The key dosage-sensitive genes RAI1 and PMP22 are respectively associated with PTLS and CMT1A. Recurrent duplications accounting for the majority of subjects with these conditions are mediated by nonallelic homologous recombination between distinct low-copy repeat (LCR) substrates. The LCRs flanking a contiguous genomic interval encompassing both RAI1 and PMP22 do not share extensive homology; thus, duplications encompassing both loci are rare and potentially generated by a different mutational mechanism. We characterized genomic rearrangements that simultaneously duplicate PMP22 and RAI1, including nine potential complex genomic rearrangements, in 23 subjects by high-resolution array comparative genomic hybridization and breakpoint junction sequencing. Insertions and microhomologies were found at the breakpoint junctions, suggesting potential replicative mechanisms for rearrangement formation. At the breakpoint junctions of these nonrecurrent rearrangements, enrichment of repetitive DNA sequences was observed, indicating that they might predispose to genomic instability and rearrangement. Clinical evaluation revealed blended PTLS and CMT1A phenotypes with a potential earlier onset of neuropathy. Moreover, additional clinical findings might be observed due to the extra duplicated material included in the rearrangements. Our genomic analysis suggests replicative mechanisms as a predominant mechanism underlying PMP22-RAI1 contiguous gene duplications and provides further evidence supporting the role of complex genomic architecture in genomic instability.

}, keywords = {Abnormalities, Multiple, Charcot-Marie-Tooth Disease, Child, Child, Preschool, Chromosome Disorders, Chromosome Duplication, Chromosomes, Human, Pair 17, Comparative Genomic Hybridization, Female, Follow-Up Studies, Gene Duplication, Gene Rearrangement, Genome, Human, Genomics, Humans, Infant, Male, Models, Genetic, Myelin Proteins, Phenotype, Prognosis, Recombination, Genetic, Transcription Factors}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2015.10.003}, author = {Yuan, Bo and Harel, Tamar and Gu, Shen and Liu, Pengfei and Burglen, Lydie and Chantot-Bastaraud, Sandra and Gelowani, Violet and Beck, Christine R and Carvalho, Claudia M B and Cheung, Sau Wai and Coe, Andrew and Malan, Val{\'e}rie and Munnich, Arnold and Magoulas, Pilar L and Potocki, Lorraine and Lupski, James R} } @article {266, title = {A general framework for estimating the relative pathogenicity of human genetic variants.}, journal = {Nat Genet}, volume = {46}, year = {2014}, month = {2014 Mar}, pages = {310-5}, abstract = {

Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.

}, keywords = {Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, INDEL Mutation, Models, Genetic, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Selection, Genetic, Support Vector Machine}, issn = {1546-1718}, doi = {10.1038/ng.2892}, author = {Kircher, Martin and Witten, Daniela M and Jain, Preti and O{\textquoteright}Roak, Brian J and Cooper, Gregory M and Shendure, Jay} } @article {229, title = {Parental somatic mosaicism is underrecognized and influences recurrence risk of genomic disorders.}, journal = {Am J Hum Genet}, volume = {95}, year = {2014}, month = {2014 Aug 07}, pages = {173-82}, abstract = {

New human mutations are thought to originate in germ cells, thus making a recurrence of the same mutation in a sibling exceedingly rare. However, increasing sensitivity of genomic technologies has anecdotally revealed mosaicism for mutations in somatic tissues of apparently healthy parents. Such somatically mosaic parents might also have germline mosaicism that can potentially cause unexpected intergenerational recurrences. Here, we show that somatic mosaicism for transmitted mutations among parents of children with simplex genetic disease is more common than currently appreciated. Using the sensitivity of individual-specific breakpoint PCR, we prospectively screened 100 families with children affected by genomic disorders due to rare deletion copy-number variants (CNVs) determined to be de novo by clinical analysis of parental DNA. Surprisingly, we identified four cases of low-level somatic mosaicism for the transmitted CNV in DNA isolated from parental blood. Integrated probabilistic modeling of gametogenesis developed in response to our observations predicts that mutations in parental blood increase recurrence risk substantially more than parental mutations confined to the germline. Moreover, despite the fact that maternally transmitted mutations are the minority of alleles, our model suggests that sexual dimorphisms in gametogenesis result in a greater proportion of somatically mosaic transmitting mothers who are thus at increased risk of recurrence. Therefore, somatic mosaicism together with sexual differences in gametogenesis might explain a considerable fraction of unexpected recurrences of X-linked recessive disease. Overall, our results underscore an important role for somatic mosaicism and mitotic replicative mutational mechanisms in transmission genetics.

}, keywords = {Cell Division, DNA Copy Number Variations, Female, Gametogenesis, Genetic Diseases, Inborn, Genomics, Germ Cells, Germ-Line Mutation, Humans, Male, Models, Genetic, Mosaicism, Mutation, Pedigree, Prospective Studies, Recurrence, Risk, Sex Characteristics, Smith-Magenis Syndrome}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.07.003}, author = {Campbell, Ian M and Yuan, Bo and Robberecht, Caroline and Pfundt, Rolph and Szafranski, Przemyslaw and McEntagart, Meriel E and Nagamani, Sandesh C S and Erez, Ayelet and Bartnik, Magdalena and Wi{\'s}niowiecka-Kowalnik, Barbara and Plunkett, Katie S and Pursley, Amber N and Kang, Sung-Hae L and Bi, Weimin and Lalani, Seema R and Bacino, Carlos A and Vast, Mala and Marks, Karen and Patton, Michael and Olofsson, Peter and Patel, Ankita and Veltman, Joris A and Cheung, Sau Wai and Shaw, Chad A and Vissers, Lisenka E L M and Vermeesch, Joris R and Lupski, James R and Stankiewicz, Pawe{\l}} } @article {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M} } @article {316, title = {A unified method for detecting secondary trait associations with rare variants: application to sequence data.}, journal = {PLoS Genet}, volume = {8}, year = {2012}, month = {2012}, pages = {e1003075}, abstract = {

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

}, keywords = {Blood Pressure, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Italy, Lipoproteins, LDL, Models, Genetic, Phenotype, Quantitative Trait Loci, Software}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003075}, author = {Liu, Dajiang J and Leal, Suzanne M} }