@article {549, title = {Global genetic insight contributed by consanguineous Pakistani families segregating hearing loss.}, journal = {Hum Mutat}, volume = {40}, year = {2019}, month = {2019 Jan}, pages = {53-72}, abstract = {

Consanguineous Pakistani pedigrees segregating deafness have contributed decisively to the discovery of 31 of the 68 genes associated with nonsyndromic autosomal recessive hearing loss (HL) worldwide. In this study, we utilized genome-wide genotyping, Sanger and exome sequencing to identify 163 DNA variants in 41 previously reported HL genes segregating in 321 Pakistani families. Of these, 70 (42.9\%) variants identified in 29 genes are novel. As expected from genetic studies of disorders segregating in consanguineous families, the majority of affected individuals (94.4\%) are homozygous for HL-associated variants, with the other variants being compound heterozygotes. The five most common HL genes in the Pakistani population are SLC26A4, MYO7A, GJB2, CIB2 and HGF, respectively. Our study provides a profile of the genetic etiology of HL in Pakistani families, which will allow for the development of more efficient genetic diagnostic tools, aid in accurate genetic counseling, and guide application of future gene-based therapies. These findings are also valuable in interpreting pathogenicity of variants that are potentially associated with HL in individuals of all ancestries. The Pakistani population, and its infrastructure for studying human genetics, will continue to be valuable to gene discovery for HL and other inherited disorders.

}, issn = {1098-1004}, doi = {10.1002/humu.23666}, author = {Richard, Elodie M and Santos-Cortez, Regie Lyn P and Faridi, Rabia and Rehman, Atteeq U and Lee, Kwanghyuk and Shahzad, Mohsin and Acharya, Anushree and Khan, Asma A and Imtiaz, Ayesha and Chakchouk, Imen and Takla, Christina and Abbe, Izoduwa and Rafeeq, Maria and Liaqat, Khurram and Chaudhry, Taimur and Bamshad, Michael J and Nickerson, Deborah A and Schrauwen, Isabelle and Khan, Shaheen N and Morell, Robert J and Zafar, Saba and Ansar, Muhammad and Ahmed, Zubair M and Ahmad, Wasim and Riazuddin, Sheikh and Friedman, Thomas B and Leal, Suzanne M and Riazuddin, Saima} } @article {13, title = {A genome-wide association study of congenital cardiovascular left-sided lesions shows association with a locus on chromosome 20.}, journal = {Hum Mol Genet}, volume = {25}, year = {2016}, month = {2016 06 01}, pages = {2331-2341}, abstract = {

Congenital heart defects involving left-sided lesions (LSLs) are relatively common birth defects with substantial morbidity and mortality. Previous studies have suggested a high heritability with a complex genetic architecture, such that only a few LSL loci have been identified. We performed a genome-wide case-control association study to address the role of common variants using a discovery cohort of 778 cases and 2756 controls. We identified a genome-wide significant association mapping to a 200 kb region on chromosome 20q11 [P= 1.72 {\texttimes} 10 for rs3746446; imputed Single Nucleotide Polymorphism (SNP) rs6088703 P= 3.01 {\texttimes} 10, odds ratio (OR)= 1.6 for both]. This result was supported by transmission disequilibrium analyses using a subset of 541 case families (lowest P in region= 4.51 {\texttimes} 10, OR= 1.5). Replication in a cohort of 367 LSL cases and 5159 controls showed nominal association (P= 0.03 for rs3746446) resulting in P= 9.49 {\texttimes} 10 for rs3746446 upon meta-analysis of the combined cohorts. In addition, a group of seven SNPs on chromosome 1q21.3 met threshold for suggestive association (lowest P= 9.35 {\texttimes} 10 for rs12045807). Both regions include genes involved in cardiac development-MYH7B/miR499A on chromosome 20 and CTSK, CTSS and ARNT on chromosome 1. Genome-wide heritability analysis using case-control genotyped SNPs suggested that the mean heritability of LSLs attributable to common variants is moderately high ([Formula: see text] range= 0.26-0.34) and consistent with previous assertions. These results provide evidence for the role of common variation in LSLs, proffer new genes as potential biological candidates, and give further insight to the complex genetic architecture of congenital heart disease.

}, keywords = {Chromosome Mapping, Chromosomes, Human, Pair 20, Cohort Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Heart Defects, Congenital, Heart Ventricles, Humans, Male, Polymorphism, Single Nucleotide}, issn = {1460-2083}, doi = {10.1093/hmg/ddw071}, author = {Hanchard, Neil A and Swaminathan, Shanker and Bucasas, Kristine and Furthner, Dieter and Fernbach, Susan and Azamian, Mahshid S and Wang, Xueqing and Lewin, Mark and Towbin, Jeffrey A and D{\textquoteright}Alessandro, Lisa C A and Morris, Shaine A and Dreyer, William and Denfield, Susan and Ayres, Nancy A and Franklin, Wayne J and Justino, Henri and Lantin-Hermoso, M Regina and Ocampo, Elena C and Santos, Alexia B and Parekh, Dhaval and Moodie, Douglas and Jeewa, Aamir and Lawrence, Emily and Allen, Hugh D and Penny, Daniel J and Fraser, Charles D and Lupski, James R and Popoola, Mojisola and Wadhwa, Lalita and Brook, J David and Bu{\textquoteright}Lock, Frances A and Bhattacharya, Shoumo and Lalani, Seema R and Zender, Gloria A and Fitzgerald-Butt, Sara M and Bowman, Jessica and Corsmeier, Don and White, Peter and Lecerf, Kelsey and Zapata, Gladys and Hernandez, Patricia and Goodship, Judith A and Garg, Vidu and Keavney, Bernard D and Leal, Suzanne M and Cordell, Heather J and Belmont, John W and McBride, Kim L} } @article {145, title = {Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits.}, journal = {Bioinformatics}, volume = {31}, year = {2015}, month = {2015 Nov 15}, pages = {3706-8}, abstract = {

MOTIVATION: There is great interest in analyzing next generation sequence data that has been generated for pedigrees. However, unlike for population-based data there are only a limited number of rare variant methods to analyze pedigree data. One limitation is the ability to evaluate type I and II errors for family-based methods, due to lack of software that can simulate realistic sequence data for pedigrees.

SUMMARY: We developed RarePedSim (Rare-variant Pedigree-based Simulator), a program to simulate region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members{\textquoteright} qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard Linkage file (.ped) and Variant Call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods.

AVAILABILITY AND IMPLEMENTATION: bioinformatics.org/simped/rare

CONTACT: sleal@bcm.edu.

}, keywords = {Base Sequence, Chromosome Segregation, Humans, Pedigree, Quantitative Trait, Heritable, Sequence Analysis, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btv412}, author = {Li, Biao and Wang, Gao T and Leal, Suzanne M} } @article {149, title = {The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities.}, journal = {Am J Hum Genet}, volume = {97}, year = {2015}, month = {2015 Aug 06}, pages = {199-215}, abstract = {

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying \~{}50\% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.

}, keywords = {Genetic Diseases, Inborn, Genetics, Medical, Humans, Phenotype, Proteins}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2015.06.009}, author = {Chong, Jessica X and Buckingham, Kati J and Jhangiani, Shalini N and Boehm, Corinne and Sobreira, Nara and Smith, Joshua D and Harrell, Tanya M and McMillin, Margaret J and Wiszniewski, Wojciech and Gambin, Tomasz and Coban Akdemir, Zeynep H and Doheny, Kimberly and Scott, Alan F and Avramopoulos, Dimitri and Chakravarti, Aravinda and Hoover-Fong, Julie and Mathews, Debra and Witmer, P Dane and Ling, Hua and Hetrick, Kurt and Watkins, Lee and Patterson, Karynne E and Reinier, Frederic and Blue, Elizabeth and Muzny, Donna and Kircher, Martin and Bilguvar, Kaya and L{\'o}pez-Gir{\'a}ldez, Francesc and Sutton, V Reid and Tabor, Holly K and Leal, Suzanne M and G{\"u}nel, Murat and Mane, Shrikant and Gibbs, Richard A and Boerwinkle, Eric and Hamosh, Ada and Shendure, Jay and Lupski, James R and Lifton, Richard P and Valle, David and Nickerson, Deborah A and Bamshad, Michael J} } @article {182, title = {Genetic linkage analysis in the age of whole-genome sequencing.}, journal = {Nat Rev Genet}, volume = {16}, year = {2015}, month = {2015 May}, pages = {275-84}, abstract = {

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.

}, keywords = {Algorithms, Female, Genetic Linkage, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Humans, Lod Score, Male, Pedigree, Penetrance, Phenotype, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {1471-0064}, doi = {10.1038/nrg3908}, author = {Ott, Jurg and Wang, Jing and Leal, Suzanne M} } @article {332, title = {GENOME-WIDE ASSOCIATION MAPPING AND RARE ALLELES: FROM POPULATION GENOMICS TO PERSONALIZED MEDICINE - Session Introduction.}, journal = {Pac Symp Biocomput}, year = {2011}, month = {2011}, pages = {74-5}, abstract = {

Genome-wide associations studies (GWAS) have been very successful in identifying common genetic variation associated to numerous complex diseases [1]. However, most of the identified common genetic variants appear to confer modest risk and few causal alleles have been identified [2]. Furthermore, these associations account for a small portion of the total heritability of inherited disease variation [1]. This has led to the reexamination of the contribution of environment, gene-gene and gene-environment interactions, and rare genetic variants in complex diseases [1, 3, 4]. There is strong evidence that rare variants play an important role in complex disease etiology and may have larger genetic effects than common variants [2]. Currently, much of what we know regarding the contribution of rare genetic variants to disease risk is based on a limited number of phenotypes and candidate genes. However, rapid advancement of second generation sequencing technologies will invariably lead to widespread association studies comparing whole exome and eventually whole genome sequencing of cases and controls. A tremendous challenge for enabling these "next generation" medical genomic studies is developing statistical approaches for correlating rare genetic variants with disease outcome. The analysis of rare variants is challenging since methods used for common variants are woefully underpowered. Therefore, methods that can deal with genetic heterogeneity at the trait-associated locus have been developed to analyze rare variants. These methods instead analyzing individual variants analyze variants within a region/gene as a group and usually rely on collapsing. They can be applied to both in cases vs. controls and quantitative trait studies are needed. The paper of Bansal et al. in this volume describes the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. The authors considered the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values. Knowledge of haplotypes can increase the power of GWAS studies and also highlight associations that are impossible to detect without haplotype phase (e.g. loss of heterozygosity). Even more complicated phase-dependent interactions of variants in linkage equilibrium have also been suggested as possible causes of missing heritability. In their work, Hallsorsson et al. formulate algorithmic strategies for haplotype phasing by multi-assembly of shared haplotypes from next-generation sequencing data. These methods would allow testing haplotypes harboring rare variants for association and potentially increase their explanatory power. Since single SNP tests are often underpowered in rare variant association analysis, Zeggini and Asimit propose a locus-based method that has high power in the presence of rare variants and that incorporate base quality scores available for sequencing data. Their results suggest that this multi-marker approach may be best suited for smaller regions, or after some filtering to reduce the number of SNPs that are jointly tested to reduce loss of power due to multiple-testing adjustments. Finally, the paper of Zhou et al., presents a penalized regression framework for association testing on sequence data, in the presence of both common and rare variants. This method also introduces the use of weights to incorporate available biological information on the variants. Although these tactics improve both false positive and false negative rates, they represent an incremental development and there is still significant room for improvement. With the development of sequencing technologies and methods to detect complex trait rare variant associations many new and exciting discovery are imminent. The analysis of rare variants is still in its infancy and the next few years promises to produce many new methods to meet the special demands of analyzing this type of data. Note from Publisher: This article contains the abstract and references.

}, issn = {2335-6936}, author = {DE LA Vega, Francisco M and Bustamante, Carlos D and Leal, Suzanne M} }