A genome-wide association study of congenital cardiovascular left-sided lesions shows association with a locus on chromosome 20. - Hanchard, Neil A; Swaminathan, Shanker; Bucasas, Kristine; Furthner, Dieter; Fernbach, Susan; Azamian, Mahshid S; Wang, Xueqing; Lewin, Mark; Towbin, Jeffrey A; D'Alessandro, Lisa C A; Morris, Shaine A; Dreyer, William; et al. (Hum Mol Genet, 2016)
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 × 10 for rs3746446; imputed Single Nucleotide Polymorphism (SNP) rs6088703 P= 3.01 × 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 × 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 × 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 × 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

Genetic linkage analysis in the age of whole-genome sequencing. - Ott, Jurg; Wang, Jing; Leal, Suzanne M (Nat Rev Genet, 2015)
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

Mutations in TBC1D24, a gene associated with epilepsy, also cause nonsyndromic deafness DFNB86. - Rehman, Atteeq U; Santos-Cortez, Regie Lyn P; Morell, Robert J; Drummond, Meghan C; Ito, Taku; Lee, Kwanghyuk; Khan, Asma A; Basra, Muhammad Asim R; Wasif, Naveed; Ayub, Muhammad; Ali, Rana A; Raza, Syed I; et al. (Am J Hum Genet, 2014)
Inherited deafness is clinically and genetically heterogeneous. We recently mapped DFNB86, a locus associated with nonsyndromic deafness, to chromosome 16p. In this study, whole-exome sequencing was performed with genomic DNA from affected individuals from three large consanguineous families in which markers linked to DFNB86 segregate with profound deafness. Analyses of these data revealed homozygous mutation c.208G>T (p.Asp70Tyr) or c.878G>C (p.Arg293Pro) in TBC1D24 as the underlying cause of deafness in the three families. Sanger sequence analysis of TBC1D24 in an additional large family in which deafness segregates with DFNB86 identified the c.208G>T (p.Asp70Tyr) substitution. These mutations affect TBC1D24 amino acid residues that are conserved in orthologs ranging from fruit fly to human. Neither variant was observed in databases of single-nucleotide variants or in 634 chromosomes from ethnically matched control subjects. TBC1D24 in the mouse inner ear was immunolocalized predominantly to spiral ganglion neurons, indicating that DFNB86 deafness might be an auditory neuropathy spectrum disorder. Previously, six recessive mutations in TBC1D24 were reported to cause seizures (hearing loss was not reported) ranging in severity from epilepsy with otherwise normal development to epileptic encephalopathy resulting in childhood death. Two of our four families in which deafness segregates with mutant alleles of TBC1D24 were available for neurological examination. Cosegregation of epilepsy and deafness was not observed in these two families. Although the causal relationship between genotype and phenotype is not presently understood, our findings, combined with published data, indicate that recessive alleles of TBC1D24 can cause either epilepsy or nonsyndromic deafness.
Keywords: Alleles; Amino Acid Sequence; Carrier Proteins; Chromosomes, Human, Pair 16; Consanguinity; Deafness; Epilepsy; Exome; Exons; Female; Genes, Recessive; Genetic Loci; Genome-Wide Association Study; Heterozygote; Homozygote; Humans; Male; Molecular Sequence Data; Mutation; Pakistan; Pedigree; Phenotype; Polymorphism, Single Nucleotide; RNA, Messenger; Sequence Analysis, DNA

Effect of catechol-o-methyltransferase-gene (COMT) variants on experimental and acute postoperative pain in 1,000 women undergoing surgery for breast cancer. - Kambur, Oleg; Kaunisto, Mari A; Tikkanen, Emmi; Leal, Suzanne M; Ripatti, Samuli; Kalso, Eija A (Anesthesiology, 2013)
BACKGROUND: Catechol-O-methyltransferase (COMT) metabolizes catecholamines in different tissues. Polymorphisms in COMT gene can attenuate COMT activity and increase sensitivity to pain. Human studies exploring the effect of COMT polymorphisms on pain sensitivity have mostly included small, heterogeneous samples and have ignored several important single nucleotide polymorphisms (SNPs). This study examines the effect of COMT polymorphisms on experimental and postoperative pain phenotypes in a large ethnically homogeneous female patient cohort. METHODS: Intensity of cold (+2-4°C) and heat (+48°C) pain and tolerance to cold pain were assessed in 1,000 patients scheduled for breast cancer surgery. Acute postoperative pain and oxycodone requirements were recorded. Twenty-two COMT SNPs were genotyped and their association with six pain phenotypes analyzed with linear regression. RESULTS: There was no association between any of the tested pain phenotypes and SNP rs4680. The strongest association signals were seen between rs165774 and heat pain intensity as well as rs887200 and cold pain intensity. In both cases, minor allele carriers reported less pain. Neither of these results remained significant after strict multiple testing corrections. When analyzed further, the effect of rs887200 was, however, shown to be significant and consistent throughout the cold pressure test. No evidence of association between the SNPs and postoperative oxycodone consumption was found. CONCLUSIONS: SNPs rs887200 and rs165774 located in the untranslated regions of the gene had the strongest effects on pain sensitivity. Their effect on pain is described here for the first time. These results should be confirmed in further studies and the potential functional mechanisms of the variants studied.
Keywords: Acute Pain; Adult; Aged; Analgesics, Opioid; Anesthesia; Breast Neoplasms; Catechol O-Methyltransferase; Cold Temperature; Databases, Genetic; Female; Follow-Up Studies; Genetic Variation; Genome-Wide Association Study; Genotype; Haplotypes; Hot Temperature; Humans; Middle Aged; Oxycodone; Pain Measurement; Pain, Postoperative; Polymorphism, Single Nucleotide

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. - Mechanic, Leah E; Chen, Huann-Sheng; Amos, Christopher I; Chatterjee, Nilanjan; Cox, Nancy J; Divi, Rao L; Fan, Ruzong; Harris, Emily L; Jacobs, Kevin; Kraft, Peter; Leal, Suzanne M; McAllister, Kimberly; et al. (Genet Epidemiol, 2012)
Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
Keywords: Data Mining; Gene-Environment Interaction; Genetic Variation; Genome-Wide Association Study; Humans; Molecular Epidemiology; National Institutes of Health (U.S.); Neoplasms; Phenotype; United States

A unified method for detecting secondary trait associations with rare variants: application to sequence data. - Liu, Dajiang J; Leal, Suzanne M (PLoS Genet, 2012)
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