@article {155, title = {Joubert syndrome: a model for untangling recessive disorders with extreme genetic heterogeneity.}, journal = {J Med Genet}, volume = {52}, year = {2015}, month = {2015 Aug}, pages = {514-22}, abstract = {

BACKGROUND: Joubert syndrome (JS) is a recessive neurodevelopmental disorder characterised by hypotonia, ataxia, cognitive impairment, abnormal eye movements, respiratory control disturbances and a distinctive mid-hindbrain malformation. JS demonstrates substantial phenotypic variability and genetic heterogeneity. This study provides a comprehensive view of the current genetic basis, phenotypic range and gene-phenotype associations in JS.

METHODS: We sequenced 27 JS-associated genes in 440 affected individuals (375 families) from a cohort of 532 individuals (440 families) with JS, using molecular inversion probe-based targeted capture and next-generation sequencing. Variant pathogenicity was defined using the Combined Annotation Dependent Depletion algorithm with an optimised score cut-off.

RESULTS: We identified presumed causal variants in 62\% of pedigrees, including the first B9D2 mutations associated with JS. 253 different mutations in 23 genes highlight the extreme genetic heterogeneity of JS. Phenotypic analysis revealed that only 34\% of individuals have a {\textquoteright}pure JS{\textquoteright} phenotype. Retinal disease is present in 30\% of individuals, renal disease in 25\%, coloboma in 17\%, polydactyly in 15\%, liver fibrosis in 14\% and encephalocele in 8\%. Loss of CEP290 function is associated with retinal dystrophy, while loss of TMEM67 function is associated with liver fibrosis and coloboma, but we observe no clear-cut distinction between JS subtypes.

CONCLUSIONS: This work illustrates how combining advanced sequencing techniques with phenotypic data addresses extreme genetic heterogeneity to provide diagnostic and carrier testing, guide medical monitoring for progressive complications, facilitate interpretation of genome-wide sequencing results in individuals with a variety of phenotypes and enable gene-specific treatments in the future.

}, keywords = {Abnormalities, Multiple, Cerebellum, Cohort Studies, DNA Mutational Analysis, Eye Abnormalities, Genetic Association Studies, Genetic Heterogeneity, Humans, Kidney Diseases, Cystic, Models, Theoretical, Pedigree, Retina, Sequence Analysis, DNA}, issn = {1468-6244}, doi = {10.1136/jmedgenet-2015-103087}, author = {Bachmann-Gagescu, R and Dempsey, J C and Phelps, I G and O{\textquoteright}Roak, B J and Knutzen, D M and Rue, T C and Ishak, G E and Isabella, C R and Gorden, N and Adkins, J and Boyle, E A and de Lacy, N and O{\textquoteright}Day, D and Alswaid, A and Ramadevi A, Radha and Lingappa, L and Louren{\c c}o, C and Martorell, L and Garcia-Cazorla, {\`A} and Ozy{\"u}rek, H and Halilo{\u g}lu, G and Tuysuz, B and Top{\c c}u, M and Chance, P and Parisi, M A and Glass, I A and Shendure, J and Doherty, D} } @article {222, title = {Parent of origin, mosaicism, and recurrence risk: probabilistic modeling explains the broken symmetry of transmission genetics.}, journal = {Am J Hum Genet}, volume = {95}, year = {2014}, month = {2014 Oct 02}, pages = {345-59}, abstract = {

Most new mutations are observed to arise in fathers, and increasing paternal age positively correlates with the risk of new variants. Interestingly, new mutations in X-linked recessive disease show elevated familial recurrence rates. In male offspring, these mutations must be inherited from mothers. We previously developed a simulation model to consider parental mosaicism as a source of transmitted mutations. In this paper, we extend and formalize the model to provide analytical results and flexible formulas. The results implicate parent of origin and parental mosaicism as central variables in recurrence risk. Consistent with empirical data, our model predicts that more transmitted mutations arise in fathers and that this tendency increases as fathers age. Notably, the lack of expansion later in the male germline determines relatively lower variance in the proportion of mutants, which decreases with paternal age. Subsequently, observation of a transmitted mutation has less impact on the expected risk for future offspring. Conversely, for the female germline, which arrests after clonal expansion in early development, variance in the mutant proportion is higher, and observation of a transmitted mutation dramatically increases the expected risk of recurrence in another pregnancy. Parental somatic mosaicism considerably elevates risk for both parents. These findings have important implications for genetic counseling and for understanding patterns of recurrence in transmission genetics. We provide a convenient online tool and source code implementing our analytical results. These tools permit varying the underlying parameters that influence recurrence risk and could be useful for analyzing risk in diverse family structures.

}, keywords = {Adult, Aged, Aged, 80 and over, Chromosome Aberrations, DNA Copy Number Variations, Fathers, Female, Gametogenesis, Genetic Diseases, Inborn, Genomics, Germ Cells, Germ-Line Mutation, Humans, Inheritance Patterns, Male, Middle Aged, Models, Theoretical, Mosaicism, Mothers, Pregnancy, Recurrence, Risk Factors, Young Adult}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.08.010}, author = {Campbell, Ian M and Stewart, Jonathan R and James, Regis A and Lupski, James R and Stankiewicz, Pawe{\l} and Olofsson, Peter and Shaw, Chad A} } @article {320, title = {Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis.}, journal = {Genet Epidemiol}, volume = {37}, year = {2013}, month = {2013 Feb}, pages = {136-41}, abstract = {

Many statistical analyses of genetic data rely on the assumption of independence among samples. Consequently, relatedness is either modeled in the analysis or samples are removed to "clean" the data of any pairwise relatedness above a tolerated threshold. Current methods do not maximize the number of unrelated individuals retained for further analysis, and this is a needless loss of resources. We report a novel application of graph theory that identifies the maximum set of unrelated samples in any dataset given a user-defined threshold of relatedness as well as all networks of related samples. We have implemented this method into an open source program called Pedigree Reconstruction and Identification of a Maximum Unrelated Set, PRIMUS. We show that PRIMUS outperforms the three existing methods, allowing researchers to retain up to 50\% more unrelated samples. A unique strength of PRIMUS is its ability to weight the maximum clique selection using additional criteria (e.g. affected status and data missingness). PRIMUS is a permanent solution to identifying the maximum number of unrelated samples for a genetic analysis.

}, keywords = {Algorithms, Genome-Wide Association Study, HapMap Project, Humans, Models, Theoretical}, issn = {1098-2272}, doi = {10.1002/gepi.21684}, author = {Staples, Jeffrey and Nickerson, Deborah A and Below, Jennifer E} }