Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network

1. Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network Ivan Iossifov, Tian Zheng, Miron Baron, T. Conrad Gilliam and Andrey Rzhetsky Genome Research (2008) 18: 1150-1162

2. Common neurodevelopmental disorders: Autism, Bipolar disorder and Schizophrenia – hereditary disorders – hard to explain by single environmental or genetic cause • Difficulties in detection of heritable patterns of disease susceptibility: • Dimensionality – the exponentially expanding search space required to explore all combinations of m genes or m genetic loci. • 2) How variations within the genes jointly affect the susceptibility to disease remains unknown even after identification of a set of genes related to the disease – genetic causes of a given disorder can be completely or partially different for different affected families.

3. Proposal of a probabilistic model: • Extension of multipoint genetic linkage model. • Multipoint linkage analysis is a mathematical model that detects correlations between disease phenotypes within human families and states of multiple polymorphic genetic markers that are experimentally probed in every family member. • Two additional major assumptions here: • Genetic variation within multiple genes (rather than a single gene that is common to all affected individuals) can influence the disease phenotype and that potential disease-predisposing genes are grouped into a compact gene cluster within a molecular network. • 2) Heterogeneity of genetic variations underlie the same phenotype in different affected families.

4. Molecular Network: GeneWays 6.0 database (Rzhetsky et. al, 2004) – Generated by the large-scale text-mining of nearly 250,000 full-text articles from 78 leading biochemical journals. – Removed all non-human specific interactions – Used only direct interactions between genes or their products (a total of 47 distinct types, such as binding, phosphorylation, and methylation) as opposed to regulatory interactions between pairs of genes that are indirect (activation or inhibition). – Used only those molecular interactions for which all genes have NCBI geneIDs and only those genes were used for which physical coordinates are available in UCSC database.

5. Data: • Molecular Network: Two different networks depending on whether the X-chromosome is included in the analysis. • Resulting molecular-interaction network comprises of • Without X-chromosome: 4,374 genes and 13,612 interactions including 1,120 self-interactions. It contains one large and several small connected components. Used only large connected componenet which includes 3,829 genes and 12,446 interactions (of the latter 985 are self-interactions, excluded from the analysis). • 2) With X-chromosome: 4,019 genes and 13,256 interactions, including 1,025 self-interactions. • Linkage Datasets: Genotypic and phenotypic data : • Autism: 336 families, Bipolar: 414 families & Schizophrenia: 87 families. • All are multiplex families – a family is ‘multiplex’ if it includes more than one affected person with respect to a specific disease.

6. A hypothetical family. Each pedigree (family) described in our data is provided with a kinship structure and phenotypic information. Circles and squares represent female and male individuals, respectively. By convention, the filled shapes represent affected (sick) individuals; the open shapes represent unaffected (healthy) ones.

7. Gene clusters: Assumption: A set of genes that can contribute to the risk of contracting a given disease phenotype when the sequence of at least one of them is critically modified. Under this assumption, gene cluster, C, is defined as a set of genes with members that are grouped by their ability to harbor genetic polymorphisms that predispose the bearer to a specific disease. For every gene within a gene cluster C, c is the size of the cluster Cluster probability (pi) as the share of guilt attributable to variations at the locus of the i-th gene for the disease phenotype in a large group of randomly selected disease-affected families. Explicit assumption: gene clusters are set of genes that are joined through direct molecular interactions into a “connected subgraph”. Genes known to participate in the same polygenic disorder tend to be close to each other in a molecular network.

8. A gene with zero cluster probability: It may not be directly involved in the disease etiology even though it is a member of the gene cluster that has the highest likelihood value. These zero contribution genes can serve as connectors for genes that are strongly linked to the disease. Genes with higher cluster probabilities are more likely to bear disease-predisposing polymorphisms at the corresponding loci. • For formulation of mathematical model two more assumptions: • Only those genes that are within a gene cluster, C, harbor a disease-predisposing variation. • 2) For every family under analysis, exactly one gene from cluster C is a disease-predisposing gene. That is, the phenotype status of every individual is determined by the state (allele) of the family-specific gene in the individual’s genome.

9. This disease model is a mixture of probabilistic models, each of which is determined by one disease-predisposing gene in C, with the mixing coefficients being the cluster probabilities. The set of families affected by the same disease under this model is a mixture of families that are predisposed to the disease via mutations at different genes that belong to C. Y = union of genotypic and phenotypic data Yf = portion of Y associated with the f-th family (pedigree) = all of the linkage related parameters, including genetic penetrance, background frequencies of marker alleles, and genetic distances between the markers.

10. Assumptions: • Every gene in C has only one healthy and one disease-predisposing allele and that the expected frequencies of these alleles are the same for all genes in the cluster. • 2) The genetic-penetrance parameters are the same for every gene in C. • Under this model, given the state of the chosen gene, the disease-phenotype state of the individual is independent of the rest of the individual’s genome and of the genotypes and phenotypes of his/her family members. • This assumption of independence leads to a “gene mixture generative model” of the data: • The i-th disease-predisposing gene is assigned to a family by a random draw from C with probability pi. • Once a gene is assigned to a family, the disease-related phenotype variation in this family is probabilistically dependent on the state of the i-th gene and is independent of the states of all other genes in C and in the rest of the genome.

14. Gene-specific significance tests: Optimum gene cluster of size c : which achieves maximum cluster LOD score, gene cluster probabilities are estimated using the maximum-likelihood method for each cluster. Identification of optimum c-gene cluster is done by simulated annealing method together with bootstrap aggregation technique. For a given gene, the statistic measures the relative strength of this gene’s linkage signal compared to all genes under study. Evaluation of the significance of each gene-specific test statistic using simulated data was done under the null hypothesis that the disease phenotype is completely unlinked to any part of the genome, preserving the real pedigree topologies encoded in data sets and the real frequencies of genetic markers.

16. One important point: This method analyzes “groups” of functionally related genes rather than individual genes and, as a result, the linkage signal is magnified through summation over multiple genes within the same gene cluster, while each individual gene may exhibit only a weak evidence of linkage. Example: TJP1 or UBE3A – on its own yields a weak empirical P-value because of the genetic heterogeneity of the families: In addition to families with positive LOD scores there are families that show strong negative signals for both genes. It is only when we consider gene clusters that the linkage signal becomes strong and obvious. Matching of LOD scores. 14 top-ranking gene clusters in all three disorders.

18. Closer look at candidate genes: 1) Some genes are regulators of cell cycle and cell death (EDAR, BCL2L11, NEK6, SFRP1, MAPK7). 2) Some genes form intercellular contacts (TJP1, LGALS4, MMRN1, IBSP, NPHP1) 3) A few genes are brain-specific growth and signal-transduction receptors and small molecule transporters (RAPSN, APBA2, UBE3A, ALK, KCNB1). 4) A few others are related to immune response (CCL15, CSF2, CD55, IL10). In a recent study (Bustamante et al, 2005) of positive selection patterns in the human genome, it has been discovered that many genes involved in cell-cycle and cell-death regulation appear to have undergone recent positive selection in the lineage leading to hominid primates. Thus, it is plausible that phenotypic effects that we classify as neurological disorders are artifacts of a mosaic of small genetic changes that occurred during evolutionary optimization of multiple physiological systems involved in the unusually prolonged individual development of a human brain.

19. In this study, top disease-predisposing candidate loci are enriched with these cell death and cell cycle related genes as computed by a test of randomized gene sampling using gene ontology categories. However, this significance of enrichment is likely to be a mere reflection of the overall abundance of cell cycle and cell death related genes in our text-mined network. (Possible weak point of this method - biasness). This analysis does not identify as significant any of the previously suggested 16 genes most likely related to schizophrenia susceptibility – consistent with the hypothesis of genetic heterogeneity, majority of the 16 genes has no or very weak linkage support in 94 families, while some genes provide good support – but, unfortunately those genes have few known direct physical interactions and are either disconnected or poorly connected in molecular-interaction network. (another weak point of the method – incompleteness of the network).