Biology subjects

McMahon, F. J.

Publications and source records attributed to McMahon, F. J..

3 recordsLinked to original sources

bioRxiv2017

Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree

Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this may be due to increased risk for comorbid diseases. Recent evidence suggests that pleiotropy, either in the form of a single risk-allele or the combination of multiple loci genome-wide, may underlie medical comorbidity between traits and diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole genome sequence (WGS) data using a population specific reference panel, for an extended multigenerational Old Order Amish pedigree (400 family members) segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores to establish each individual's risk for several complex diseases. To explore the contribution of disease genes to BD we performed gene-based and variant-based association tests for BD, and found that Mendelian disease genes are enriched in the top results from both tests (OR=20.3, p=1x10-3; OR=2.2, p=1x10-2). We next identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using polygenic risk scores, we demonstrated that BD individuals from this pedigree were enriched for the same common risk-alleles for BD as in the general population ({beta}=0.416, p=6x10-4). Furthermore, in the extended Amish family we find evidence for a common genetic etiology between BD and clinical autoimmune thyroid disease (p=1x10-4), diabetes (p=1x10-3), and lipid traits such as triglyceride levels (p=3x10-4). We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits. Identifying such patterns may enable the subtyping of complex diseases and facilitate our understanding of the genetic mechanisms underlying phenotypic heterogeneity.

genetics↗

bioRxiv2017

Sodium valproate rescues expression of TRANK1 in iPSC-derived neural cells that carry a genetic variant associated with serious mental illness

Biological characterization of genetic variants identified in genome-wide association studies (GWAS) remains a substantial challenge. Here we used human induced pluripotent stem cells (hiPSC) and their neural derivatives to characterize common variants on chromosome 3p22 that have been associated by GWAS with major mental illnesses. HiPSC-derived neural progenitor cells carrying the risk allele of the single nucleotide polymorphism (SNP), rs9834970, displayed lower baseline TRANK1 expression that was rescued by chronic treatment with therapeutic dosages of valproic acid (VPA). Although rs9834970 has no known function, we demonstrated that a nearby SNP, rs90832, strongly affects binding by the transcription factor, CTCF, and that the high-affinity allele usually occurs on haplotypes carrying the rs9834970 risk allele. Decreased expression of TRANK1 perturbed expression of many genes involved in neural development and differentiation. These findings have important implications for the pathophysiology of major mental illnesses and the development of novel therapeutics.\n\nHighlightsO_LIhiPSC-derived neural cells carrying a mental illness risk allele showed lower expression of TRANK1\nC_LIO_LIValproic acid rescued TRANK1expression in cells carrying the risk allele\nC_LIO_LIRisk haplotypes usually carry an allele that increased CTCF binding\nC_LIO_LIReduced expression of TRANK1 perturbed genes involved in neural development and differentiation\nC_LI\n\nIn BriefUsing neural derivatives of human induced pluripotent stem cells, Jiang et al. demonstrate that genetic variants associated with mental illness alter transcription factor binding and decrease expression of a nearby gene, an effect which is rescued by valproic acid.

genetics↗

bioRxiv2017

Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

genomics↗