Biology subjects

Suna Onengut-Gumuscu

Publications and source records attributed to Suna Onengut-Gumuscu.

3 recordsLinked to original sources

bioRxiv2015

Using genotype data to distinguish pleiotropy from heterogeneity: deciphering coheritability in autoimmune and neuropsychiatric diseases

Shared genetic architecture between phenotypes may be driven by a common genetic basis (pleiotropy) or a subset of genetically similar individuals (heterogeneity). We developed BUHMBOX, a well-powered statistical method to distinguish pleiotropy from heterogeneity using genotype data. We observed a shared genetic basis between 11 of 17 tested autoimmune diseases and type I diabetes (T1D, p<10-12) and 11 of 17 tested autoimmune diseases and rheumatoid arthritis (RA, p<10-7). This sharing could not be explained by heterogeneity (corrected pBUHMBOX>0.2 using 6,670 T1D cases and 7,279 RA cases), suggesting that shared genetic features in autoimmunity are due to pleiotropy. We observed a shared genetic basis between seronegative and seropostive RA (p<10-22), explained by heterogeneity (pBUHMBOX=0.008 in 2,406 seronegative RA cases). Consistent with previous observations, we observed genetic sharing between major depressive disorder (MDD) and schizophrenia (p<10-9). This sharing is not explained by heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases).

Genomics↗

bioRxiv2015

Genetic basis underlying connection between hyperglycemia and dyslipidemia in Apoe-deficient mice

Individuals with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains to be determined whether there are genetic connections between the 2 disorders. A female F2 cohort, generated from BALB/cJ (BALB) and SM/J (SM) Apoe-deficient (Apoe-/-) strains, was fed a Western diet for 12 weeks. Fasting plasma glucose and lipid levels were measured before and after Western diet feeding. 144 genetic markers across the entire genome were used for analysis. One significant QTL on chromosome 9, named Bglu17 [26.4 cM, logarithm of odds ratio (LOD): 5.4], and 3 suggestive QTLs were identified for fasting glucose levels. The suggestive QTL near the proximal end of chromosome 9 (2.4 cM, LOD: 3.12) was detected when mice were fed chow or Western diet and named Bglu16. Bglu17 coincided with a significant QTL for HDL and a suggestive QTL for non-HDL cholesterol levels. Plasma glucose levels were inversely correlated with HDL but positively correlated with non-HDL cholesterol levels in F2 mice fed either diet. A significant correlation between fasting glucose and triglyceride levels was observed on the Western but not chow diet. Haplotype analysis revealed that \"lipid genes\" Sik3 and Apoc3 were probable candidates for Bglu17. We have identified multiple QTLs for fasting glucose and lipid levels. The colocalization of QTLs for both phenotypes and the sharing of potential causal genes suggest that dyslipidemia and type 2 diabetes are genetically connected.\n\nArticle SummaryPatients with dyslipidemia often develop type 2 diabetes, and diabetic patients often have dyslipidemia. It remains unknown whether there are genetic connections between the 2 disorders. Using a female F2 cohort derived from BALB/cJ and SM/J Apoe-deficient mice, we identified one significant QTL on chromosome 9, named Bglu17, and 3 suggestive QTLs were identified for fasting glucose levels. Bglu17 coincided with a significant QTL for HDL and a suggestive QTL for non-HDL levels. Plasma glucose levels were significantly correlated with HDL and non-HDL levels in F2 mice. Haplotype analysis revealed Sik3 and Apoc3 were probable candidates for both QTLs.

Genetics↗

bioRxiv2015

Dissection of a complex disease susceptibility region using a Bayesian stochastic search approach to fine mapping

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 [~=] 0.3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

Genetics↗