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Genetic Studies of PTSD and Substance Use Disorders
Published in Anka A. Vujanovic, Sudie E. Back, Posttraumatic Stress and Substance Use Disorders, 2019
Christina M. Sheerin, Leslie A. Brick, Nicole R. Nugent, Ana B. Amstadter
Referring back to Figure 15.1, genome-wide complex trait analysis (GCTA) and LD score regression (methods for determining SNP-based heritability in unrelated individuals) could be used to address heritability of environmental exposures (i.e., A1 and A2) and heritability of the disorders (i.e., C1 and C2). Bivariate GCTA and LD score regression can address both the conditionality within one disorder (i.e., B1 and B2, shared heritability between environmental exposure and disorder), conditionality cross-disorder (i.e., E1 and E2), and overlap in risk for exposures (i.e., D1) and disorders (i.e., D2). For example, Stein and colleagues (2016) used the LD score regression technique to examine genetic correlation between PTSD and psychiatric and immune-related disorders. While they did not find evidence of genetic overlap between PTSD and psychiatric phenotypes of schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, major depressive disorder, and autism spectrum disorder, they did find evidence for significant shared genetic risk between PTSD and rheumatoid arthritis and psoriasis. Aggregate allelic risk for one exposure/phenotype can be modeled (i.e., polygenic risk score) and examined in reference to other phenotypes, either from environmental exposure to disorder (e.g., B1 and B2; E1 and E2), between disorders (i.e., D2), or between environmental exposures (i.e., D1). Indeed, Nievergelt and colleagues (2015) used polygenic risk scores to conduct a cross-disorders analysis from existing GWAS of bipolar disorder, major depressive disorder, and schizophrenia and found evidence for shared genetic overlap with bipolar disorder.
Myopia: is the nature‐nurture debate finally over?
Published in Clinical and Experimental Optometry, 2019
There are many possible explanations of missing heritability.2009 An obvious one is that for multifactorial conditions, involving many small genetic (and perhaps environmental) effects, sample size limitations may make it difficult to obtain statistically significant genome‐wide associations. Statistical techniques have therefore been developed for summing all the genetic associations detected in GWAS, whether they reach genome‐wide significance or not – the genome‐wide complex trait analysis or single‐nucleotide polymorphism (SNP) heritability approach. It is argued that this may give a better estimate of the total amount of variation that can be explained genetically than relying only on statistically significant genome‐wide associations.2011 In some cases, such as height,2010 this technique has helped to account for a substantial proportion of the missing heritability; in others, it has added little, if any, explanatory power.2013
Clinical and genetic predictors of diabetes drug’s response
Published in Drug Metabolism Reviews, 2019
Adriana Fodor, Angela Cozma, Ramona Suharoschi, Adela Sitar-Taut, Gabriela Roman
The glycemic response to metformin is probably determined by the interaction of genetic and environmental factors. Clinical parameters such as BMI, age, diabetes duration, serum creatinine, baseline HbA1c only account for part of the variation. Genome-wide complex trait analysis of more than 2000 T2D patients, revealed that genetic variation contributes up to 34% of differential glycemic response to metformin (Zhou K et al. 2014). So far, only a few polymorphisms in genes affecting metformin response have been identified. Enhanced GWAS analyses, with adequate statistical power, are expected to find more genetic variants that would enable better metformin response.