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Genetics
Published in Cathy Laver-Bradbury, Margaret J.J. Thompson, Christopher Gale, Christine M. Hooper, Child and Adolescent Mental Health, 2021
Bipolar disorder has a heritability of about 85% (McGuffin et al., 2003). To date, 30 common variants have been associated with bipolar risk (Stahl et al., 2019). These variants affect ion channels, neurotransmitter transporters, synaptic, metabolic and immune components. There are significant genetic correlations with schizophrenia, depression, traits of ASD and anorexia nervosa. Interestingly, cases with a bipolar I diagnosis have higher schizophrenia polygenic risk scores than cases with bipolar II. Bipolar II cases have higher depression polygenic risk scores than bipolar I cases. Fewer CNVs have been associated with bipolar disorder than with schizophrenia or neurodevelopmental disorders (Green et al., 2016), and increased CNV burden may be limited to schizoaffective cases (Charney et al., 2018). This may provide a biological basis for clinical subtypes of bipolar disorder.
Twin Studies of Human Obesity
Published in Claude Bouchard, The Genetics of Obesity, 2020
Joanne M. Meyer, Albert J. Stunkard
The most striking feature of the Virginia data was the consistently high heritability estimates obtained on all measures. For these 11-year-olds, most of the variation in skinfold thickness and BMI was genetic in origin. Furthermore, in an analysis of longitudinal data from the same study, Meyer and colleagues10 have recently shown that the high heritability of the BMI remains throughout the adolescence of these twins (up to age 17). Meyer and colleagues also found that the “tracking” of the BMI from age 11 to 17 was high and primarily due to genetic factors, with age-to-age genetic correlations exceeding 0.79. However, some of the genetic and environmental variance during early adolescence (ages 11 and 12.5) dissipated over time, perhaps due to the variable onset of puberty in the twins.
Assigning the LR
Published in Jo-Anne Bright, Michael D. Coble, Forensic DNA Profiling, 2019
Jo-Anne Bright, Michael D. Coble
The formulae calculate the conditional probability that the true offender has a certain genotype given the person of interest also has this genotype (Balding & Nichols, 1994) (and the offender and POI are from the same subpopulation). For a homozygote example, this can be written as Pr(offender = aa | POI = aa) or simply Pr(aa | aa). For a heterozygote example, this is Pr(ab | ab). Recommendation 4.2 corrects for between individual genetic correlations, which are of interest when calculating match probabilities. By ignoring correlations, θ is set to 0 within Equation 3.3, thus returning the product rule (Equation 3.1).
Genome- and transcriptome-wide association studies show that pulmonary embolism is associated with bone-forming proteins
Published in Expert Review of Hematology, 2022
Ruoyang Feng, Mengnan Lu, Yanni Yang, Pan Luo, Lin Liu, Ke Xu, Peng Xu
In previous genetic studies of pulmonary embolism, more attention has been paid to pedigree genetics, while few studies have investigated genes associated with pulmonary embolism, and biological pathways, or common causes of pulmonary embolism. Genetic correlations are those between phenotypes of hybrid populations due to genotype, and recent studies have shown the prevalence of genetic correlations among complex human phenotypes. Linkage disequilibrium score regression (LDSC) analysis is an effective method for evaluating genetic relationships among human phenotypes [13]. Using GWAS summary data, LDSC analysis provides a simple and reliable method to simultaneously screen thousands of traits and identify the true genetic correlations among them [14]. Recently Lu and colleagues [15] reported a significant genetic correlation between rheumatoid arthritis and systemic lupus erythematosus using LDSC. Similarly, Kafle et al [16] used LDSC to find a significant genetic association between gout and attention deficit hyperactivity disorder.
Genetic variants in miRNAs differentially expressed during brain development and their relevance to psychiatric disorders susceptibility
Published in The World Journal of Biological Psychiatry, 2021
Clarice Brinck Brum, Vanessa Rodrigues Paixão-Côrtes, Andressa Marques Carvalho, Thais Martins-Silva, Marina Xavier Carpena, Kauana Ferreira Ulguim, Karen Yumaira Sánchez Luquez, Angélica Salatino-Oliveira, Luciana Tovo-Rodrigues
We also confirmed some overlap between 20 miRNAs associated with more than one psychiatric disorder at a nominal level (Table 3). It is well known that many psychiatric symptoms may transcend diagnostic criteria (Cross-Disorder Group of the Psychiatric Genomics C. 2013), which is also reinforced by the common coexistence of two or more psychiatric diagnoses in one patient (Kessler et al. 2005; van Loo and Romeijn 2015). Seven of these miRNAs are shared among at least three disorders (MIR137, MIR135A1, MIR217, MIR219A1, MIR33B, MIR96, and MIR1249). All of them, except MIR1249, are important in the early stages of development, as they are differentially expressed from infancy to early childhood. These characteristics demonstrate that different psychiatric disorders have substantial genetic correlations with one another, and our results corroborate this perspective. Besides being important for SCZ, miR-137 has been reported as relevant for the development and progression of other psychiatric disorders. miR-137 is actively being considered as a potential biomarker of bipolar disorder (BD) in the peripheral blood (Fries et al. 2018). Moreover, MIR137 plays an important role in autism, as previous autism candidate genes are enriched in the putative miR-137 regulatory sites (Devanna and Vernes 2014).
Longitudinal change of sleep timing: association between chronotype and longevity in older adults
Published in Chronobiology International, 2019
Altug Didikoglu, Asri Maharani, Antony Payton, Neil Pendleton, Maria Mercè Canal
Chronotype is important due to its relation to human health, for example, eveningness has been associated with affective disorders, cardiovascular disease, and metabolic health (Au and Reece 2017; Jones et al. 2019; Knutson and von Schantz 2018; Merikanto et al. 2013). Genetic correlation can partially explain the link between chronotype and health disorders (Jones et al. 2019, 2016). A discrepancy between social, natural, and internal rhythm can result in circadian misalignment, which may contribute towards disease risk (Fischer et al. 2016; Roenneberg et al. 2012). In addition, behaviors related to poor health such as smoking and alcohol intake, irregular eating habits and lower activity have previously been associated with evening-type diurnal preference (Gibson et al. 2018; Klimentidis et al. 2018; Vera et al. 2018). Consequently, chronotype has been shown to influence longevity (Knutson and von Schantz 2018). However, the relationship between physical and mental health problems and sleep timing is not well understood in the elderly.