Karen Hodgson

Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index

Abstract Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

Epigenetic Age Acceleration Assessed with Human White-Matter Images

The accurate estimation of age using methylation data has proved a useful and heritable biomarker, with acceleration in epigenetic age predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are also heritable and highly sensitive to both normal and pathological aging processes across adulthood. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity in humans. Our goal was to investigate processes that underlie interindividual variability in age-related changes in the brain. Using blood taken from a Mexican-American extended pedigree sample (n = 628; age = 23.28–93.11 years), epigenetic age was estimated using the method developed by Horvath (2013). For n = 376 individuals, diffusion tensor imaging scans were also available. The interrelationship between epigenetic age acceleration and global white matter integrity was investigated with variance decomposition methods. To test for neuroanatomical specificity, 16 specific tracts were additionally considered. We observed negative phenotypic correlations between epigenetic age acceleration and global white matter tract integrity (ρpheno = −0.119, p = 0.028), with evidence of shared genetic (ρgene = −0.463, p = 0.013) but not environmental influences. Negative phenotypic and genetic correlations with age acceleration were also seen for a number of specific white matter tracts, along with additional negative phenotypic correlations between granulocyte abundance and white matter integrity. These findings (i.e., increased acceleration in epigenetic age in peripheral blood correlates with reduced white matter integrity in the brain and shares common genetic influences) provide a window into the neurobiology of aging processes within the brain and a potential biomarker of normal and pathological brain aging. SIGNIFICANCE STATEMENT Epigenetic measures can be used to predict age with a high degree of accuracy and so capture acceleration in biological age, relative to chronological age. The white matter tracts within the brain are also highly sensitive to aging processes. We show that increased biological aging (measured using epigenetic data from blood samples) is correlated with reduced integrity of white matter tracts within the human brain (measured using diffusion tensor imaging) with data from a large sample of Mexican-American families. Given the family design of the sample, we are also able to demonstrate that epigenetic aging and white matter tract integrity also share common genetic influences. Therefore, epigenetic age may be a potential, and accessible, biomarker of brain aging.

Genome-wide significant loci for addiction and anxiety.

BACKGROUNDnPsychiatric comorbidity is common among individuals with addictive disorders, with patients frequently suffering from anxiety disorders. While the genetic architecture of comorbid addictive and anxiety disorders remains unclear, elucidating the genes involved could provide important insights into the underlying etiology.nnnMETHODSnHere we examine a sample of 1284xa0Mexican-Americans from randomly selected extended pedigrees. Variance decomposition methods were used to examine the role of genetics in addiction phenotypes (lifetime history of alcohol dependence, drug dependence or chronic smoking) and various forms of clinically relevant anxiety. Genome-wide univariate and bivariate linkage scans were conducted to localize the chromosomal regions influencing these traits.nnnRESULTSnAddiction phenotypes and anxiety were shown to be heritable and univariate genome-wide linkage scans revealed significant quantitative trait loci for drug dependence (14q13.2-q21.2, LOD=3.322) and a broad anxiety phenotype (12q24.32-q24.33, LOD=2.918). Significant positive genetic correlations were observed between anxiety and each of the addiction subtypes (ρg=0.550-0.655) and further investigation with bivariate linkage analyses identified significant pleiotropic signals for alcohol dependence-anxiety (9q33.1-q33.2, LOD=3.054) and drug dependence-anxiety (18p11.23-p11.22, LOD=3.425).nnnCONCLUSIONSnThis study confirms the shared genetic underpinnings of addiction and anxiety and identifies genomic loci involved in the etiology of these comorbid disorders. The linkage signal for anxiety on 12q24 spans the location of TMEM132D, an emerging gene of interest from previous GWAS of anxiety traits, whilst the bivariate linkage signal identified for anxiety-alcohol on 9q33 peak coincides with a region where rare CNVs have been associated with psychiatric disorders. Other signals identified implicate novel regions of the genome in addiction genetics.

The genetic basis of the comorbidity between cannabis use and major depression

BACKGROUND AND AIMSnWhile the prevalence of major depression is elevated among cannabis users, the role of genetics in this pattern of comorbidity is not clear. This study aimed to estimate the heritability of cannabis use and major depression, quantify the genetic overlap between these two traits and localize regions of the genome that segregate in families with cannabis use and major depression.nnnDESIGNnFamily-based univariate and bivariate genetic analysis.nnnSETTINGnSan Antonio, Texas, USA.nnnPARTICIPANTSnGenetics of Brain Structure and Function study (GOBS) participants: 1284 Mexican Americans from 75 large multi-generation families and an additional 57 genetically unrelated spouses.nnnMEASUREMENTSnPhenotypes of life-time history of cannabis use and major depression, measured using the semistructured MINI-Plus interview. Genotypes measured using ~1xa0M single nucleotide polymorphisms (SNPs) on Illumina BeadChips. A subselection of these SNPs were used to build multi-point identity-by-descent matrices for linkage analysis.nnnFINDINGSnBoth cannabis use [h2 xa0=xa00.614, Pxa0=xa01.00xa0×xa010-6 , standard error (SE)xa0=xa00.151] and major depression (h2 xa0=xa00.349, Pxa0=xa01.06xa0×xa010-5 , SExa0=xa00.100) are heritable traits, and there is significant genetic correlation between the two (ρg xa0=xa00.424, Pxa0=xa00.0364, SExa0=xa00.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 [logarithm of the odds (LOD)xa0=xa03.144 at 2xa0centimorgans (cM)], with a suggestive peak for cannabis use on chromosome 21 (LODxa0=xa02.123 at 37xa0cM). A significant pleiotropic linkage peak influencing both cannabis use and major depression was identified on chromosome 11 using a bivariate model (LODxa0=xa03.229 at 112xa0cM). Follow-up of this pleiotropic signal identified a SNP 20xa0kb upstream of NCAM1 (rs7932341) that shows significant bivariate association (Pxa0=xa03.10xa0×xa010-5 ). However, this SNP is rare (seven minor allele carriers) and does not drive the linkage signal observed.nnnCONCLUSIONSnThere appears to be a significant genetic overlap between cannabis use and major depression among Mexican Americans, a pleiotropy that appears to be localized to a region on chromosome 11q23 that has been linked previously to these phenotypes.

Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity

Despite over 400 peer‐reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (nu2009=u2009226) was associated with wide‐spread surface area reductions, bipolar disorder (nu2009=u2009227) and healthy comparison subjects (nu2009=u2009370) did not differ. We replicate earlier reports that head motion (estimated from time‐series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between‐group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex‐level analyses, between‐group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757–3770, 2017.

The Genetic Basis Of The Comorbidity Between Cannabis Use And Major Depression

Background While the prevalence of major depression is known to be elevated amongst cannabis users, the causes of this comorbidity are not clear. Here we investigate the role of genetics in this relationship and identify genomic loci linked to these traits. Methods Using a sample of Mexican American extended families (n=1,284), we use variance decomposition methods to establish the degree of genetic correlation between cannabis use and major depression. Genome-wide univariate and bivariate linkage scans are conducted to localize the chromosomal regions influencing these traits and the comorbidity observed between them. Results Both major depression (h2=0.349, p=1.06x10-5, SE=0.100) and cannabis use (h2=0.614, p=1.00x10-6, SE=0.151) are heritable traits, and there is significant genetic correlation between the two (ρg=0.424, p=0.0364, SE=0.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 (LOD=3.144 at 2cM), with a suggestive peak for cannabis use on chromosome 21 (LOD=2.123 at 37cM). A significant pleiotropic linkage peak influencing both major depression and cannabis use was identified on chromosome 11, using a bivariate model (LOD=3.229 at 112cM). This location spans the NCAM1-TTC12-ANKK1-DRDR2 gene cluster. Follow-up of this pleiotropic signal provided tentative evidence implicating a rare SNP 20kb upstream of NCAM1 (s7932341); with peak-wide significant bivariate association with cannabis use and major depression (p=3.10x10-5). Discussion We show that genetic influences play an important role in the comorbidity between cannabis use and major depression.Specifically, we identify a pleiotropic locus on chromosome 11, spanning the NCAM1-TTC12-ANKK1-DRDR2 gene cluster, which has been previously implicated in both addiction and depression research.

Epigenetic Age Acceleration Assessed In White-Matter Integrity: Towards A Biomarker of Successfully Brain Aging

Background Biological age acceleration as measured by aggregate epigenetic markers has been associated with a number of phenotypes including Parkinson’s, obesity and physical fitness in the elderly. Additionally, epigenetic indices of age acceleration are touted as molecular biomarkers of brain aging, based on findings linking epigenetic age of prefrontal cortex tissue with Alzheimer’s-related phenotypes. Yet, in order to develop a clinically useful brain age acceleration biomarker, peripheral tissue, rather than neural tissue, is preferable. Likewise, in vivo neuroimaging measures are desirable. To that end, we examined the relationship between blood-derived epigenetic measures of age acceleration and white matter integrity, as indexed with diffusion weighted imaging (DWI), in a large randomly ascertained family cohort. Our goal was to investigate processes that underlie inter-individual variability in age-related changes in the white matter tracts. DWI measures of white mater integrity are among the most sensitive imaging measures of aging with demonstrated heritabilities. Methods Using blood DNA methylation data taken from a Mexican-American extended pedigree sample (n=634; mean age=49.16y, range 28.11y-97.52y), epigenetic age was estimated using the method developed by S. Horvath (2013). Epigenetic age acceleration was calculated as epigenetic age regressed upon age, sex, age x sex, age2, age2 x sex, and blood cell count estimates. 379 of these individuals had available DWI scans collected on a Siemens 3T Trio MRI located at the Research Imaging Institute, UTHSCSA. An average fractional anisotropy (FA) map was derived for each subject and skeletonized using the TBSS algorithm, providing FA measures for each of 16 white matter tracts and a global index. Variance decomposition methods were then used to investigate the interrelationship between epigenetic age acceleration and white matter integrity and identify genetic influences on these two phenotypes. Results Consistent with previous reports, both epigenetic age acceleration and white matter integrity measures are heritable in this sample. We observe significant (FDR Discussion Here we demonstrate that age acceleration, as measured via methylation profiles from peripheral blood, is significantly correlated with white matter integrity in a number of tracts within the brain. We also demonstrate evidence of shared genetic influences acting on both age acceleration and white matter integrity. These findings provide an interesting window into the neurobiology of aging processes within the brain and a potential new biomarker of normal and pathological brain aging.

LOCALIZING GENETIC LOCI FOR AFFECTIVE AND PSYCHOTIC DISORDERS WITH ENDOPHENOTYPES

Abstract Although several genome-wide significant loci have been localized for schizophrenia and bipolar disorder, these findings explain a fraction of the genetic variance predisposing these illnesses and, in general, have yet to result in true gene identifications. Furthermore, there have been no similar findings for the more common mental illnesses like depression and anxiety disorders. Yet, progress in elucidating the pathophysiology of mental illnesses is predicated on causal gene identification. Focusing on quantitative endophenotypes, traits that index genetic liability for an illness, rather than diagnoses alone, provides a complementary strategy for identifying risk genes. Quantitative endophenotypes established in family-based studies of clinical samples typically vary within the normal population, providing the opportunity to localize genes influencing these traits in unselected pedigrees. Such genes are then validated in case-control samples. This normal endophenotypic variation strategy has been successfully applied to identify disease-risk genes for heart disease, obesity, and diabetes and there is no a priori reason this strategy can not be as profitable for mental illnesses. Here, I will discuss our recent work with applying neurocognitive, neuroimage and transcriptional endophenotypes in search of risk genes for affective and psychotic illnesses. Specifically, I will present work from the “Genetics of Brain Structure and Function” study, which involves acquisition of behavioral, neurocognitive and neuroimaging endophenotypes in ~2000 Mexican Americans from randomly-selected extended pedigrees. All participants have high-density SNP arrays, transcriptional data from two time points and ~1000 individuals have genome-wide sequence data. Our approach involves localizing loci for an endophenotype via genome-wide linkage or association, identifying the non-synonymous or regulatory variants diving that effect with sequence data, and then demonstrating pleiotropy with published association studies. Our results provide clear examples of how endophenotypes can provide novel genetic insights for mental illness.

Conceptualizing Major Depression: From Genes to Neuroanatomy to Epidemiology

Because its etiology remains largely unexplained, major depression, like other psychiatric diseases, is understood entirely on the basis of symptomatology. Major depression is the most common mental illness and is responsible for substantial mortality, morbidity, and disability. Arguably we know less about the root causes of major depression than about other major mental illnesses (eg, schizophrenia, bipolar disorder, autism). In the current chapter, we examine the literature on the prevalence, diagnostic heterogeneity, risk factors, neuroanatomy, neurophysiology, heritability, endophenotypes, and genetic architecture of major depressive disorder. In addition, we briefly discuss current treatments. Whereas epidemiological results stress the heterogeneity and complex nature of the illness, neuroimaging-based models typically ignore the diversity of clinical factors, potentially limiting their usefulness. Although certainly influenced by environmental factors, there is ample evidence for a genetic component to major depression. However, to date no specific genomic variant or gene has been implicated for depression.Abstract Because its etiology remains largely unexplained, major depression, like other psychiatric diseases, is understood entirely on the basis of symptomatology. Major depression is the most common mental illness and is responsible for substantial mortality, morbidity, and disability. Arguably we know less about the root causes of major depression than about other major mental illnesses (eg, schizophrenia, bipolar disorder, autism). In the current chapter, we examine the literature on the prevalence, diagnostic heterogeneity, risk factors, neuroanatomy, neurophysiology, heritability, endophenotypes, and genetic architecture of major depressive disorder. In addition, we briefly discuss current treatments. Whereas epidemiological results stress the heterogeneity and complex nature of the illness, neuroimaging-based models typically ignore the diversity of clinical factors, potentially limiting their usefulness. Although certainly influenced by environmental factors, there is ample evidence for a genetic component to major depression. However, to date no specific genomic variant or gene has been implicated for depression.