Browsing by Subject "Heritability"
Now showing 1 - 10 of 14
Results Per Page
Sort Options
Item Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits(Elsevier, 2021-06-15) Martin, Joanna; Khramtsova, Ekaterina A.; Goleva, Slavina B.; Blokland, Gabriëlla A.M.; Traglia, Michela; Walters, Raymond K.; Hübel, Christopher; Coleman, Jonathan R.I.; Breen, Gerome; Børglum, Anders D.; Demontis, Ditte; Grove, Jakob; Werge, Thomas; Bralten, Janita; Bulik, Cynthia M.; Lee, Phil H.; Mathews, Carol A.; Peterson, Roseann E.; Winham, Stacey J.; Wray, Naomi; Edenberg, Howard J.; Guo, Wei; Yao, Yin; Neale, Benjamin M.; Faraone, Stephen V.; Petryshen, Tracey L.; Weiss, Lauren A.; Duncan, Laramie E.; Goldstein, Jill M.; Smoller, Jordan W.; Stranger, Barbara E.; Davis, Lea K.; Biochemistry and Molecular Biology, School of MedicineBackground: The origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations, we tested for a sex-differentiated genetic architecture within and between traits. Methods: Using European ancestry genome-wide association summary statistics for 20 neuropsychiatric and behavioral traits, we tested for sex differences in single nucleotide polymorphism (SNP)-based heritability and genetic correlation (rg < 1). For each trait, we computed per-SNP z scores from sex-stratified regression coefficients and identified genes with sex-differentiated effects using a gene-based approach. We calculated correlation coefficients between z scores to test for shared sex-differentiated effects. Finally, we tested for sex differences in across-trait genetic correlations. Results: We observed no consistent sex differences in SNP-based heritability. Between-sex, within-trait genetic correlations were high, although <1 for educational attainment and risk-taking behavior. We identified 4 genes with significant sex-differentiated effects across 3 traits. Several trait pairs shared sex-differentiated effects. The top genes with sex-differentiated effects were enriched for multiple gene sets, including neuron- and synapse-related sets. Most between-trait genetic correlation estimates were not significantly different between sexes, with exceptions (educational attainment and risk-taking behavior). Conclusions: Sex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic and unlikely to fully account for observed sex-differentiated attributes. Larger sample sizes are needed to identify sex-differentiated effects for most traits. For well-powered studies, we identified genes with sex-differentiated effects that were enriched for neuron-related and other biological functions. This work motivates further investigation of genetic and environmental influences on sex differences.Item Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use(Wiley, 2021) Huggett, Spencer B.; Johnson, Emma C.; Hatoum, Alexander S.; Lai, Dongbing; Srijeyanthan, Jenani; Bubier, Jason A.; Chesler, Elissa J.; Agrawal, Arpana; Palmer, Abraham A.; Edenberg, Howard J.; Palmer, Rohan H. C.; Medical and Molecular Genetics, School of MedicineBackground: Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction. Methods: Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP heritability of these gene-sets using four large human GWAS: (1) alcoholic drinks per week, (2) problematic alcohol use, (3) cigarettes per day, and (4) smoking cessation. We benchmarked our findings with curated human alcohol and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height). Results: The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance-use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. Conclusion: Our results suggest that rodent gene expression studies can help to identify genes that contribute to the heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations, and considerations for future research.Item Genetic associations with psychosis and affective disturbance in Alzheimer's disease(Wiley, 2024-05-23) Antonsdottir, Inga Margret; Creese, Byron; Klei, Lambertus; DeMichele-Sweet, Mary Ann A.; Weamer, Elise A.; Garcia-Gonzalez, Pablo; Marquie, Marta; Boada, Mercè; Alarcón-Martín, Emilio; Valero, Sergi; NIA-LOAD Family Based Study Consortium; Alzheimer's Disease Genetics Consortium (ADGC); AddNeuroMed Consortium; Liu, Yushi; Hooli, Basavaraj; Aarsland, Dag; Selbaek, Geir; Bergh, Sverre; Rongve, Arvid; Saltvedt, Ingvild; Skjellegrind, Håvard K.; Engdahl, Bo; Andreassen, Ole A.; Borroni, Barbara; Mecocci, Patrizia; Wedatilake, Yehani; Mayeux, Richard; Foroud, Tatiana; Ruiz, Agustín; Lopez, Oscar L.; Kamboh, M. Ilyas; Ballard, Clive; Devlin, Bernie; Lyketsos, Constantine; Sweet, Robert A.; Medical and Molecular Genetics, School of MedicineIntroduction: Individuals with Alzheimer's disease (AD) commonly experience neuropsychiatric symptoms of psychosis (AD+P) and/or affective disturbance (depression, anxiety, and/or irritability, AD+A). This study's goal was to identify the genetic architecture of AD+P and AD+A, as well as their genetically correlated phenotypes. Methods: Genome-wide association meta-analysis of 9988 AD participants from six source studies with participants characterized for AD+P AD+A, and a joint phenotype (AD+A+P). Results: AD+P and AD+A were genetically correlated. However, AD+P and AD+A diverged in their genetic correlations with psychiatric phenotypes in individuals without AD. AD+P was negatively genetically correlated with bipolar disorder and positively with depressive symptoms. AD+A was positively correlated with anxiety disorder and more strongly correlated than AD+P with depressive symptoms. AD+P and AD+A+P had significant estimated heritability, whereas AD+A did not. Examination of the loci most strongly associated with the three phenotypes revealed overlapping and unique associations. Discussion: AD+P, AD+A, and AD+A+P have both shared and divergent genetic associations pointing to the importance of incorporating genetic insights into future treatment development. Highlights: It has long been known that psychotic and affective symptoms are often comorbid in individuals diagnosed with Alzheimer's disease. Here we examined for the first time the genetic architecture underlying this clinical observation, determining that psychotic and affective phenotypes in Alzheimer's disease are genetically correlated. Nevertheless, psychotic and affective phenotypes in Alzheimer's disease diverged in their genetic correlations with psychiatric phenotypes assessed in individuals without Alzheimer's disease. Psychosis in Alzheimer's disease was negatively genetically correlated with bipolar disorder and positively with depressive symptoms, whereas the affective phenotypes in Alzheimer's disease were positively correlated with anxiety disorder and more strongly correlated than psychosis with depressive symptoms. Psychosis in Alzheimer's disease, and the joint psychotic and affective phenotype, had significant estimated heritability, whereas the affective in AD did not. Examination of the loci most strongly associated with the psychotic, affective, or joint phenotypes revealed overlapping and unique associations.Item Genetic clustering on the hippocampal surface for genome-wide association studies(Springer Nature, 2013) Hibar, Derrek P.; Medland, Sarah E.; Stein, Jason L.; Kim, Sungeun; Shen, Li; Saykin, Andrew J.; de Zubicaray, Greig I.; McMahon, Katie L.; Montgomery, Grant W.; Martin, Nicholas G.; Wright, Margaret J.; Djurovic, Srdjan; Agartz, Ingrid; Andreassen, Ole A.; Thompson, Paul M.; Radiology and Imaging Sciences, School of MedicineImaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.Item Genome wide association studies of the Self-Rating of Effects of Ethanol (SRE)(Wiley, 2020-03) Lai, Dongbing; Wetherill, Leah; Kapoor, Manav; Johnson, Emma C.; Schwandt, Melanie; Ramchandani, Vijay A.; Goldman, David; Joslyn, Geoff; Rao, Xi; Liu, Yunlong; Farris, Sean; Mayfield, R. Dayne; Dick, Danielle; Hesselbrock, Victor; Kramer, John; McCutcheon, Vivia V.; Nurnberger, John; Tischfield, Jay; Goate, Alison; Edenberg, Howard J.; Porjesz, Bernice; Agrawal, Arpana; Foroud, Tatiana; Schuckit, Marc; Medical and Molecular Genetics, School of MedicineThe level of response (LR) to alcohol as measured with the Self-Report of the Effects of Alcohol Retrospective Questionnaire (SRE) evaluates the number of standard drinks usually required for up to four effects. The need for a higher number of drinks for effects is genetically influenced and predicts higher risks for heavy drinking and alcohol problems. We conducted genome-wide association study (GWAS) in the African-American (COGA-AA, N = 1527 from 309 families) and European-American (COGA-EA, N = 4723 from 956 families) subsamples of the Collaborative Studies on the Genetics of Alcoholism (COGA) for two SRE scores: SRE-T (average of first five times of drinking, the period of heaviest drinking, and the most recent 3 months of consumption) and SRE-5 (the first five times of drinking). We then meta-analyzed the two COGA subsamples (COGA-AA + EA). Both SRE-T and SRE-5 were modestly heritable (h2 : 21%-31%) and genetically correlated with alcohol dependence (AD) and DSM-IV AD criterion count (rg : 0.35-0.76). Genome-wide significant associations were observed (SRE-T: chromosomes 6, rs140154945, COGA-EA P = 3.30E-08 and 11, rs10647170, COGA-AA+EA P = 3.53E-09; SRE-5: chromosome13, rs4770359, COGA-AA P = 2.92E-08). Chromosome 11 was replicated in an EA dataset from the National Institute on Alcohol Abuse and Alcoholism intramural program. In silico functional analyses and RNA expression analyses suggest that the chromosome 6 locus is an eQTL for KIF25. Polygenic risk scores derived using the COGA SRE-T and SRE-5 GWAS predicted 0.47% to 2.48% of variances in AD and DSM-IV AD criterion count in independent datasets. This study highlights the genetic contribution of alcohol response phenotypes to the etiology of alcohol use disorders.Item Heritability estimation of reliable connectome features(2018) Xie, Linhui; Salama, Paul; Shen, Li; Yan, Jingwen; Rizkalla, Maher; Ben Miled, ZinaBrain imaging genetics is an emerging research field aimed at studying the underlying genetic architecture of brain structure and function by utilizing different imaging modalities. However, not all the changes in the brain are a direct result of the genetic effect. Furthermore, the imaging phenotypes are promising for genetic analyses are usually unknown. In this thesis, we focus on identifying highly heritable measures of structural brain networks derived from Diffusion Weighted Magnetic Resonance imaging data. Using data for twins that is made available by the Human Connectome Project (HCP), the reliability of edge-level measures, namely fractional anisotropy, fiber length, and fiber number in the structural connectome, as well as seven network-level measures, specifically assortativity coefficient, local efficiency, modularity, transitivity, cluster coefficient, global efficiency, and characteristic path length, were evaluated using intraclass correlation coefficients. In addition, estimates of the heritability of the reliable measures were also obtained. It was observed that across all 64,620 network edges between 360 brain regions in the Glasser parcellation, approximately 5% were significantly high heritability based on fractional anisotropy, fiber length, or fiber number. Moreover, all tested network level measures, that capture network integrity, segregation, or resilience, were found to be highly heritable, having a variance ranging from 59% to 77% that is attributable to an additive genetic effect.Item Heritability Estimation of Reliable Connectomic Features*(Springer Nature, 2018-09) Xie, Linhui; Amico, Enrico; Salama, Paul; Wu, Yu-chien; Fang, Shiaofen; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Yan, Jingwen; Shen, Li; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.Item Heritability of circle of Willis variations in families with intracranial aneurysms(Public Library of Science, 2018-01-29) Sánchez van Kammen, Mayte; Moomaw, Charles J.; Schaaf, Irene C. van der; Brown, Robert D., Jr.; Woo, Daniel; Broderick, Joseph P.; Mackey, Jason S.; Rinkel, Gabriël J. E.; Huston, John, III; Ruigrok, Ynte M.; Neurology, School of MedicineBACKGROUND: Intracranial aneurysms more often occur in the same arterial territory within families. Several aneurysm locations are associated with specific circle of Willis variations. We investigated whether the same circle of Willis variations are more likely to occur in first-degree relatives than in unrelated individuals. METHODS: We assessed four circle of Willis variations (classical, A1-asymmetry, incomplete posterior communicating artery and fetal circulation) in two independent groups of families with familial aneurysms and ≥2 first-degree relatives with circle of Willis imaging on MRA/CTA. In each (index) family we determined the proportion of first-degree relatives with the same circle of Willis variation as the proband and compared it to the proportion of first-degree relatives of a randomly selected unrelated (comparison) family who had the same circle of Willis variation as the index family's proband. Concordance in index families and comparison families was compared with a conditional logistic events/trials model. The analysis was simulated 1001 times; we report the median concordances, odds ratios (ORs), and 95% confidence intervals (95%CI). The groups were analysed separately and together by meta-analysis. RESULTS: We found a higher overall concordance in circle of Willis configuration in index families than in comparison families (meta-analysis, 244 families: OR 2.2, 95%CI 1.6-3.0) mostly attributable to a higher concordance in incomplete posterior communicating artery (meta-analysis: OR 2.8, 95%CI 1.8-4.3). No association was found for the other three circle of Willis variations. CONCLUSIONS: In two independent groups of families with familial aneurysms, the incomplete PcomA variation occurred more often within than between families suggesting heritability of this circle of Willis variation. Further studies should investigate genetic variants associated with circle of Willis formation.Item Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data(World Scientific, 2022) Bao, Jingxuan; Wen, Zixuan; Kim, Mansu; Zhao, Xiwen; Lee, Brian N.; Jung, Sang-Hyuk; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Kim, Dokyoon; Zhao, Yize; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer’s disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability.Item Novel rare variant associations with late‐life cognitive performance(Wiley, 2025-01-09) Regelson, Alexandra N.; Archer, Derek B.; Durant, Alaina; Mukherjee, Shubhabrata; Lee, Michael L.; Choi, Seo-Eun; Scollard, Phoebe; Trittschuh, Emily H.; Mez, Jesse; Bush, William S.; Kuzma, Amanda B.; Cuccaro, Michael L.; Cruchaga, Carlos; Farrer, Lindsay A.; Wang, Li-San; Schellenberg, Gerard D.; Mayeux, Richard; Kukull, Walter A.; Keene, C. Dirk; Saykin, Andrew J.; Johnson, Sterling C.; Engelman, Corinne D.; Bennett, David A.; Barnes, Lisa L.; Larson, Eric B.; Nho, Kwangsik; Goate, Alison M.; Renton, Alan E.; Marcora, Edoardo; Fulton-Howard, Brian; Patel, Tulsi; Risacher, Shannon L.; DeStefano, Anita L.; Schneider, Julie A.; Habes, Mohamad; Seshadri, Sudha; Satizabal, Claudia L.; Maillard, Pauline; Toga, Arthur W.; Crawford, Karen; Tosun, Duygu; Vance, Jeffery M.; Mormino, Elizabeth; DeCarli, Charles S.; Montine, Thomas J.; Beecham, Gary; Biber, Sarah A.; De Jager, Philip L.; Vardarajan, Badri N.; Lee, Annie J.; Brickman, Adam M.; Reitz, Christiane; Manly, Jennifer J.; Lu, Qiongshi; Rentería, Miguel Arce; Deming, Yuetiva; Pericak-Vance, Margaret A.; Haines, Jonathan L.; Crane, Paul K.; Hohman, Timothy J.; Dumitrescu, Logan C.; Medical and Molecular Genetics, School of MedicineBackground: Despite evidence that Alzheimer’s disease (AD) is highly heritable, there remains substantial “missing” heritability, likely due in part to the effect of rare variants and to the past reliance on case‐control analysis. Here, we leverage powerful endophenotypes of AD (cognitive performance across multiple cognitive domains) in a rare variant analysis to identify novel genetic drivers of cognition in aging and disease. Method: We leveraged 8 cohorts of cognitive aging with whole genome sequencing data from the AD Sequencing Project to conduct rare variant analyses of multiple domains of cognition (N = 9,317; mean age = 73; 56% female; 52% cognitively unimpaired). Harmonized scores for memory, executive function, and language were derived using confirmatory factor analysis models. Participants genetically similar to the 1000Genomes EUR reference panel were included in analysis. Variants included in the analysis had a minor allele frequency < 0.01, a minor allele count of ≥ 10, and were annotated as a high or moderate impact SNP using VEP. Associations of baseline scores in each cognitive domain were performed using SKAT‐O, including 92,905 rare variants among 16,243 genes. All tests were adjusted for sex, baseline age, sequencing center and platform, and genetic principal components. Correction for multiple comparisons was completed using the Benjamini‐Hochberg false discovery rate (FDR) procedure. Result: APOE was associated with baseline memory, language, and executive function, though only memory survived multiple‐test correction (p.FDR = 0.001). Outside of APOE, ITPKB was associated with baseline executive function (p.FDR = 0.048). AKTIP, SHCBP1L, and CCNF showed nominal associations with multiple domains of cognition that did not survive correction for multiple comparisons (p.FDRs<0.07). Conclusion: These results highlight novel rare variants associated with cognition. IPTKB is an AGORA nominated gene target for potential AD treatment. It is important in the regulation of immune cells and displays higher expression in the cortex of AD patients compared to controls. CCNF and AKTIP are brain eQTLs and have differential RNA expression in AD brains. Previously, variants in AKTIP have been associated with educational attainment, intelligence, and memory, while variants in CCNF have been associated with neuritic plaques and neurofibrillary tangles. Future analyses will incorporate longitudinal cognition and expand into additional populations.