ORCID Profile
0000-0002-5369-6502
Current Organisation
Yale University
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Publisher: Springer Science and Business Media LLC
Date: 26-02-2018
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222786
Abstract: Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to “impute” genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder. We applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 in iduals with subtype 1 (BD-I), 3,543 in iduals with subtype 2 (BD-II), and 1,500 in iduals with schizoaffective subtype (SAB). We identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 28-04-2023
Abstract: Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.
Publisher: Cold Spring Harbor Laboratory
Date: 23-08-2019
DOI: 10.1101/741512
Abstract: Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [ r g ], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total s le sizes per phenotype ranged from ~2,400 to ~537,000 in iduals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g =0.18 false discovery rate q =0.0006), cannabis initiation and AN ( r g =0.23 q .0001), and cannabis initiation and AN with binge-eating ( r g =0.27 q =0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating ( r gs =-0.19 to −0.23 qs .04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.
Publisher: Cambridge University Press (CUP)
Date: 05-04-2022
DOI: 10.1017/S0033291721004554
Abstract: Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and erse clinical biobank populations with electronic health record phenotypes. Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai Bio Me ™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations. Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex. Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.
Publisher: Cold Spring Harbor Laboratory
Date: 07-10-2021
DOI: 10.1101/2021.10.04.21264526
Abstract: Longitudinal weight trajectories may reflect in idual health status. We examined the genetic aetiology and clinical consequences of adult weight trajectories in males and females leveraging genetic and phenotypic data in the electronic health records (EHR) of the Bio Me ™ Biobank. We constructed four longitudinal weight trajectories using annual EHR-recorded weights (stable weight, weight gain, weight loss, or weight cycle) (n=21,487). After validating the accuracy of the trajectories (n=100), we conducted a hypothesis-free phenome-wide association study (PheWAS), including sex-stratified PheWAS, to identify diseases associated with each weight trajectory. We then performed a hypothesis-driven polygenic risk score (PRS) analysis on these weight trajectories, focusing on anorexia nervosa (AN) and depression—both commonly associated with weight changes. Weight trajectory classification was highly accurate (accuracy, sensitivity, and specificity 97% for all four trajectories). Hypothesis-free PheWAS analyses identified a significant association between depression and weight cycle (OR=1.4, p≤7.7×10 −16 ) after Bonferroni correction, but not with weight gain or loss. Compared to other weight trajectories, we also observed a significant association of osteoporosis-related phecodes with weight loss in females only (OR female =1.4, p female ≤ 1.4×10 −7 , OR male =0.8, p male ≥ 0.18). AN-PRS was positively associated with weight loss trajectory among in iduals without eating disorder diagnoses (OR top vs. bottom 10% PRS =1.95, p=0.00035). Consistent effect direction was observed across three ancestry groups. The AN-PRS-weight loss association was not attenuated by obesity-PRS (OR top vs. bottom 10% PRS =1.94). Adult weight trajectory is associated with disease both phenotypically and genetically. Our PheWAS reveals unique relationships between diseases and weight trajectory patterns, including the association of depression and weight cycle trajectory in both males and females, and osteoporosis-weight loss trajectory association in females only. In addition, our PRS analysis suggests that adults with higher AN genetic risk are more likely to have a weight loss trajectory, and this association may be independent of BMI/obesity-related genetic pathways. Klarman Family Foundation, NIMH. We used PubMed and medRxiv to search for phenome-wide association studies (PheWAS) of BMI/weight that have been published and/or are currently in preprint. For the weight PheWAS, we used search terms: “(phewas[tiab] OR phenome wide[tiab]) AND (weight[tiab] OR BMI[tiab] OR body mass index[tiab])” on PubMed, and “phewas weight”, “phewas BMI”, “phewas body mass index”, “phenome weight”, “phenome BMI”, or “phenome body mass index” for abstract or title search on medRxiv (up to March 17, 2021). The literature search identified 45 studies in total. From title screening, 13 of the studies were further reviewed, and 5 studies were ultimately included as relevant evidence of PheWAS on weight or BMI. These five PheWAS included four studies of adult populations of European ancestry, and one study conducted in children (ALSPAC). The weight-related exposure variables used in these studies were genetic variants of the obesity-associated FTO gene, BMI-associated SNPs, BMI PRS, BMI value, and obesity status. Through using BMI/obesity-related exposures, these published PheWAS identified comorbidities associated with obesity, including type 2 diabetes, sleep apnea, hypertension, edema, liver disease, asthma, bronchitis, and earlier age of puberty in at least two of the PheWAS. The childhood PheWAS found positive associations of BMI PRS with multiple biomarkers, including leptin, C-reactive protein, IL6, triglyceride, very low-density lipoprotein, and a negative association with high density lipoprotein. One BMI PheWAS published in 2020 observed that hyperlipidemia and gastroesophageal reflux disease were only significantly associated with BMI on a phenotypic level, but not on a genetic level (e.g., BMI or obesity SNPs), likely due to the small genetic effect of single genetic variants. Regarding the impact of anorexia nervosa (AN) and depression genetic risk on weight trajectory, we searched “anorexia nervosa[title] AND (weight[title] OR BMI[title] OR body mass index[title]) AND (genetic[tiab])” or “depression[title] AND (weight[title] OR BMI[title] OR body mass index[title]) AND (genetic[tiab])” on PubMed, and “anorexia polygenic weight” or “anorexia polygenic BMI” or “anorexia polygenic body mass index” or “depression polygenic weight” or “depression BMI” or “depression body mass index” for abstract or title search on medRxiv (up to March 17, 2021). The literature search identified 36 studies in total, and 21 were further reviewed through the title screening. No studies were identified that examined the effect of depression genetic risk on BMI or weight, and only two were included as relevant evidence of AN genetic risk on BMI and weight. Of these two studies, one was cross-sectional in a small adult s le (age 18-59, n=380), and the other was longitudinal in a children/young adult population in the ALSPAC cohort (age 10-24, n=8,654). BMI PRS was found to be associated with lower BMI cross-sectionally, and with weight loss over time only in females. In this study, we create a novel inflection-point based method to classify longitudinal weight trajectory using weights recorded in the EHR in a hospital-based biobank (Mount Sinai Bio Me ™ Biobank), with an accuracy of 98% or higher through our validation study (n=100). With this validated phenotype of weight pattern over time (i.e., weight trajectory), our PheWAS analysis afforded us the opportunity to examine comorbidity across the weight spectrum and across time. We identified 143 diseases associated with weight cycle (e.g., depression, anemias, renal failure),13 diseases positively associated with weight gain trajectory (e.g., obesity, obstructive sleep apnea, edema), and 36 with weight loss (e.g., protein-calorie malnutrition, gastrointestinal complication, end stage renal disease), after Bonferroni correction, using 5% as the cutoff for clinically relevant weight change. All diseases were negatively associated with a stable weight trajectory. Furthermore, we performed, to our knowledge, the first sex-stratified PheWAS related to weight trajectory, and identified eight sex-stratified associations with weight gain (e.g., obstructive sleep apnea), eight with weight loss (e.g., osteoporosis), and ten with weight cycle (e.g., vitamin B-complex deficiencies). On a genetic level, our study fills in the gap of the impact of AN genetic risk on longitudinal weight changes in the adult population. Unlike the finding in adolescents in the ALSPAC study, which found an AN-PRS-weight loss trajectory association only in females, we found an association of higher AN genetic risk with weight loss trajectory in both men and women, with consistent effect direction observed across in iduals with European, African, and Hispanic ancestry in the Bio Me ™ Biobank. Additionally, this association of AN genetics with weight loss was independent of the influence of obesity/BMI related genetic variants on weight. PheWAS is an excellent tool for exploring comorbidities associated across the weight spectrum. Our PheWAS findings identify diseases with different weight patterns (e.g., depression and weight cycle), which may reflect characteristics of these diseases, including age of onset, progression pattern, severity, and chronicity (e.g., the episodic nature of depression with the weight cycle pattern). In addition, our sex-stratified PheWAS implicates the important role of sex in weight regulation in the presence of disease. Certain sub-populations may be at greater risk of weight loss in some disease states (e.g., women with osteoporosis) and may need targeted treatment to address nutritional needs and to prevent further weight loss. Our study also suggests that people who have high AN genetic risk are at greater risk of displaying a weight loss trajectory during adulthood. However, given the limited amount of variation in the outcome of interest (e.g., weight loss) explained by the AN-PRS, the PRS may have to be jointly modeled with other risk factors to predict weight loss more accurately, or to identify subgroups at risk of weight loss. In addition, given our finding that the effect of AN genetics on weight loss was minimally affected by the obesity-related genetics, and the previously reported low genetic correlation of −0.22 between AN and obesity in the 2019 AN GWAS, this may indicate that AN- and obesity-related weight changes might have unique genetic underpinnings. Future studies that assess the pathway-specific genetic risk on weight pattern will further our understanding of the genetic architecture of longitudinal weight trajectory.
Publisher: Cold Spring Harbor Laboratory
Date: 18-02-2021
DOI: 10.1101/2021.02.12.21250941
Abstract: Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional GWAS studies produce principal evidence for the association of genetic variants with disease, and provide a jumping-off point for downstream functional analyses. Transcriptomic imputation (TI) allows for the translation of SNPs into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a more broad setting through the use of phenome-wide association studies (PheWAS) in large and erse clinical biobank populations with electronic health record (EHR) phenotypes. Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai Bio Me ™ Biobank and performed PheWAS on over 2000 clinical outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of BMI on AN-GReX clinical associations. Our S-PrediXcan analysis identified 47 genes associated with AN, including what is, to our knowledge, the first genetic association of AN with the Major Histocompatibility Complex (MHC). AN-GReX was associated with autoimmune, anthropometric, metabolic, psychiatric and gastrointestinal diagnoses in our biobank cohort, as well as measures of anthropometry, substance use, and pain score. Our analyses reveal that AN-GReX associations with measures of weight and substance use are modified by BMI, and indicate potential avenues of functional mechanism to investigate further.
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
Publisher: Springer Science and Business Media LLC
Date: 03-06-2019
DOI: 10.1038/S41588-019-0450-7
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Publisher: Springer Science and Business Media LLC
Date: 25-03-2019
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
No related grants have been discovered for Laura Huckins.