Abanish Singh

The putamen plays a key role in motor control, learning, and cognition, with abnormal putamen volumes associated with neuropsychiatric disorders. Using data from the ABCD study, we performed a genome wide association study (GWAS) of putamen volumes, followed by replication and pathway enrichment analyses. We next evaluated the shared genetic architecture of putamen volume and neuropsychiatric disorders—including depression, schizophrenia, Parkinson’s, ADHD, bipolar, and OCD— using SNP associations from this and prior GWASs. We identified 142 genome-wide significant SNP associations in White participants. Most identified SNPs were in gene regulatory regions and in the neuronal growth-linked genes, DCC and DSCAM. Sixteen of the seventeen most significant associations observed in White participants were also observed in non-White participants. Seventeen known SNPs from prior GWASs of putamen volume were also replicated in our ABCD analysis, including five of the eight most significant GWAS SNPs. There was considerable genetic heterogeneity between White and non-White participants in putamen-linked SNPs with significant differences between the minor allele frequencies across the two groups (Wilcoxon rank-sum test Exact prob < 0.0001). We identified a key pathway (REACTOME_DSCAM_INTERACTIONS) associated with putamen volume that involves DSCAM gene, netrin-1 protein and/or DCC gene. In addition, 28 unique SNPs from prior GWASs of neuropsychiatric disorders were strongly associated with putamen volume at Bonferroni-corrected significance, while 41 SNPs shared by at least three disorders were associated with putamen volume at a 0.05 threshold. Our findings provide deeper insights into the shared genetic architecture and cross-population differences in genetic associations of putamen volume.

The putamen plays a key role in motor control, learning, and cognition, with abnormal putamen volumes associated with neuropsychiatric disorders. Using data from the ABCD study, we performed a genome wide association study (GWAS) of putamen volumes, followed by replication and pathway enrichment analyses. We next evaluated the shared genetic architecture of putamen volume and neuropsychiatric disorders—including depression, schizophrenia, Parkinson’s, ADHD, bipolar, and OCD— using SNP associations from this and prior GWASs. We identified 199 genome-wide significant SNP associations in White participants. Most identified SNPs were in gene regulatory regions and in the neuronal growth-linked genes, DCC and DSCAM . Sixteen of the most significant associations observed in Whites were replicated in non-White participants. Twenty-one SNPs from prior GWASs of putamen volumes were also replicated in our ABCD GWAS analysis, including five of the top eight SNPs. There was considerable genetic heterogeneity between White and non-White participants in putamen-linked SNPs with significant differences between the minor allele frequencies across the two groups (Wilcoxon rank-sum test Exact prob < 0.0001). We identified a key pathway ( REACTOME_DSCAM_INTERACTIONS) associated with putamen volume that involves DSCAM gene, netrin-1 protein and/or DCC gene. In addition, 28 unique SNPs from prior GWASs of neuropsychiatric disorders were strongly associated with putamen volume at Bonferroni-corrected significance, while 40 SNPs shared by at least three disorders were associated with putamen volume at a 0.05 threshold. Our findings provide deeper insights into the shared genetic architecture and cross-population differences in genetic associations of putamen volume.

Background: Several mechanisms have been hypothesized for the pathogenesis of Alzheimer’s disease and related dementias (ADRD); however, we are still far from effective prevention and treatment of the disease, thus more basic research is needed to understand the etiological neuropathogenesis. We hypothesized that a part of increased ADRD‐risk could be due to a complex interplay of genes and daily‐life psychosocial stress.Method: We used the Health and Retirement Study (HRS) surveys and Medicare‐linked data for the discovery analysis and the Long‐Life Family Study (LLFS) dataset for replication analysis, creating a synthetic stress measure using the algorithm based on stress proxy‐indicators, as described in Singh et al., 2015. The two datasets were compared by evaluating samples’ stress levels and socioeconomic status. We performed the HRS gene‐by‐stress discovery GWAS analyses on age‐at‐onset of ADRD using linear regression with SNP, STRESS, SEX, and ancestry principal components as other terms in model. We fitted the same regression model in the LLFS replication analysis, adjusting it additionally with field centers and Huber–White familial clustering.Result: The HRS GWASs resulted in associations at p‐value of order 10⁻⁸ in both Blacks and Whites. Although most of the top 25 significant SNPs were mapped to non‐coding regions, we identified four characterized genes (BCR, MCC, KCNQ5, and DCP2) in Whites and three genes (EMCN, DPYD, and INSYN2A) in Blacks. Published literature from human and experimental studies suggested that five out of the eight genes were known for their association with neurodevelopmental disorders including Autism, intellectual disability, but also for cancers including colorectal carcinoma, gastric carcinoma, and leukemia. Although LLFS samples presented higher socioeconomic status and lower stress levels compared to HRS samples, the associations of one‐third tested SNPs from HRS Whites replicated in LLFS samples.Conclusion: Our findings suggest that daily‐life stress may contribute to the risk of ADRD via complex interactions with genes. The associations with non‐coding genetic variants may indicate their influence through gene‐regulatory roles. In addition, the associations observed with cancer genes may possibly point towards the involvement of a common mechanistic feature, e.g., angiogenesis, which needs to be investigated further.

In prior work, we identified a novel gene-by-stress association of EBF1’s common variation (SNP rs4704963) with obesity (i.e., hip, waist) in Whites, which was further strengthened through multiple replications using our synthetic stress measure. We now extend this prior work in a precision medicine framework to find the risk group using harmonized data from 28,026 participants by evaluating the following: (a) EBF1 SNPxSTRESS interaction in Blacks; (b) 3-way interaction of EBF1 SNPxSTRESS with sex, race, and age; and (c) a race and sex-specific path linking EBF1 and stress to obesity to fasting glucose to the development of cardiometabolic disease risk. Our findings provided additional confirmation that genetic variation in EBF1 may contribute to stress-induced human obesity, including in Blacks (P = 0.022) that mainly resulted from race-specific stress due to “racism/discrimination” (P = 0.036) and “not meeting basic needs” (P = 0.053). The EBF1 gene-by-stress interaction differed significantly (P = 1.01e−03) depending on the sex of participants in Whites. Race and age also showed tentative associations (Ps = 0.103, 0.093, respectively) with this interaction. There was a significant and substantially larger path linking EBF1 and stress to obesity to fasting glucose to type 2 diabetes for the EBF1 minor allele group (coefficient = 0.28, P = 0.009, 95% CI = 0.07-0.49) compared with the same path for the EBF1 major allele homozygotes in White females and also a similar pattern of the path in Black females. Underscoring the race-specific key life-stress indicators (e.g., racism/discrimination) and also the utility of our synthetic stress, we identified the potential risk group of EBF1 and stress-induced human obesity and cardiometabolic disease.

Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial stress from a dozen studies. Using our synthetic stress algorithm, we constructed a synthetic chronic psychosocial stress measure in nine out of twelve studies where a formal self-rated stress measure was not available. The mega-analytic partial correlation between the stress measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and measurement of human health risk variables.

Chronic psychosocial stress adversely affects health and is associated with the development of disease [Williams, ]. Systematic epidemiological and genetic studies are needed to uncover genetic variants that interact with stress to modify metabolic responses across the life cycle that are the proximal contributors to the development of cardiovascular disease and precipitation of acute clinical events. Among the central challenges in the field are to perform and replicate gene‐by‐environment (G × E) studies. The challenge of measurement of individual experience of psychosocial stress is magnified in this context. Although many research datasets exist that contain genotyping and disease‐related data, measures of psychosocial stress are often either absent or vary substantially across studies. In this paper, we provide an algorithm to create a synthetic measure of chronic psychosocial stress across multiple datasets, applying a consistent criterion that uses proxy indicators of stress components. We validated the computed scores of chronic psychosocial stress by observing moderately strong and significant correlations with the self‐rated chronic psychosocial stress in the Multi‐Ethnic Study of Atherosclerosis Cohort (Rho = 0.23, P < 0.0001) and with the measures of depressive symptoms in five datasets (Rho = 0.15–0.42, Ps = 0.005 to <0.0001) and by comparing the distributions of the self‐rated and computed measures. Finally, we demonstrate the utility of this computed chronic psychosocial stress variable by providing three additional replications of our previous finding of gene‐by‐stress interaction with central obesity traits [Singh et al., ].