We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. ...We apply MTAG to summary statistics for depressive symptoms (N
= 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.
Using a dataset that includes over 17 million students from across all 50 states, we estimate the causal impact of making structural transitions into middle school (in grades 4, 5, 6, or 7) on ...student math and reading achievement trajectories. This dataset provides an ideal opportunity to engage in the valuable scientific practice of conducting replication studies. Prior research on the impacts of middle school transitions is of high quality and rests on a strong causal warrant, but the study settings vary greatly and use data from a prior decade. We conduct a replication (i.e., using the same methods on different data) using larger, broader, and more recent data. We extend prior analyses in ways that may further strengthen the causal warrant. Finally, we explore heterogeneity of effects across subgroups and states, which may help reconcile differences in the magnitude of estimated effects across studies.
Education, Smoking, and Cohort Change Wedow, Robbee; Zacher, Meghan; Huibregtse, Brooke M. ...
American sociological review,
08/2018, Letnik:
83, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Sociologists interested in the effects of genes on complex social outcomes claim environmental conditions structure when and how genes matter, but they have only studied environmental moderation of ...genetic effects on single traits at a time (gene-by-environment interactions). In this article, we propose that the social environment can also transform the genetic link between two traits. Taking the relationship between educational attainment and smoking as an exemplary case, we use genome-wide methods to examine whether genetic variants linked to education are also linked to smoking, and whether the strength of this relationship varies across birth cohorts. Results suggest that the genetic relationship between education and smoking is stronger among U.S. adults born between 1974 and 1983 than among those born between 1920 and 1959. These results are supported by replication in additional data from the United Kingdom. Environmental conditions that differ across birth cohorts may result in the bundling of genetic effects on multiple outcomes, as anticipated by classic cohort theory. We introduce genetic correlation-by-environment interaction (rG)xE as a sociologically-informed model that will become especially useful as data for more well-powered analyses become available.
Because socioeconomic status (SES) has a major impact on mental health, an increasing number of genetic and epigenetic studies are modeling SES to investigate more accurately the molecular basis of ...psychiatric disorders. SES can also affect genomic studies through several indirect pathways, such as participation bias and gene-environment correlation. Accordingly, our field needs to become more aware of the challenges that SES can introduce in psychiatric genetic and epigenetic research. This symposium will showcase different approaches to explore the dynamics by which SES can affect psychiatric disorders and their comorbidities. Michel Nivard (Vrije Universiteit Amsterdam, The Netherlands) will present results regarding the causal effect of educational attainment on mental illness. Combining results from a within-sibship analysis in the Dutch national registry (N=1.7 million) and a two-sample Mendelian randomization analysis, educational attainment showed a causal effect with depression, alcohol dependence, anxiety, and posttraumatic stress disorder and a bidirectional relationship with attention-deficit hyperactivity disorder. Manuela Kouakou (Yale University, United States) will describe findings regarding the effect of SES on psychiatric and somatic comorbidities of schizophrenia. Leveraging data from the Psychiatric Genomic Consortium, UK Biobank, and FinnGen, this study applied multivariable genetically causal inference methods to estimate whether SES partially contributes to the association of schizophrenia with negative health outcomes such as substance abuse, depression, anxiety, metabolic disorders, cardiovascular diseases, and respiratory diseases. Sarah Paul (Washington University in St. Louis, United States) will present novel findings regarding the interplay of neighborhood and familial SES (i.e., neighborhood poverty and familial income) with polygenic scores (PGS) for educational attainment, intelligence, and executive function in the Adolescent Brain Cognitive Development Study. These results show how neighborhood poverty moderates the influence of educational attainment PGS, such that at high levels of neighborhood disadvantage, genetic propensity toward educational achievement differentiates executive function ability more so than in advantaged environments. Anders Jespersen (Aarhus University, Denmark) will discuss an epigenome-wide association study of area-based SEP deprivation that identified 15 differentially methylated regions that were upregulated during brain development in early childhood and have previously been linked to a host of chronic physical and mental health conditions, in particular of an inflammatory origin. After these four talks, Robbee Wedow (Purdue University, United States) will use his expertise in sociology, demography, and computational genetics to lead a discussion on the themes that emerged across talks and highlight both the challenges and opportunities that lie ahead for sociogenomics research.
Responses to survey questionnaires are a vital component of nearly all social and behavioural research. This study examined item nonresponse behaviour across 109 questionnaire items from 360,628 ...individuals in the UK Biobank using phenotypic and genetic data. These results were used to build an improved understanding of how item nonresponse might lead to bias in genetic studies in general.
A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This ...prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-oforigin environments and their social mobility.
Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered. We performed a genome-wide ...association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior. In aggregate, all tested genetic variants accounted for 8 to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual's sexual behavior. Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior. Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.
Abstract Introduction Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched ...scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors. Aims and Methods Drawing on data from 2337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes—use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c)—explained by PGI, SES, and lifestyle. Results Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately. Conclusions PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules. Implications Study findings point to the complementary relationship of PGI and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.
Abstract
In light of recent findings on the small proportion of variance in body mass index (BMI) explained by shared environment, and growing interests in the role of genetic susceptibility, we ...assessed the relative contribution of socioeconomic status (SES) and genome-wide polygenic score for BMI to explaining variation in BMI. Our final analytic sample included 4,918 white and 1,546 black individuals from the US National Longitudinal Study of Adolescent to Adult Health Wave IV (2007–2008) who had complete measures on BMI, demographics, SES, genetic data, and health behaviors. We used ordinary least-squares regression to assess variation in log(BMI) as a function of the aforementioned predictors, independently and mutually adjusted. All analyses were stratified by race/ethnicity in the main analysis, and further by sex. The age-adjusted variation in log(BMI) was 0.055 among whites and 0.066 among blacks. The contribution of SES and polygenic score ranged from less than1% to 6% and from 2% to 8%, respectively, and majority of the variation (87%–96%) in log(BMI) remained unexplained. Differential distribution of socioeconomic resources, stressors, and buffers may interact to produce systematically larger variation in vulnerable populations. More understanding of the contribution of biological, genetic, and environmental factors, as well as stochastic elements, in diverse phenotypic variance is needed in population health sciences.
PDF
