scholarly journals Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes

2019 ◽  
Author(s):  
Benjamin W. Domingue ◽  
Jason Fletcher
Keyword(s):  
Educational Outcomes ◽  
Child Outcomes ◽  
Parental Genotype ◽  
Adopted Children ◽  
Socioeconomic Outcomes ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Biological Children ◽  
Research Designs ◽  
Children's Schooling

AbstractThere has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated lifecourse mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n=7,193) and educational outcomes for adopted (n=855) and biological (n=20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on “genetic nurture” by showing associations of parental polygenic scores with adopted children’s schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease

2017 ◽  
Author(s):  
Amit V. Khera ◽  
Mark Chaffin ◽  
Krishna G. Aragam ◽  
Connor A. Emdin ◽  
Derek Klarin ◽  
...  
Keyword(s):  
Coronary Disease ◽  
Fold Increase ◽  
Predictive Capacity ◽  
Cumulative Impact ◽  
Polygenic Score ◽  
Genome Wide ◽  
A Genome ◽  
Increased Risk ◽  
Polygenic Scores ◽  
Artery Disease

AbstractIdentification of individuals at increased genetic risk for a complex disorder such as coronary disease can facilitate treatments or enhanced screening strategies. A rare monogenic mutation associated with increased cholesterol is present in ~1:250 carriers and confers an up to 4-fold increase in coronary risk when compared with non-carriers. Although individual common polymorphisms have modest predictive capacity, their cumulative impact can be aggregated into a polygenic score. Here, we develop a new, genome-wide polygenic score that aggregates information from 6.6 million common polymorphisms and show that this score can similarly identify individuals with a 4-fold increased risk for coronary disease. In >400,000 participants from UK Biobank, the score conforms to a normal distribution and those in the top 2.5% of the distribution are at 4-fold increased risk compared to the remaining 97.5%. Similar patterns are observed with genome-wide polygenic scores for two additional diseases – breast cancer and severe obesity.One Sentence SummaryA genome-wide polygenic score identifies 2.5% of the population born with a 4-fold increased risk for coronary artery disease.

scholarly journals Cognitive Capacity Genomewide Polygenic Scores Identify Individuals Resilient to Cognitive Decline in Aging 

2021 ◽  
Author(s):  
Yoonjung Yoonie Joo ◽  
Jiook Cha ◽  
Jeremy Freese ◽  
M Geoffrey Hayes
Keyword(s):  
Cognitive Decline ◽  
Cognitive Abilities ◽  
Cognitive Capacity ◽  
European Ancestry ◽  
Memory Recall ◽  
Protective Effects ◽  
The Novel ◽  
Cognitive Domains ◽  
Genome Wide ◽  
Polygenic Scores

Abstract The genetic underpinnings of cognitive resilience in aging remains unknown. Predicting an individual’s rate of cognitive decline—or cognitive resilience—using genetics will allow personalized intervention for cognitive enhancement and optimal selection of target samples in clinical trials. Here, using genome-wide polygenic scores(GPS) as the genomic indicators for variations of human intelligence, we examined the genetic liability of cognitive abilities in the behavioral/cognitive phenome to understand individual differences in cognitive capacity over time. Using the longitudinal sociogenomic data of 8,509 European-ancestry adults between the ages of mid-60s to 70s, we found that a higher cognitive GPS significantly correlated with a slower cognitive decline specifically in memory recall, but not in other cognitive domains. Linear mixed models with cognitive GPSs explained proportions of the variances in cognitive tests up to 60.4%. This study presents the novel genetic protective effects of cognitive ability on the decline of memory recall in aging population.

scholarly journals Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

2018 ◽  
Vol 115 (22) ◽  
pp. E4970-E4979 ◽  
Author(s):  
Thomas A. DiPrete ◽  
Casper A. P. Burik ◽  
Philipp D. Koellinger
Keyword(s):  
Instrumental Variable ◽  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Body Height ◽  
Outcome Variable ◽  
Endogeneity Bias ◽  
Multiple Indicators ◽  
Instrumental Variable Regression ◽  
Genome Wide ◽  
Polygenic Scores

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.

scholarly journals Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

2019 ◽  
Vol 28 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Daniel W. Belsky ◽  
K. Paige Harden
Keyword(s):  
Educational Attainment ◽  
Association Studies ◽  
Science Research ◽  
Well Being ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Research Activities ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes

2016 ◽  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Randomized Clinical Trials ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Phenotypes ◽  
Genes And Environment ◽  
Polygenic Scores ◽  
Research Designs ◽  
Statistical Approaches

Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence—genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.  The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions. Contributors Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang

scholarly journals Personality Polygenes, Positive Affect, and Life Satisfaction

2016 ◽  
Vol 19 (5) ◽  
pp. 407-417 ◽  
Author(s):  
Alexander Weiss ◽  
Bart M. L. Baselmans ◽  
Edith Hofer ◽  
Jingyun Yang ◽  
Aysu Okbay ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Positive Affect ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genetic Influences ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Neo Five Factor Inventory ◽  
General Wellbeing ◽  
Human Primate

Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximalN= 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.

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