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Behavior Genetics Mar 2023A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic... (Review)
Review
A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to slow down in the 1990s, molecular genetics made it possible to assess DNA variation directly. From a rocky start with candidate gene association research, by 2005 the technological advance of DNA microarrays enabled genome-wide association studies, which have successfully identified some of the DNA variants that contribute to the ubiquitous heritability of behavioral traits. The ability to aggregate the effects of thousands of DNA variants in polygenic scores has created a DNA revolution in the behavioral sciences by making it possible to use DNA to predict individual differences in behavior from early in life.
Topics: Genome-Wide Association Study; Genetics, Behavioral; Phenotype; Oligonucleotide Array Sequence Analysis; Multifactorial Inheritance
PubMed: 36662387
DOI: 10.1007/s10519-023-10132-3 -
American Journal of Human Genetics Dec 2022The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous... (Review)
Review
The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.
Topics: Humans; Multifactorial Inheritance; Genome-Wide Association Study; Genetic Research; Genotype; Phenotype
PubMed: 36459976
DOI: 10.1016/j.ajhg.2022.10.012 -
PLoS Genetics Jul 2022Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We...
Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. We thus used a birth cohort study to investigate differences in association and explained variance in polygenic risk for high BMI across infancy to old age (2-69 years). A secondary aim was to investigate how the association between BMI and a key purported environmental determinant (childhood socioeconomic position) differed across life, and whether this operated independently and/or multiplicatively of genetic influences. Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured BMI at 12 timepoints from 2-69 years. We used multiple polygenic indices from GWAS of adult and childhood BMI, and investigated their associations with BMI at each age. For polygenic liability to higher adult BMI, the trajectories of effect size (β) and explained variance (R2) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; quantile regression analyses showed that effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance ('non-shared') environmental influences on BMI across life.
Topics: Adolescent; Adult; Aged; Body Mass Index; Child; Child, Preschool; Cohort Studies; Follow-Up Studies; Humans; Middle Aged; Multifactorial Inheritance; Risk Factors; Socioeconomic Factors; Young Adult
PubMed: 35834443
DOI: 10.1371/journal.pgen.1010233 -
Nature Neuroscience Oct 2016Hundreds of genetic loci increasing risk for neuropsychiatric disorders have recently been identified. This success, perhaps paradoxically, has posed challenges for... (Review)
Review
Hundreds of genetic loci increasing risk for neuropsychiatric disorders have recently been identified. This success, perhaps paradoxically, has posed challenges for therapeutic development, which are amplified by the highly polygenic and pleiotropic nature of these genetic contributions. Success requires understanding the biological impact of single genetic variants and predicting their effects within an individual. Comprehensive functional genomic annotation of risk loci provides a framework for interpretation of neurobiological impact, requiring experimental validation with in vivo or in vitro model systems. Systems-level, integrative pathway analyses are beginning to elucidate the additive, polygenic contributions of risk variants on specific cellular, molecular, developmental, or circuit-level processes. Although most neuropsychiatric disease modeling has focused on genes disrupted by rare, large-effect-size mutations, common smaller-effect-size variants may also provide solid therapeutic targets to inform precision medicine approaches. Here we enumerate the promise and challenges of a genomics-driven approach to uncovering neuropsychiatric disease mechanisms and facilitating therapeutic development.
Topics: Animals; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Mutation; Precision Medicine; Psychotherapeutic Processes
PubMed: 27786179
DOI: 10.1038/nn.4409 -
Neuropsychopharmacology : Official... Jan 2021Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from... (Review)
Review
Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.
Topics: Depression; Depressive Disorder, Major; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Neuroimaging
PubMed: 32781460
DOI: 10.1038/s41386-020-00789-3 -
Nature Genetics Dec 2023Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face...
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
Topics: Humans; Depressive Disorder, Major; Genetic Predisposition to Disease; Biological Specimen Banks; Genome-Wide Association Study; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide
PubMed: 37985818
DOI: 10.1038/s41588-023-01559-9 -
Philosophical Transactions of the Royal... Mar 2018An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the... (Review)
Review
An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'.
Topics: Genomics; Humans; Mental Disorders; Multifactorial Inheritance; Phenotype
PubMed: 29352030
DOI: 10.1098/rstb.2017.0031 -
American Journal of Human Genetics Dec 2022Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated...
Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.
Topics: Adult; Humans; Genome-Wide Association Study; Diabetes Mellitus, Type 2; Multifactorial Inheritance; Genetic Predisposition to Disease; Medical History Taking; Risk Factors
PubMed: 36347255
DOI: 10.1016/j.ajhg.2022.10.009 -
Pharmacogenomics Dec 2014The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified... (Review)
Review
The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.
Topics: Disease; Drug Therapy; Drug-Related Side Effects and Adverse Reactions; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Pharmacogenetics
PubMed: 25521360
DOI: 10.2217/pgs.14.144 -
Nature Communications Dec 2020The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets...
The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5×) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations (P < 7.45 × 10) across the allele frequency spectrum, all of which replicate in an independent cohort (n = 1605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.
Topics: Gene Expression Regulation; Gene Regulatory Networks; Genetic Predisposition to Disease; Humans; Multifactorial Inheritance; Myocardium; Proteome; Quantitative Trait Loci; Risk Factors; Whole Genome Sequencing
PubMed: 33303764
DOI: 10.1038/s41467-020-20079-2