3 results
Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis
- Gerard Muntané, Javier Vázquez-Bourgon, Ester Sada, Lourdes Martorell, Sergi Papiol, Elena Bosch, Arcadi Navarro, Benedicto Crespo-Facorro, Elisabet Vilella
-
- Journal:
- European Psychiatry / Volume 66 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 28 February 2023, e28
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Background
Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown.
MethodsWe assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set.
ResultsThe results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%).
ConclusionsWe prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.
4 - GWAS replicability across time and space
- from Part I - Genome-wide association studies
-
- By Urko M. Marigorta, Universitat Pompeu Fabra, Juan Antonio Rodriguez, Universitat Pompeu Fabra, Arcadi Navarro, Universitat Pompeu Fabra, Biomedical Research Park
- Edited by Krishnarao Appasani
- Foreword by Stephen W. Scherer, Peter M. Visscher
-
- Book:
- Genome-Wide Association Studies
- Published online:
- 18 December 2015
- Print publication:
- 14 January 2016, pp 53-66
-
- Chapter
- Export citation
-
Summary
Introduction
The key step to validating associations between genetic variants and complex human diseases is the replication of findings in independent samples. This was, perhaps, the main lesson learned by the community from the candidate–gene association studies that were performed prior to the era dominated by genome-wide association studies (GWAS). Since the mid-1990s, thousands of papers had been published describing new associations between candidate variants and complex diseases (Ioannidis et al., 2001). However, the actual worth of many of these publications was inherently constrained by small sample sizes, among many other factors, which imposed hard limits to statistical power; by a poor characterization of the structure of genomic variability in human populations, which generated many false positives; and by a focus on common alleles discovered in peoples of European ancestry, with frequencies usually above 5%, which resulted in a strong ascertainment bias. Due to these powerful reasons, and despite their enormous popularity, associations reported during the pre-GWAS era frequently failed to replicate in independent studies (Ioannidis et al., 2001). For instance, out of the 166 most widely studied associations by 2002, only six had been positively replicated three or more times (Lohmueller et al., 2003). This plethora of promising but eventually failed associations seriously undermined the credibility of the whole association-mapping approach, but, on the bright side, made researchers aware that they needed to do better.
Many of the problems were indeed addressed by the design of GWAS. In sharp contrast with previous association studies, the GWAS era has been characterized by much larger sample sizes, an extensive coverage of human genomic diversity, careful control of the effects of population stratification, more stringent significance thresholds to avoid false positives due to multiple testing, and, in many publications, built-in replication samples (McCarthy et al., 2008). What has been the impact of these improvements? Do associations discovered by GWAS replicate, and, whatever the answer to these questions, can we learn anything from replication attempts? In what follows, we analyze the degree and patterns of replicability of disease-associated variants discovered by GWAS during the last 10 years. We first summarize the main patterns of GWAS replicability considering the time at which discoveries were made. We study these patterns paying special attention to differences observed according to disease classes, the strength of the reported association, as well as the statistical significance in the discovery GWAS.
2 - GWAS: a milestone in the road from genotypes to phenotypes
- from Part I - Genome-wide association studies
-
- By Urko M. Marigorta, Universitat Pompeu Fabra, Juan Antonio Rodriguez, Universitat Pompeu Fabra, Arcadi Navarro, Universitat Pompeu Fabra, Biomedical Research Park
- Edited by Krishnarao Appasani
- Foreword by Stephen W. Scherer, Peter M. Visscher
-
- Book:
- Genome-Wide Association Studies
- Published online:
- 18 December 2015
- Print publication:
- 14 January 2016, pp 12-25
-
- Chapter
- Export citation
-
Summary
Introduction: phenotypes and genetic variation
Phenotypes are composites of the observable traits of organisms and living individuals that originate from the expression of the instructions recorded in the organism's DNA under the influence of environmental factors. Researchers working in such disparate fields as livestock selection, medical genetics, behavioral economics, or evolutionary biology need to understand the genetic basis of phenotypes. For instance, plant breeders aim to predict traits such as crop response to fertilizers (Hospital, 2009); clinical geneticists intend to trace genetic mutations that result in diseases – abnormal phenotypes characterized by pathology (Sullivan et al., 2012); behavioral economists try to understand the genetic underpinnings of human behavioral variation (Navarro, 2009); and evolutionary biologists try to detect the molecular signature of natural selection in genes related to adaptive traits, such as lactase persistence (Hurst, 2009).
Despite its outstanding scientific and economic interest, studying the genetics of phenotypes is not devoid of complexities. Most traits, such as human height, tend to present continuous variation across individuals. This is because they are controlled by large numbers of genes and each causal variant explains but a tiny fraction of the overall phenotypic variation. In this regard, genome-wide association studies (GWAS) have arisen as one of the most powerful tools to unravel the alleles that underlie individual phenotypic variation. This chapter reviews the bases of the study of the genetics of polygenic traits and provides a brief historical account of the developments in the field until the current wave of GWAS.
The study of the genetic architecture of phenotypes
Forces shaping human genetic variation
Many different tools of statistical genomics, including GWAS, have been designed with the aim of mapping phenotype diversity to the underlying causal genetic factors that vary across individuals. The two main forces increasing genetic diversity in human genomes are mutation and recombination. Additional forces, such as genetic drift and natural selection, govern the fate of extant genetic variation in populations. Together, all of them shape the degree of phenotypic variability present across humans.
Two major classes of genetic variation can be distinguished in our genomes according to their size: point mutations, and structural variation (Frazer et al., 2009). Point mutations are substitutions of a single base and are known as single nucleotide polymorphisms (SNPs).