Editorial: Genetics of Complex Traits and Diseases From Under-Represented Populations
Ontology highlight
ABSTRACT:
SUBMITTER: Fatumo S
PROVIDER: S-EPMC8801802 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A major task for genetics is searching for genetic variants associated with disease. But we may well be missing a large number of "unknown unknown" alleles in the "fog of genetics".
| S-EPMC6831998 | biostudies-literature
Project description:IntroductionCancer risk and screening data are limited in their ability to inform local interventions to reduce the burden of cancer in vulnerable populations. The San Francisco Health Information National Trends Survey was developed and administered to assess the use of cancer-related information among under-represented populations in San Francisco to provide baseline data for the San Francisco Cancer Initiative.MethodsThe survey instrument was developed through consultation with research and community partners and translated into 4 languages. Participants were recruited between May and September 2017 through community-based snowball sampling with quotas to ensure adequate numbers of under-represented populations. Chi-square tests and multivariate logistic regression were used between 2018 and 2019 to assess differences in screening rates across groups and factors associated with cancer screening.ResultsOne thousand twenty-seven participants were recruited. Asians had lower rates of lifetime mammogram (p=0.02), Pap test (p<0.01), and prostate-specific antigen test (p=0.04) compared with non-Asians. Hispanics had higher rates of lifetime mammogram (p=0.02), lifetime Pap test (p=0.01), recent Pap test (p=0.03), and lifetime prostate-specific antigen test (p=0.04) compared with non-Hispanics. Being a female at birth was the only factor that was independently associated with cancer screening participation (AOR=3.17, 95% CI=1.40, 7.19).ConclusionsScreening adherence varied by race, ethnicity, and screening type. A collaborative, community-based approach led to a large, diverse sample and may serve as a model for recruiting diverse populations to add knowledge about cancer prevention preferences and behaviors. Results suggest targeted outreach efforts are needed to address disparate cancer screening behaviors within this diverse population.
| S-EPMC7173710 | biostudies-literature
Project description:Under-representation of certain populations, based on gender, race/ethnicity, and age, in data collection for predictive modeling may yield less-accurate predictions for the under-represented groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Methods to achieve fairness in the machine learning literature typically build a single prediction model subject to some fairness criteria in a manner that encourages fair prediction performances for all groups. These approaches have two major limitations: i) fairness is often achieved by compromising accuracy for some groups; ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a Joint Fairness Model (JFM) approach for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an Accelerated Smoothing Proximal Gradient Algorithm to solve the convex objective function, and demonstrate the properties of the proposed JFM estimates. Next, we presented the key asymptotic properties for the JFM parameter estimates. We examined the efficacy of the JFM approach in achieving prediction performances and parities, in comparison with the Single Fairness Model, group-separate model, and group-ignorant model through extensive simulations. Finally, we demonstrated the utility of the JFM method in the motivating example to obtain fair risk predictions for under-represented older patients diagnosed with coronavirus disease 2019 (COVID-19).
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Project description:Virtually all psychiatric traits are genetically complex. This article discusses the genetics of complex traits in psychiatry. The complexity is accounted for by numerous factors, including multiple risk alleles, epistasis, and epigenetic effects such as methylation. Risk alleles can individually be common or rare, and can include, for example, single nucleotide polymorphisms and copy number variants that are transmitted or are new mutations, and other kinds of variation. Many different kinds of variation can be important for trait risk, either together in various proportions or as different factors in different subjects. Until more recently, approaches to complex traits were limited, and consequently only a few variants, usually of individually minor effect, were identified. At the present time, a much richer armamentarium exists that includes the routine application of genome-wide association studies and next-generation high-throughput sequencing and the combination of this information with other biologically relevant information, such as expression data. We have also seen the emergence of large meta-analysis and mega-analysis consortia. These developments are extremely important for psychiatric genetics, have advanced the field substantially, and promise formidable gains in the years to come as they are applied more widely.
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Project description:Since the publication of the Human Genome Project, genetic information has been used as an accepted, evidence-based biomarker to optimize patient care through the delivery of precision health. Pharmacogenetics (PGx) uses information about genes that encode proteins involved in pharmacokinetics, pharmacodynamics, and hypersensitivity reactions to guide clinical decision making to optimize medication therapy selection. Clinical PGx implementation is growing from the dramatic increase in PGx studies over the last decade. However, an overwhelming lack of genetic diversity in current PGx studies is evident. This lack of diverse representation in PGx studies will impede equitable clinical implementation through potentially inappropriate application of gene-based dosing algorithms, whereas representing a missed opportunity for identification of population specific single nucleotide variants and alleles. In this review, we discuss the challenges of studying PGx in under-represented populations, highlight two successful PGx studies conducted in non-European populations, and propose a path forward through community-based participatory research for equitable PGx research and clinical translation.
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Project description:This study compares the effectiveness of approaches used to recruit a diverse sample for a randomized clinical trial for Hoarding Disorder (HD) in the San Francisco Bay Area. Of the 632 individuals who inquired about the study, 313 were randomized and 231 completed treatment. Most participants heard about the study via flyering (N?=?161), followed by advocacy groups (N?=?113), word of mouth (N?=?84), health care professionals (N?=?78), online (N?=?68), and media (N?=?11). However, those that heard about the study via advertising methods, such as flyers, were less likely to complete the study, p?=?.01, while those recruited via advocacy groups were most likely to be randomized, p?=?.03. No source proved more effective in recruiting underrepresented groups such as men, p?=?.60; non-whites, p?=?.49; or Hispanics, p?=?.97. Advertising recruited the youngest individuals, p?<?0.001, and word of mouth was most likely to recruit unemployed, disabled, or retired individuals, p?=?.01. Thus, results suggest an ongoing multimodal approach is likely to be most effective in both soliciting and retaining a diverse sample. Future studies should compare recruitment methods across greater geographical regions too, as well as in terms of financial and human costs.
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Project description:Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits.
| S-EPMC2752214 | biostudies-literature
Project description: Not available
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