Editorial: Genetics of Complex Traits and Diseases From Under-Represented Populations
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ABSTRACT:
SUBMITTER: Fatumo S
PROVIDER: S-EPMC8801802 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.
| S-EPMC10690022 | biostudies-literature
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:BackgroundIntrogression from extinct Neanderthal and Denisovan human species has been shown to contribute to the genetic pool of modern human populations and their phenotypic spectrum. Evidence of how Neanderthal introgression shaped the genetics of human traits and diseases has been extensively studied in populations of European descent, with signatures of admixture reported for instance in genes associated with pigmentation, immunity, and metabolic traits. However, limited information is currently available about the impact of archaic introgression on other ancestry groups. Additionally, to date, no study has been conducted with respect to the impact of Denisovan introgression on the health and disease of modern populations. Here, we compare the way evolutionary pressures shaped the genetics of complex traits in East Asian and European populations, and provide evidence of the impact of Denisovan introgression on the health of East Asian and Central/South Asian populations.ResultsLeveraging genome-wide association statistics from the Biobank Japan and UK Biobank, we assessed whether Denisovan and Neanderthal introgression together with other evolutionary genomic signatures were enriched for the heritability of physiological and pathological conditions in populations of East Asian and European descent. In EAS, Denisovan-introgressed loci were enriched for coronary artery disease heritability (1.69-fold enrichment, p=0.003). No enrichment for archaic introgression was observed in EUR. We also performed a phenome-wide association study of Denisovan and Neanderthal alleles in six ancestry groups available in the UK Biobank. In EAS, the Denisovan-introgressed SNP rs62391664 in the major histocompatibility complex region was associated with albumin/globulin ratio (beta=-0.17, p=3.57×10-7). Neanderthal-introgressed alleles were associated with psychiatric and cognitive traits in EAS (e.g., "No Bipolar or Depression"-rs79043717 beta=-1.5, p=1.1×10-7), and with blood biomarkers (e.g., alkaline phosphatase-rs11244089 beta=0.1, p=3.69×10-116) and red hair color (rs60733936 beta=-0.86, p=4.49×10-165) in EUR. In the other ancestry groups, Neanderthal alleles were associated with several traits, also including the use of certain medications (e.g., Central/South East Asia: indapamide - rs732632 beta=-2.38, p=5.22×10-7).ConclusionsOur study provides novel evidence regarding the impact of archaic introgression on the genetics of complex traits in worldwide populations, highlighting the specific contribution of Denisovan introgression in EAS populations.
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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).
| S-EPMC8132236 | 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.
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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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