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- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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- Mishra, A., et al.
(author)
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Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611, s. 115-123
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Journal article (peer-reviewed)abstract
- Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
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- Luczak, S. E., et al.
(author)
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Remember this : Age moderation of genetic and environmental contributions to verbal episodic memory from midlife through late adulthood
- 2023
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In: Intelligence. - : Elsevier. - 0160-2896 .- 1873-7935. ; 99
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Journal article (peer-reviewed)abstract
- It is well documented that memory is heritable and that older adults tend to have poorer memory performance than younger adults. However, whether the magnitudes of genetic and environmental contributions to late-life verbal episodic memory ability differ from those at earlier ages remains unresolved. Twins from 12 studies participating in the Interplay of Genes and Environment in Multiple Studies (IGEMS) consortium constituted the analytic sample. Verbal episodic memory was assessed with immediate word list recall (N = 35,204 individuals; 21,792 twin pairs) and prose recall (N = 3805 individuals; 2028 twin pairs), with scores harmonized across studies. Average test performance was lower in successively older age groups for both measures. Twin models found significant age moderation for both measures, with total inter-individual variance increasing significantly with age, although it was not possible definitively to attribute the increase specifically to either genetic or environmental sources. Pooled results across all 12 studies were compared to results where we successively dropped each study (leave-one-out) to assure results were not due to an outlier. We conclude the models indicated an overall increase in variance for verbal episodic memory that was driven by a combination of increases in the genetic and nonshared environmental parameters that were not independently statistically significant. In contrast to reported results for other cognitive domains, differences in environmental exposures are comparatively important for verbal episodic memory, especially word list learning.
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- Beam, Christopher R., et al.
(author)
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Estimating Likelihood of Dementia in the Absence of Diagnostic Data : A Latent Dementia Index in 10 Genetically Informed Studies
- 2022
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In: Journal of Alzheimer's Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 90:3, s. 1187-1201
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Journal article (peer-reviewed)abstract
- BACKGROUND: Epidemiological research on dementia is hampered by differences across studies in how dementia is classified, especially where clinical diagnoses of dementia may not be available. OBJECTIVE: We apply structural equation modeling to estimate dementia likelihood across heterogeneous samples within a multi-study consortium and use the twin design of the sample to validate the results. METHODS: Using 10 twin studies, we implement a latent variable approach that aligns different tests available in each study to assess cognitive, memory, and functional ability. The model separates general cognitive ability from components indicative of dementia. We examine the validity of this continuous latent dementia index (LDI). We then identify cut-off points along the LDI distributions in each study and align them across studies to distinguish individuals with and without probable dementia. Finally, we validate the LDI by determining its heritability and estimating genetic and environmental correlations between the LDI and clinically diagnosed dementia where available. RESULTS: Results indicate that coordinated estimation of LDI across 10 studies has validity against clinically diagnosed dementia. The LDI can be fit to heterogeneous sets of memory, other cognitive, and functional ability variables to extract a score reflective of likelihood of dementia that can be interpreted similarly across studies despite diverse study designs and sampling characteristics. Finally, the same genetic sources of variance strongly contribute to both the LDI and clinical diagnosis. CONCLUSION: This latent dementia indicator approach may serve as a model for other research consortia confronted with similar data integration challenges.
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- Howe, LJ, et al.
(author)
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Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
- 2022
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:65, s. 581-
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Journal article (peer-reviewed)abstract
- Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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- Finkel, Deborah, et al.
(author)
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Age and sex differences in the genetic architecture of measures of subjective health : Relationships with physical health, depressive symptoms, and episodic memory
- 2024
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In: The journals of gerontology. Series B, Psychological sciences and social sciences. - : Oxford University Press. - 1079-5014 .- 1758-5368.
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Journal article (peer-reviewed)abstract
- OBJECTIVES: Subjective health (SH) is not just an indicator of physical health, but also reflects active cognitive processing of information about one's own health and has been associated with emotional health measures, such as neuroticism and depression. Behavior genetic approaches investigate the genetic architecture of SH, i.e., genetic and environmental influences on individual differences in SH and associations with potential components such as physical, cognitive, and emotional health. Previous twin analyses have been limited by sex, sample size, age range, and focus on single covariates.METHODS: The current analysis used data from 24,173 adults ranging in age from 40-90 years from the international Interplay of Genes and Environment Across Multiple Studies (IGEMS) consortium to investigate the genetic architecture of three measures of SH: self-rated health, health compared to others, and impact of health on activities. Independent pathways model of SH included physical health, depressive symptoms, and episodic memory, with age, sex, and country included as covariates.RESULTS: Most or all of the genetic variance for SH measures was shared with physical health, depressive symptoms, and episodic memory. Genetic architecture of SH differed across measures, age groups (40-65, 66-90), and sexes. Age comparisons indicated stronger correlations with all 3 covariates in older adults, often resulting from greater shared genetic variance.DISCUSSION: The predictive value of SH has been amply demonstrated. The higher genetic contributions to associations between SH and its components in older adults support the increasing conceptualization with age of SH as an intuitive summation of one's vital reserve.
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