| 1. |
|
|
| 2. |
- Justice, A. E., et al.
(författare)
-
Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits
- 2017
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
-
Tidskriftsartikel (refereegranskat)abstract
- Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.
|
|
| 3. |
|
|
| 4. |
- Clark, DW, et al.
(författare)
-
Associations of autozygosity with a broad range of human phenotypes
- 2019
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 4957-
-
Tidskriftsartikel (refereegranskat)abstract
- In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.
|
|
| 5. |
|
|
| 6. |
- Lee, James J, et al.
(författare)
-
Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.
- 2018
-
Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 50:8, s. 1112-1121
-
Tidskriftsartikel (refereegranskat)abstract
- Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1million individuals and identify 1,271independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
|
|
| 7. |
- Frazier-Wood, Alexis C., et al.
(författare)
-
Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses
- 2016
-
Ingår i: Nature Genetics. - : Nature Research (part of Springer Nature). - 1061-4036 .- 1546-1718. ; 48, s. 624-
-
Tidskriftsartikel (refereegranskat)abstract
- Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (vertical bar(p) over cap vertical bar approximate to 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
|
|
| 8. |
- Becker, Joel, et al.
(författare)
-
Resource profile and user guide of the Polygenic Index Repository
- 2021
-
Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
-
Tidskriftsartikel (refereegranskat)abstract
- Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
|
|
| 9. |
|
|
| 10. |
- Kilpeläinen, Tuomas O, et al.
(författare)
-
Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile.
- 2011
-
Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 43:8, s. 753-60
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies have identified 32 loci influencing body mass index, but this measure does not distinguish lean from fat mass. To identify adiposity loci, we meta-analyzed associations between ∼2.5 million SNPs and body fat percentage from 36,626 individuals and followed up the 14 most significant (P < 10(-6)) independent loci in 39,576 individuals. We confirmed a previously established adiposity locus in FTO (P = 3 × 10(-26)) and identified two new loci associated with body fat percentage, one near IRS1 (P = 4 × 10(-11)) and one near SPRY2 (P = 3 × 10(-8)). Both loci contain genes with potential links to adipocyte physiology. Notably, the body-fat-decreasing allele near IRS1 is associated with decreased IRS1 expression and with an impaired metabolic profile, including an increased visceral to subcutaneous fat ratio, insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease and decreased adiponectin levels. Our findings provide new insights into adiposity and insulin resistance.
|
|
