| 1. |
- Sawcer, Stephen, et al.
(author)
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Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
- 2011
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 476:7359, s. 214-219
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Journal article (peer-reviewed)abstract
- Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.
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| 2. |
- Beecham, Ashley H, et al.
(author)
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Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.
- 2013
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:11, s. 1353-60
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Journal article (peer-reviewed)abstract
- Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.
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| 3. |
- McQuillan, Ruth, et al.
(author)
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Evidence of Inbreeding Depression on Human Height
- 2012
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In: PLOS Genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 8:7, s. e1002655-
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Journal article (peer-reviewed)abstract
- Stature is a classical and highly heritable complex trait, with 80%–90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ2 = 83.89, df = 1; p = 5.2×10−20). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.
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| 4. |
- Nagueh, Sherif F., et al.
(author)
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Interobserver Variability in Applying American Society of Echocardiography/European Association of Cardiovascular Imaging 2016 Guidelines for Estimation of Left Ventricular Filling Pressure
- 2019
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In: Circulation Cardiovascular Imaging. - 1941-9651 .- 1942-0080. ; 12:1
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Journal article (peer-reviewed)abstract
- BACKGROUND:Assessment of left ventricular (LV) filling pressure is among the important components of a comprehensive echocardiographic report. Previous studies noted wide limits of agreement using 2009 American Society of Echocardiography/European Association of Echocardiography guidelines, but reproducibility of 2016 guidelines update in estimating LV filling pressure is unknown.METHODS:Echocardiographic and hemodynamic data were obtained from 50 patients undergoing cardiac catheterization for clinical indications. Clinical and echocardiographic findings but not invasive hemodynamics were provided to 4 groups of observers, including experienced echocardiographers and cardiology fellows. Invasively acquired LV filling pressure was the gold standard.RESULTS:In group I of 8 experienced echocardiographers from the guidelines writing committee, sensitivity for elevated LV filling pressure was 92% for all observers, and specificity was 93 +/- 6%. Fleiss kappa-value for the agreement in group I was 0.80. In group II of 4 fellows in training, sensitivity was 91 +/- 2%, and specificity was 95 +/- 2%. Fleiss kappa-value for the agreement in group II was 0.94. In group III of 9 experienced echocardiographers who had not participated in drafting the guidelines, sensitivity was 88 +/- 5%, and specificity was 91 +/- 7%. Fleiss kappa-value for the agreement in group III was 0.76. In group IV of 7 other fellows, sensitivity was 91 +/- 3%, and specificity was 92 +/- 5%. Fleiss kappa-value for the agreement in group IV was 0.89.CONCLUSIONS:There is a good level of agreement and accuracy in the estimation of LV filling pressure using the American Society of Echocardiography/European Association of Cardiovascular Imaging 2016 recommendations update, irrespective of the experience level of the observer.
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| 5. |
- Rothenberg, W. Andrew, et al.
(author)
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Predicting Adolescent Mental Health Outcomes Across Cultures : A Machine Learning Approach
- 2023
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In: Journal of Youth and Adolescence. - 0047-2891 .- 1573-6601. ; 52:8, s. 1595-1619
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Journal article (peer-reviewed)abstract
- Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.
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