| 1. |
- Davies, G., et al.
(författare)
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Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function
- 2018
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Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
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| 2. |
- Akram, Muhammad N., et al.
(författare)
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A new biased estimator for the gamma regression model : Some applications in medical sciences
- 2023
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Ingår i: Communications in Statistics - Theory and Methods. - : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 52:11, s. 3612-3632
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Tidskriftsartikel (refereegranskat)abstract
- The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.
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| 3. |
- Qasim, Muhammad, et al.
(författare)
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Forecasting buffalo population of Pakistan using autoregressive integrated moving average (ARIMA) time series models
- 2019
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Ingår i: Proceedings of the Pakistan Academy of Sciences: Part A. - : Giunti. - 2518-4245 .- 2518-4253. ; 56:3, s. 11-20
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Tidskriftsartikel (refereegranskat)abstract
- Livestock plays a vital role in Pakistan’s economy. Buffalo is the primary source of milk and meat, which is a basic need for human health. So, there is a need to forecast the buffalo population of Pakistan. The main objective of the current study is to determine an appropriate empirical model for forecasting buffalo population of Pakistan to assess its future trend up to the year 2030. We apply different Autoregressive Integrated Moving Average (ARIMA) models on the buffalo population-based on fifty-years’ time-series dataset. Different model selection criteria are used to test the reliability of the ARIMA models. Based on these criteria, we perceive that ARIMA (1, 0, 0) is a more suitable model. Moreover, we also test the fitted model assumptions, such as normality and independence, to find out more accurate forecasted values. This study revealed that the buffalo population expected to increase 30% up to the year 2030 under the assumption that there is no irregular trend can be encountered during forecasted years.
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