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相关概念视频

Probability Laws01:49

Probability Laws

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Polygenic Traits01:18

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
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相关实验视频

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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基于使用BPC方法的多基因预测估计疾病概率.

Emil Uffelmann, ,

    medRxiv : the preprint server for health sciences
    |January 23, 2024
    PubMed
    概括

    贝叶斯多基因分数 (PGSs) 对二进制疾病特征的校准不佳. 新的贝叶斯多基因分数概率转换 (BPC) 方法改进了使用GWAS数据对绝对疾病概率预测的校准.

    科学领域:

    • 遗传学 是一个遗传学.
    • 生物统计学 生物统计学
    • 计算生物学 计算生物学

    背景情况:

    • 多基因分数 (PGSs) 使用全基因组关联研究 (GWAS) 数据汇总特征的遗传倾向.
    • 现有的贝叶斯式PGS方法为连续特征提供了更好的预测准确性,但在确定的样本中缺乏对二进制失序特征的校准.
    • 准确的PGS校准对于估计临床应用的绝对个体疾病概率至关重要.

    研究的目的:

    • 引入和评估贝叶斯多基因分数概率转换 (BPC) 方法,用于对二进制疾病特征进行PGS校准.
    • 为了使个人绝对疾病概率的可靠计算.

    主要方法:

    • BPC方法使用了GWAS总结统计,贝叶斯式PGS方法 (例如PRScs,SBayesR),个体基因型数据和先前疾病概率.
    • 它涉及将PGS转换为负债规模,计算PGS在案例和控制中的差异,并应用贝叶斯定理.
    • 该方法很实用,因为它不需要单独的调样本,包括基因型和表型数据.

    主要成果:

    • BPC方法在九种疾病的广泛模拟和经验数据上展示了精确校准的结果.
    • 与最近发表的另一种校准方法相比,性能始终优越.
    • 该方法有效地将PGS转换为准确的绝对乱概率.

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    结论:

    • 贝叶斯多基因分数概率转换 (BPC) 方法为校准多基因分数用于二进制疾病预测提供了强大而实用的解决方案.
    • 这种方法有助于对绝对个体疾病概率的可靠估计,为临床实施铺平了道路.
    • 与现有的疾病遗传风险预测方法相比,BPC提供了更好的准确性和校准.