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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
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Relative Risk01:12

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Pleiotropy01:33

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Related Experiment Video

Updated: Jul 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Ensembled best subset selection using summary statistics for polygenic risk prediction.

Tony Chen, Haoyu Zhang, Rahul Mazumder

    Biorxiv : the Preprint Server for Biology
    |October 27, 2023
    PubMed
    Summary

    ALL-Sum offers a faster, more accurate method for polygenic risk scores (PRS), improving personalized medicine. This computational tool enhances risk stratification by balancing predictive power and efficiency for complex traits.

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    Area of Science:

    • Genetics and Bioinformatics
    • Computational Biology
    • Personalized Medicine

    Background:

    • Polygenic risk scores (PRS) are crucial for risk stratification and personalized medicine.
    • Existing PRS methods often present a trade-off between predictive accuracy and computational efficiency.
    • There is a need for scalable and accurate PRS methods for large-scale genomic studies.

    Approach:

    • Introduced ALL-Sum, a novel PRS method utilizing an efficient summary statistic-based L0L2 penalized regression algorithm.
    • Incorporated an ensembling step to aggregate estimates from various tuning parameters, enhancing prediction performance.
    • Developed ALL-Sum as a user-friendly command-line software with pre-computed reference data for accessible analysis.

    Key Points:

    • ALL-Sum demonstrates superior prediction accuracy, runtime, and memory efficiency compared to existing methods in large-scale simulations.
    • The method performs exceptionally well across diverse polygenicity levels and genome-wide association study (GWAS) sample sizes.
    • Analysis of 27 GWAS summary statistics for 11 complex traits confirmed ALL-Sum's high accuracy, especially for large GWAS datasets.

    Conclusions:

    • ALL-Sum provides a significant advancement in PRS methodology, offering a computationally efficient and highly accurate solution.
    • The tool facilitates improved population risk stratification and accelerates the application of personalized medicine.
    • ALL-Sum is poised to become a valuable resource for genetic researchers and clinicians analyzing complex trait associations.