全基因组关联研究(GWAS)中归算技术的问题与挑战综述

Rahul Banerjee, Bharti ., Shbana Begum, Pankaj Das, Tauqueer Ahmad
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引用次数: 0

摘要

一项全基因组关联研究(GWAS)快速扫描许多个体的DNA标记,以发现与疾病的遗传联系。新的发现有助于疾病的检测、治疗和预防。在基因研究中,当数据由于质量、成本或设计问题而缺失时,Imputation预测未分型的基因型。这是一种经过验证的统计技术,可以通过从密集的基因型参考面板中借用单倍型片段来估计未观察到的基因型。这允许在未分析的变量中对关联进行估计和测试。基因型插入在分析全基因组关联扫描中至关重要,有助于遗传学家评估未分型遗传标记的关联证据。本摘要概述了缺失的数据问题和各种代入方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Issues and Challenges of Imputation Techniques in Genome Wide Association Studies (GWAS): A Review
A genome-wide association study (GWAS) rapidly scans DNA markers in many individuals to find genetic links to diseases. New findings aid in disease detection, treatment and prevention. Imputation predicts untyped genotypes in genetic studies when data is missing due to quality, cost, or design issues. It’s a proven statistical technique for estimating unobserved genotypes by borrowing haplotype segments from a densely genotyped reference panel. This allows estimation and testing of associations at unassayed variants.Genotype imputation is vital in analyzing genome-wide association scans, helping geneticists evaluate evidence for association at untyped genetic markers. This summary outlines missing data issues and various imputation methods.
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