Advances and challenges in quantitative delineation of the genetic architecture of complex traits

被引：0

作者：

Tang, Hua ^{[1
]}

He, Zihuai ^{[2
,3
]}

机构：

[1] Stanford Univ, Dept Genet, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Neurol & Neurol Sci, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Med, Quantitat Sci Unit, Stanford, CA 94305 USA

来源：

QUANTITATIVE BIOLOGY | 2021年 / 9卷 / 02期

关键词：

genome-wide association study; heritability; rare variants; biobank; colocalization; eQTL; polygenic risk scores; transcriptome-wide association study; GENOME-WIDE ASSOCIATION; LEVERAGING MULTIETHNIC EVIDENCE; MIXED-MODEL ANALYSIS; COMMON DISEASE; PARTITIONING HERITABILITY; GENOTYPE IMPUTATION; RARE VARIANTS; SEQUENCE; GWAS; TRANSCRIPTOME;

D O I：

10.15302/J-QB-021-0249

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Background: Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases. Results: This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted. Conclusion: GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

引用

页码：168 / 184

页数：17

共 155 条

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