GWAS这十年 | 10 Years of GWAS Discovery: Biology, Function, and Translation
相关文章：
A Unified Framework for Association Analysis with Multiple Related Phenotypes
太重要了，不得不单独拿出来分析一下。本review高度总结了GWAS这10年的成绩、以及现在的局限性。每个搞统计遗传的都必须要好好看看。
第一篇GWAS是什么时候？谁提出的？The first successful GWAS published in 2002 studied myocardial infarction. Ozaki
trans-ethnic和meta analysis的区别？不同人种和整合分析，概念不一样，目的不一样
Twin study是什么？优势是什么？用于估计heritability。本质就是控制变量，同卵双胞胎share了同样的遗传物质，所以他们的表型差异完全可以归结为非genetic的环境变异。"identical" or monozygotic (MZ) twins share essentially 100% of their genes, which means that most differences between the twins (such as height, susceptibility to boredom, intelligence, depression, etc.) are due to experiences that one twin has but not the other twin.
GWAS里面cohort study是什么？The importance of cohort studies in the postGWAS era
LD和correlation的联系和区别？LD表征的是一个SNP的特征，correlation是两个对象之间的相关性。LD就是某个SNP的1M区域内，其所有阈值超过0.5的r2的总和。
heritability是什么？在GWAS中如何计算？heritability在不同人群中是会变的，因为GE interaction会变，看wiki定义。
在GWAS实验设计中，有哪些因素会影响power？如何计算power和控制power？见下文
什么是genetic architecture？the joint distribution of effect size and allele frequency
GWAS的SNP arrays通常包含多少个SNP位点，是如何选择出这些位点的？minor allele frequency (MAF) typically larger than 1%.
为什么要做SNP imputation，根据什么来做？haplotype
什么情况下WGS能检测出rare variant与trait的association？sample size足够，或者effect size足够，最终还是要power足够。
burden testing of rare variant是什么？以基因为单位，检验rare variant在case和control中的差异是否显著。
10 Years of GWAS Discovery: Biology, Function, and Translation
之前的第一个五年总结：Five Years of GWAS Discovery
The GWAS is an experimental design used to detect associations between genetic variants and traits in samples from populations. 可以说genetic variants，也可以说gene，或者loci。
GWAS其实是一种包含了实验设计和分析的整合方法，主要用于complex-trait的控制基因定位。如果是单基因病monogenic disease的话，就没必要做GWAS了。
对于正常的性状，比如身高，定位到的就是控制身高的一些loci；对于疾病就是定位到导致疾病的一些variant上。
variant有很多种，目前GWAS主要关注的是SNP，其实还有InDel、CNV和SV。
这就是我的主要工作，学学别人的措辞：
The path from GWAS to biology is not straightforward because an association between a genetic variant at a genomic locus and a trait is not directly informative with respect to the target gene or the mechanism whereby the variant is associated with phenotypic differences.
The statistical power to detect associations between DNA variants and a trait depends on the experimental sample size, the distribution of effect sizes of (unknown) causal genetic variants that are segregating in the population, the frequency of those variants, and the LD between observed genotyped DNA variants and the unknown causal variants.
In addition, other genome-wide scans, such as WES and WGS studies, allow testing for a burden of rare variants across shared functional units (e.g., genes) in a way that is not accessible to GWASs.
这个视频讲得很好，由浅及深：BroadE: Statistical Genetics - Rare variants
以下是本review的框架：
复杂性状的高度多基因性Complex Traits Are Highly Polygenic
phenotype可以很general，大到身高、小到基因表达、表观变化。
Polygenic就是控制复杂性状的基因或loci是很多的，如何整合解释它们整体的影响非常重要。
多效性是普遍存在的Pleiotropy Is Pervasive
多效性就是一个位点的突变可能影响了多种表现，这也就是为什么很多表型具有高度的相关性。
新的分析方法学New Analysis Methodology Underpinning New Discovery
GWAS的后续研究主要有以下四个方面：
(1) methods of better modeling population structure and relatedness between individuals in a sample during association analyses,28–34
(2) methods of detecting novel variants and gene loci on the basis of GWAS summary statistics, 35–37
(3) methods of estimating and partitioning genetic (co)variance,38,39 and
(4) methods of inferring causality.40–42
常见变异解释了大部分的累积遗传变异Common Variants Together Tag a Substantial Proportion of Additive Genetic Variance
Additive Genetic Variance就是指AA、Aa、aa之间的表型是线性的，而不是显性和隐性的关系。
遗传预测方法The Utility of GWAS-Derived Genetic Predictors
polygenic risk score (PRS)，就是根据每个个体的变异和effect size，给每一个个体一个具体的患某疾病的打分。
公共数据库的应用The Public Availability of Data Has Enabled Novel Research and Discoveries
GWAS Catalog - EMBL-EBI，最有名的数据库。
UK Biobank
从GWAS到生物学From GWAS to Biology
如何填补这个gap，于是出现了很多数据库：ENCODE Epigenome RoadMap, and GTEx
三个成功的GWAS案例Three Exemplars of GWAS Success
值得一看，如何用精简的语言高度总结一个项目。
