该文献纳入了EGG（Early Growth Genetics Consortium）和UK biobank两大数据库，分为欧洲祖先和非欧洲祖先群体。这两个数据用到的样本量分别如下：
Early Growth Genetics Consortium（网址：http://egg-consortium.org/）
» 30 studies (European ancestry)
N=75,891
» 6 studies (Non-European ancestry),
N=10,104
UK BioBank（网址： http://www.ukbiobank.ac.uk/）
» 1 study (European ancestry)
N=67,786
整个分析流程如下图：
对于GWAS大样本量研究来说，该文献可借鉴的研究思路有以下几个：
1、先把GWAS和QQ plot画出来
Manhattan and quantile–quantile (QQ) plots of the trans-ancestry meta-analysis for BW
2、将不同群体做一个meta分析
Sixty loci associated with BW (P<5×10−8) in European ancestry meta-analysis of up to 143,677 individuals and/or trans-ancestry meta-analysis of up to 153,781 individuals. 53 of these loci were novel and seven previously reported BW signals.
3、计算显著信号的SNP的效应值（effect size）
The effect of the lead SNP (absolute value of β, y axis) is given as a function of minor allele frequency (x axis) for 60 known (pink) and novel (green) BW loci from the trans-ancestry meta-analysis.
4、近似条件分析（Approximate conditional analysis）
Loci with multiple distinct association signals attaining genome-wide significance (P<5x10-8) in approximate conditional meta-analysis of 143,677 individuals of European ancestry, using 5,000 white British participants from UK BioBank as a reference for linkage disequilibrium.
5、计算母体和胎儿基因型对表型的影响（Effects on phenotypes of maternal and fetal genotype）
此分析针对有母亲和婴儿的基因型数据
Comparison of fetal effect sizes and maternal effect sizes at 60 known and novel birth weight loci, for the first 24 loci
6、连锁不平衡得分回归分析（Linkage-disequilibrium score regression）
Genome-wide genetic correlation between BW and a range of traits and diseases in later life.
7、基因集富集分析（Gene set enrichment analysis）
8、与表型相关位点的层次聚类分析（Hierarchical clustering）
9、蛋白质相互作用网络分析（Protein–protein interaction network analyses）
10、接触点分析（Point of contact analyses）
总结：只要有大样本量，GWAS想怎么玩转就怎么玩转，高分文章都不在话下。但问题就是，很多人都卡在了大样本量这一步了，这也是后面的分析玩不开的原因。信号都没有，何谈各种互作分析。