GWAS 全基因组关联分析 | summary statistic 概括统计 | meta-analysis 综合分析

有很多概念需要明确区分：

人有23对染色体，其中22对常染色体autosome，另外一对为性染色体sex chromosome，XX为女，XY为男。

染色体区带命名：在标示一特定的带时需要包括4项：①染色体号；②臂的符号；③区号；④在该区内的带号。

1p22表示为1号染色体短臂2区2带。

等位基因其实是一个集合，在同一个locus出现得基因型互为等位基因。Aa不能叫等位基因，正确的逻辑是：A和a是一组等位基因。由等位基因可以定义纯合和杂合。

二倍体与多倍体细胞的某些染色体上，在同一基因座上有相同的等位基因，这类细胞称为纯合子/同型合子（homozygous）。若是相同基因座上含有不同的等位基因，则称作杂合子/异型合子（heterozygous）。

summary statistic顾名思义，就和R里面的summary函数一样，是对GWAS数据的一个概括总结，包含了结果中最核心的信息。

ebi也提供了很多GWAS研究summary statistic的结果下载，https://www.ebi.ac.uk/gwas/summary-statistics

GWAS的基本原理

如何跑GWAS？

转到姊妹篇：GWAS | 全基因组关联分析 | Linkage disequilibrium (LD)连锁不平衡 | 曼哈顿图 Manhattan_plot | QQ_plot | haplotype phasing

Power

Effect size

Major allele,

Minor allele,

Minor allele frequency (MAF),

Missingness per genotype,

Missingness per individuals,

metrics that we look at include

linkage disequilibrium (LD),

variance inflation factor (VIF),

runs of homozygosity (ROH),

These provide a broad 'summary' of the data and allow us to appropriately set thresholds for quality control. It would be wrong, for example, to run a statistical test on a genotype with high missingness because the resulting P value would be misleading and could lead to erroneous conclusions from the data.

PLINK is usually the 'go to' program for analysing GWAS data, but there are other alternatives. It is also possible to read PLINK data into R and do your own analyses, but for now there are not many programs to do that.

Further information can be found here: http://zzz.bwh.harvard.edu/plink/summary.shtml

A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis

Clumping: This is a procedure in which only the most significant SNP (i.e., lowest p value) in each LD block is identified and selected for further analyses. This reduces the correlation between the remaining SNPs, while retaining SNPs with the strongest statistical evidence.

Co‐heritability: This is a measure of the genetic relationship between disorders. The SNP‐based co‐heritability is the proportion of covariance between disorder pairs (e.g., schizophrenia and bipolar disorder) that is explained by SNPs.

Gene: This is a sequence of nucleotides in the DNA that codes for a molecule (e.g., a protein)

Heterozygosity: This is the carrying of two different alleles of a specific SNP. The heterozygosity rate of an individual is the proportion of heterozygous genotypes. High levels of heterozygosity within an individual might be an indication of low sample quality whereas low levels of heterozygosity may be due to inbreeding.

Individual‐level missingness: This is the number of SNPs that is missing for a specific individual. High levels of missingness can be an indication of poor DNA quality or technical problems.

Linkage disequilibrium (LD): This is a measure of non‐random association between alleles at different loci at the same chromosome in a given population. SNPs are in LD when the frequency of association of their alleles is higher than expected under random assortment. LD concerns patterns of correlations between SNPs.

Minor allele frequency (MAF): This is the frequency of the least often occurring allele at a specific location. Most studies are underpowered to detect associations with SNPs with a low MAF and therefore exclude these SNPs.

Population stratification: This is the presence of multiple subpopulations (e.g., individuals with different ethnic background) in a study. Because allele frequencies can differ between subpopulations, population stratification can lead to false positive associations and/or mask true associations. An excellent example of this is the chopstick gene, where a SNP, due to population stratification, accounted for nearly half of the variance in the capacity to eat with chopsticks (Hamer & Sirota, 2000).

Pruning: This is a method to select a subset of markers that are in approximate linkage equilibrium. In PLINK, this method uses the strength of LD between SNPs within a specific window (region) of the chromosome and selects only SNPs that are approximately uncorrelated, based on a user‐specified threshold of LD. In contrast to clumping, pruning does not take the p value of a SNP into account.

Relatedness: This indicates how strongly a pair of individuals is genetically related. A conventional GWAS assumes that all subjects are unrelated (i.e., no pair of individuals is more closely related than second‐degree relatives). Without appropriate correction, the inclusion of relatives could lead to biased estimations of standard errors of SNP effect sizes. Note that specific tools for analysing family data have been developed.

Sex discrepancy: This is the difference between the assigned sex and the sex determined based on the genotype. A discrepancy likely points to sample mix‐ups in the lab. Note, this test can only be conducted when SNPs on the sex chromosomes (X and Y) have been assessed.

Single nucleotide polymorphism (SNP): This is a variation in a single nucleotide (i.e., A, C, G, or T) that occurs at a specific position in the genome. A SNP usually exists as two different forms (e.g., A vs. T). These different forms are called alleles. A SNP with two alleles has three different genotypes (e.g., AA, AT, and TT).

SNP‐heritability: This is the fraction of phenotypic variance of a trait explained by all SNPs in the analysis.

SNP‐level missingness: This is the number of individuals in the sample for whom information on a specific SNP is missing. SNPs with a high level of missingness can potentially lead to bias.

Summary statistics: These are the results obtained after conducting a GWAS, including information on chromosome number, position of the SNP, SNP(rs)‐identifier, MAF, effect size (odds ratio/beta), standard error, and p value. Summary statistics of GWAS are often freely accessible or shared between researchers.

The Hardy–Weinberg (dis)equilibrium (HWE) law: This concerns the relation between the allele and genotype frequencies. It assumes an indefinitely large population, with no selection, mutation, or migration. The law states that the genotype and the allele frequencies are constant over generations. Violation of the HWE law indicates that genotype frequencies are significantly different from expectations (e.g., if the frequency of allele A = 0.20 and the frequency of allele T = 0.80; the expected frequency of genotype AT is 2*0.2*0.8 = 0.32) and the observed frequency should not be significantly different. In GWAS, it is generally assumed that deviations from HWE are the result of genotyping errors. The HWE thresholds in cases are often less stringent than those in controls, as the violation of the HWE law in cases can be indicative of true genetic association with disease risk.

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Meta-analysis

Generally, if a sample includes multiple ethnic groups (e.g., Africans, Asians, and Europeans), it is recommended to perform tests of association in each of the ethnic groups separately and to use appropriate methods, such as meta‐analysis (Willer, Li, & Abecasis, 2010), to combine the results.

Fast and efficient meta‐analysis of genomewide association scans