Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data
Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data
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DOI：
10.1093/hmg/ddu174
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发表时间：
2014-09-01
影响因子：
3.5
通讯作者：
Visscher, Peter M.
中科院分区：
文献类型：
--
作者：
Chen, Guo-Bo;Lee, Sang Hong;Visscher, Peter M.
由于定制阵列比通用GWAS阵列更便宜，因此可以实现更大的样本大小来进行基因发现。定制阵列可以通过对相关基因座上的SNPs进行更密集的基因分型来标记更多的变异，但代价是失去全基因组覆盖。平衡这种权衡对于最大化实验设计是很重要的。我们使用免疫芯片(IChip)量化了已知候选区域捕获的SNP遗传力的增加和炎症性肠病全基因组覆盖不完善造成的损失，并分别在61 251和38 550个样本上计算了Gwas数据。对于克罗恩病(CD)，iChip和Gwas数据分别解释了19%和26%的易感性变异，而密集基因分型iChip区域的SNPs解释了iChip和Gwas数据13%的SNP遗传率。对于溃疡性结肠炎(UC)，iChip和Gwas数据分别解释了15%和19%的易感性变异，iChip和Gwas数据中密集的iChip区域分别解释了10%和9%的SNP遗传率。根据双变量分析，ICIP和GWASD数据的CD和UC之间的遗传相关性估计分别为0.75(SE 0.017)和0.62(SE 0.042)。我们还量化了包含或不包含先前对CD和UC的163次GWAS命中的基因组区域的SNP遗传率，以及密集基因分型的iChip区域和163次GWAS命中之间重叠基因座的SNP遗传率。对于这两种疾病，在不同的基因组分区上，iChip上的密集基因分型区域标记的易感性至少与Gwas数据中相应区域中的变异一样大，但由于未选择区域的低覆盖率，使用iChip数据中标记的SNP遗传性丢失了一定量。这些结果表明，带有Gwas主干的定制阵列将有助于在相关和新的基因座上发现更多的基因。
As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61 251 and 38 550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci.