Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis.
Common genetic variation associated with Mendelian disease severity revealed through cryptic phenotype analysis.
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DOI：
10.1038/s41467-022-31030-y
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发表时间：
2022-06-27
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
临床异质性在孟德尔病中很常见，但样本量小，难以确定具体的影响因素。然而，如果一种疾病代表表型变异谱中受严重影响的极端，那么修饰效应可能在人群的更大子集中是明显的。利用这种全频谱的分析可以大大提高功率。为了验证这一点，我们开发了隐型表型分析，这是一种基于模型的方法，通过定性症状数据推断定量性状，从而捕获与疾病相关的表型变异性。通过将这种方法应用于两个队列中的50种孟德尔疾病，我们确定了可靠量化疾病严重程度的特征。然后，我们对五种推断的隐型进行全基因组关联分析，揭示了预测孟德尔病相关诊断和结果的常见变异。总的来说，这项研究强调了计算衍生表型和生物库规模队列的效用，用于研究孟德尔疾病的复杂遗传结构。罕见遗传疾病的严重程度往往因人而异，但样本量小，很难确定致病因素。在这里，作者使用生物库规模的临床和遗传数据来研究常见遗传变异的作用。
Clinical heterogeneity is common in Mendelian disease, but small sample sizes make it difficult to identify specific contributing factors. However, if a disease represents the severely affected extreme of a spectrum of phenotypic variation, then modifier effects may be apparent within a larger subset of the population. Analyses that take advantage of this full spectrum could have substantially increased power. To test this, we developed cryptic phenotype analysis, a model-based approach that infers quantitative traits that capture disease-related phenotypic variability using qualitative symptom data. By applying this approach to 50 Mendelian diseases in two cohorts, we identify traits that reliably quantify disease severity. We then conduct genome-wide association analyses for five of the inferred cryptic phenotypes, uncovering common variation that is predictive of Mendelian disease-related diagnoses and outcomes. Overall, this study highlights the utility of computationally-derived phenotypes and biobank-scale cohorts for investigating the complex genetic architecture of Mendelian diseases. The severity of rare genetic diseases often varies between individuals, but small sample sizes make it difficult to identify contributing factors. Here, the authors use biobank-scale clinical and genetic data to investigate a role for common genetic variation.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
16.6
作者：
Corvol H;Blackman SM;Boëlle PY;Gallins PJ;Pace RG;Stonebraker JR;Accurso FJ;Clement A;Collaco JM;Dang H;Dang AT;Franca A;Gong J;Guillot L;Keenan K;Li W;Lin F;Patrone MV;Raraigh KS;Sun L;Zhou YH;O'Neal WK;Sontag MK;Levy H;Durie PR;Rommens JM;Drumm ML;Wright FA;Strug LJ;Cutting GR;Knowles MR
通讯作者：
Knowles MR
DOI：
10.1126/science.aal4043
发表时间：
2018-03-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bastarache L;Hughey JJ;Hebbring S;Marlo J;Zhao W;Ho WT;Van Driest SL;McGregor TL;Mosley JD;Wells QS;Temple M;Ramirez AH;Carroll R;Osterman T;Edwards T;Ruderfer D;Velez Edwards DR;Hamid R;Cogan J;Glazer A;Wei WQ;Feng Q;Brilliant M;Zhao ZJ;Cox NJ;Roden DM;Denny JC
通讯作者：
Denny JC
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen