Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.
Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.
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DOI：
10.1126/science.abm7993
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发表时间：
2023-04-28
期刊：
影响因子：
56.9
通讯作者：
Pfenning, Andreas R.
中科院分区：
文献类型：
--
作者：
Kaplow, Irene M.;Lawler, Alyssa J.;Schaffer, Daniel E.;Srinivasan, Chaitanya;Sestili, Heather H.;Wirthlin, Morgan E.;Phan, BaDoi N.;Prasad, Kavya;Brown, Ashley R.;Zhang, Xiaomeng;Foley, Kathleen;Genereux, Diane P.;Karlsson, Elinor K.;Lindblad-Toh, Kerstin;Meyer, Wynn K.;Pfenning, Andreas R.
在哺乳动物的历史中，不同的表型，包括相对于体型而言较大的大脑、群体生活和声音学习能力，已经进化了多次。这些共有的表型可能通过基因组比较可辨别的共同机制反复出现。蛋白质编码序列的差异未能完全解释多种哺乳动物表型的进化。这表明这些表型至少部分是通过基因表达的变化而进化的，这意味着它们在物种之间的差异可能是由控制特定组织和细胞类型中基因表达的增强子区域的基因组序列差异引起的。然而参与表型进化的增强子在很大程度上还是未知的。基于序列保守性的方法来识别此类增强子是有限的，因为即使序列中的单个核苷酸保守性很差，增强子活性也可以保守。这是因为在绝大多数情况下，核苷酸以高速率翻转，但转录因子结合位点和其他序列特征的类似组合可以在数百万年的进化过程中得以维持，从而使得增强子的功能在特定细胞类型或组织中得以保守。目前通过实验测量数十个物种的直向同源增强子的功能是不可行的，但新的机器学习方法使得对特定组织和细胞类型中跨物种的增强子功能进行可靠的基于序列的预测成为可能。为了克服研究单个核苷酸的限制，我们开发了组织感知保守推理工具包（TACIT）。 TACIT 不是测量单个核苷酸在某个区域的保守程度，而是使用机器学习来测试基因组给定部分的功能是否可能保守。更具体地说，卷积神经网络使用仅从少数物种中识别出的候选增强子来学习将基因组序列与增强子活性连接起来的组织或细胞类型特异性调节代码。这种方法使我们能够准确地将物种之间组织或细胞类型特异性增强子活性的差异与增强子直向同源物的基因组序列差异联系起来。然后，我们将这些增强子功能的预测与数百种哺乳动物的表型联系起来，以解释物种的系统发育相关性。我们应用 TACIT 从 222 只哺乳动物的运动皮层和小清蛋白神经元开放染色质数据中识别出候选增强子，这些数据与大脑相对于身体大小、独居生活和发声学习的大小相关。我们的结果包括鉴定出多个与大脑尺寸相对于身体尺寸相关的候选增强子，其中一些增强子位于与人类小头畸形或大头畸形相关的蛋白质编码突变相关基因的线性或三维邻近位置。我们还确定了与分离焦虑相关基因附近的独居进化相关的候选增强子，以及与声音学习能力进化相关的其他增强子。我们获得了大块运动皮层和小白蛋白神经元的独特结果，证明了将 TACIT 应用到大块组织和特定少数细胞类型群体的价值。为了便于将来对 TACIT 的结果和应用进行分析，我们发布了 222 种哺乳动物中每一种中超过 400,000 个候选增强子的预测增强子活性及其与我们研究的表型的关联。 TACIT 利用预测的增强子活性保守性而不是核苷酸水平保守性，将物种之间的基因序列差异与大量哺乳动物的表型联系起来。 TACIT 可应用于任何表型，增强子活性数据可从相关组织或细胞类型中的至少几个物种获得，并且全基因组比对可在数十个具有显着表型变异的物种中获得。虽然我们为转录增强子开发了 TACIT，但它也可以应用于涉及基因调控其他组件的基因组区域，例如启动子、剪接增强子和沉默子。随着测序基因组数量的增加，TACIT 等机器学习方法有可能帮助理解微妙的基因组模式的保存或变化如何帮助解释表型进化。组织感知保护推理工具包 (TACIT) 将物种之间的遗传差异与表型联系起来。 TACIT 的工作原理是，从与表型相关的组织中的几个物种生成开放染色质数据，使用开放和封闭染色质区域的序列来训练机器学习模型，用于预测组织特异性开放染色质，并将数十种哺乳动物的开放染色质预测与表型相关联。 [物种剪影来自 PhyloPic] 物种之间的蛋白质编码差异通常无法解释表型多样性，这表明调节基因表达的基因组元件（例如增强子）的参与。识别增强子和表型之间的关联具有挑战性，因为尽管序列保守性较低，但增强子活性可能是组织依赖性的并且功能保守。我们开发了组织感知保护推理工具包（TACIT），利用在特定组织上训练的机器学习模型的预测，将候选增强子与物种表型相关联。应用 TACIT 将运动皮层和小清蛋白阳性中间神经元增强子与神经表型相关联，揭示了数十种增强子-表型关联，包括与小头畸形或大头畸形相关基因相互作用的大脑大小相关增强子。 TACIT 为识别与具有对齐基因组的任何大型物种中任何趋同进化表型的进化相关的增强子提供了基础。
Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic] Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species’ phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer–phenotype associations, including brain size–associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.
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影响因子：
4.7
作者：
Adachi M;Monteggia LM
通讯作者：
Monteggia LM
DOI：
10.1126/science.abl8189
发表时间：
2023-04-28
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
通讯作者：
--
影响因子：
25
作者：
Chen, Yi-Chuan;Kuo, Hsiao-Ying;Bornschein, Ulrich;Takahashi, Hiroshi;Chen, Shih-Yun;Lu, Kuan-Ming;Yang, Hao-Yu;Chen, Gui-May;Lin, Jing-Ruei;Lee, Yi-Hsin;Chou, Yun-Chia;Cheng, Sin-Jhong;Chien, Cheng-Ting;Enard, Wolfgang;Hevers, Wulf;Paeaebo, Svante;Graybiel, Ann M.;Liu, Fu-Chin
通讯作者：
Liu, Fu-Chin
影响因子：
14.9
作者：
Frankish A;Diekhans M;Ferreira AM;Johnson R;Jungreis I;Loveland J;Mudge JM;Sisu C;Wright J;Armstrong J;Barnes I;Berry A;Bignell A;Carbonell Sala S;Chrast J;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Hunt T;Izuogu OG;Lagarde J;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Xu J;Yates A;Zerbino D;Zhang Y;Aken B;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Reymond A;Tress ML;Flicek P
通讯作者：
Flicek P
影响因子：
64.8
作者：
Armstrong J;Hickey G;Diekhans M;Fiddes IT;Novak AM;Deran A;Fang Q;Xie D;Feng S;Stiller J;Genereux D;Johnson J;Marinescu VD;Alföldi J;Harris RS;Lindblad-Toh K;Haussler D;Karlsson E;Jarvis ED;Zhang G;Paten B
通讯作者：
Paten B