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Estimating the additive genetic variance for relative fitness from changes in allele frequency. 从等位基因频率的变化估计相对适合度的加性遗传变异。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf232
Manas Geeta Arun, Aidan Angus-Henry, Darren J Obbard, Jarrod D Hadfield

The rate of adaptation is equal to the additive genetic variance for relative fitness (VA) in the population. Estimating VA typically involves obtaining suitable measures of fitness on a large number of individuals with known pairwise relatedness. Such data are hard to collect and the results are often sensitive to the definition of fitness used. Here, we present a new method for estimating VA that does not involve making measurements of fitness on individuals, but instead tracks changes in the genetic composition of the population. First, we show that VA can readily be expressed as a function of the genome-wide diversity/linkage disequilibrium matrix and genome-wide expected change in allele frequency due to selection. We then show how independent experimental replicates can be used to infer the expected change in allele frequency due to selection and then estimate VA via a linear mixed model. Finally, using individual-based simulations, we demonstrate that our approach yields precise and accurate estimates over a range of biologically plausible scenarios.

适应率等于群体中相对适合度(VA)的加性遗传方差。估计VA通常涉及对已知的成对亲缘关系的大量个体获得合适的适应度度量。这样的数据很难收集,而且结果往往对所使用的适应度定义很敏感。在这里,我们提出了一种估算VA的新方法,该方法不涉及测量个体的适合度,而是跟踪种群遗传组成的变化。首先,我们表明,VA可以很容易地表达为全基因组多样性/连锁不平衡矩阵和全基因组因选择而导致的等位基因频率预期变化的函数。然后,我们展示了如何使用独立的实验重复来推断由于选择而导致的等位基因频率的预期变化,然后通过线性混合模型估计VA。最后,使用基于个体的模拟,我们证明了我们的方法对一系列生物学上合理的情景产生了精确和准确的估计。
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引用次数: 0
Sex-specific evolutionary programs shape recombination rate evolution in house mice. 性别特异性进化程序塑造了家鼠的重组率进化。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf251
Lydia K Wooldridge, Micah Pietraho, Peyton DiSiena, Sam Littman, Benjamin Clauss, Beth L Dumont

Recombination rates vary across species, populations, and sexes. House mice (Mus musculus) present a particularly extreme example. Prior studies have established large differences in global recombination rates between M. musculus subspecies and inbred strains, with males exhibiting more extensive variation than females. The observation of sex-limited variation has prompted the hypothesis that male and female recombination rates may evolve by distinct evolutionary mechanisms in M. musculus. Here, we formally evaluate this hypothesis in a phylogenetic framework. We combine cytogenetic estimates of genomic crossover counts with published data to compile a large dataset of sex-specific crossover rate estimates totaling >6,000 single meiotic cells from 31 genetically diverse inbred mouse strains representing five Mus species and four M. musculus subspecies. We show that the phylogenetic distribution of male recombination rates is well predicted by the underlying Mus phylogeny (phylogenetic heritability, HP2 = 0.82), contrasting with the weaker phylogenetic signal observed in females (HP2 = 0.24). M. m. musculus males exhibit a marked increase in recombination rate compared to males from other M. musculus subspecies, prompting us to test explicit models of lineage-specific evolution. We uncover evidence for an adaptive increase in male recombination rate along the M. m. musculus subspecies lineage but find no support for a parallel increase in females. Taken together, our findings confirm the hypothesis that recombination rate evolution in house mice is governed by distinct sex-specific evolutionary regimes and motivate future efforts to ascertain the sex-specific selective pressures and sex-specific genetic architectures that underlie these observations.

重组率因物种、种群和性别而异。家鼠(小家鼠)是一个特别极端的例子。先前的研究已经确定,在肌肉支原体亚种和近交系之间,全球重组率存在很大差异,雄性比雌性表现出更广泛的变化。性别限制变异的观察提示了一个假设,即雄性和雌性的重组率可能通过不同的进化机制进化。在这里,我们在系统发育框架中正式评估这一假设。我们将基因组交叉计数的细胞遗传学估计与已发表的数据结合起来,编制了一个性别特异性交叉率估计的大型数据集,总计来自31个遗传多样化的近交小鼠品系(代表5个小家鼠种和4个小家鼠亚种)的bbb6000个单个减数分裂细胞。研究结果表明,雄鼠重组率的系统发育分布可以通过系统发育遗传力(HP2= 0.82)很好地预测,而雌鼠的系统发育信号较弱(HP2=0.24)。与其他肌肉支原体亚种的雄性支原体相比,肌肉支原体雄性支原体的重组率显著增加,这促使我们对谱系特异性进化的明确模型进行测试。我们发现了雄性重组率在m.m.a musus亚种谱系中适应性增加的证据,但没有发现雌性重组率平行增加的证据。综上所述,我们的发现证实了一个假设,即家鼠的重组率进化是由不同的性别特异性进化机制控制的,并激发了未来的努力,以确定这些观察结果背后的性别特异性选择压力和性别特异性遗传结构。
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引用次数: 0
Dimensionality reduction of genetic data using contrastive learning. 使用对比学习的遗传数据降维。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf068
Filip Thor, Carl Nettelblad

We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create principal component analysis (PCA)-like population visualizations. Contrastive learning is a self-supervised deep learning method that uses similarities between samples to train the neural network to discriminate between samples. Many of the advances in these types of models have been made for computer vision, but some common methodology does not translate well from image to genetic data. We define a loss function that outperforms loss functions commonly used in contrastive learning, and a data augmentation scheme tailored specifically towards SNP genotype datasets. We compare the performance of our method to PCA and contemporary nonlinear methods with respect to how well they preserve local and global structure, and how well they generalize to new data. Our method displays good preservation of global structure and has improved generalization properties over t-distributed stochastic neighbor embedding, Uniform Manifold Approximation and Projection, and popvae, while preserving relative distances between individuals to a high extent. A strength of the deep learning framework is the possibility of projecting new samples and fine-tuning to new datasets using a pretrained model without access to the original training data, and the ability to incorporate more domain-specific information in the model. We show examples of population classification on two datasets of dog and human genotypes.

我们引入了一个框架,用于在遗传数据集上使用对比学习进行降维,以创建类似pca的种群可视化。对比学习是一种自我监督的深度学习方法,它利用样本之间的相似性来训练神经网络来区分样本。这类模型的许多进展都是在计算机视觉方面取得的,但是一些常用的方法不能很好地从图像转换到基因数据。我们定义了一个优于对比学习中常用的损失函数的损失函数,以及一个专门针对SNP基因型数据集量身定制的数据增强方案。我们将我们的方法与PCA和当代非线性方法的性能进行了比较,包括它们如何很好地保留局部和全局结构,以及它们如何很好地推广到新数据。我们的方法对全局结构有很好的保存,在t-SNE、UMAP和popvae的泛化性能上有提高,同时在很大程度上保留了个体之间的相对距离。深度学习框架的一个优势在于,它可以在不访问原始训练数据的情况下,使用预训练模型来预测新的样本和微调到新的数据集,并且能够在模型中加入更多特定领域的信息。我们在狗和人类基因型的两个数据集上展示了种群分类的例子。
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引用次数: 0
Clade distillation for genome-wide association studies. 进化精馏用于全基因组关联研究。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf158
Ryan Christ, Xinxin Wang, Louis J M Aslett, David Steinsaltz, Ira Hall

Testing inferred haplotype genealogies for association with phenotypes has been a longstanding goal in human genetics given their potential to detect association signals driven by allelic heterogeneity-when multiple causal variants modulate a phenotype-in both coding and noncoding regions. Recent scalable methods for inferring locus-specific genealogical trees along the genome, or representations thereof, have made substantial progress towards this goal; however, the problem of testing these trees for association with phenotypes has remained unsolved due to the growth in the number of clades with increasing sample size. To address this issue, we introduce several practical improvements to the kalis ancestry inference engine, including a general optimal checkpointing algorithm for decoding hidden Markov models, thereby enabling efficient genome-wide analyses. We then propose LOCATER, a powerful new procedure based on the recently proposed Stable Distillation framework, to test local tree representations for trait association. Although LOCATER is demonstrated here in conjunction with kalis, it may be used for testing output from any ancestry inference engine, regardless of whether such engines return discrete tree structures, relatedness matrices, or some combination of the two at each locus. Using simulated quantitative phenotypes, our results indicate that LOCATER achieves substantial power gains over traditional single marker testing, ARG-Needle, and window-based testing in cases of allelic heterogeneity, while also improving causal region localization. These findings suggest that genealogy-based association testing will be a fruitful approach for gene discovery, especially for signals driven by multiple ultra-rare variants.

检测推断的单倍型家谱与表型的关联一直是人类遗传学的长期目标,因为它们有潜力检测由等位基因异质性驱动的关联信号——当多个因果变异调节表型时——在编码区和非编码区。最近用于推断基因座特异性谱系树的可扩展方法,或其表示,已经朝着这一目标取得了实质性进展;然而,由于随着样本量的增加,进化枝数量的增加,测试这些树与表型关联的问题仍未解决。为了解决这个问题,我们对kalis祖先推理引擎进行了一些实际的改进,包括用于解码隐马尔可夫模型的通用最优检查点算法,从而实现了高效的全基因组分析。然后,我们提出了LOCATER,一个基于最近提出的稳定蒸馏框架的强大的新过程,用于测试特征关联的局部树表示。尽管LOCATER在这里是与kalis一起演示的,但它可以用于测试来自任何祖先推理引擎的输出,而不管这些引擎是否返回离散树结构、相关性矩阵,或者在每个位点返回两者的某种组合。通过模拟定量表型,我们的研究结果表明,在等位基因异质性的情况下,LOCATER比传统的单标记测试、ARG-Needle和基于窗口的测试取得了显著的优势,同时也改善了因果区域定位。这些发现表明,基于家谱的关联检测将是一种卓有成效的基因发现方法,特别是对于由多个超罕见变异驱动的信号。
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引用次数: 0
Maintenance of polymorphism in spatially heterogeneous environments. 空间异构环境中多态性的维护。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf229
Takahiro Sakamoto, Sam Yeaman

Local adaptation occurs when species adapt to spatially heterogeneous environments. The stability of local adaptation is determined by migration-selection-drift balance: selection favors adaptive divergence whereas migration and random genetic drift cause the collapse of divergence. The evolutionary dynamics of this balance have been extensively studied, but most previous theories used models with simple population structure and environmental variation, precluding their applicability to complex situations in nature. To address this issue, we developed a new theoretical method to analyze complex multi-population models, allowing heterogeneity in selection, migration, and population density. In essence, our method approximates a complex spatial model with a panmictic one-population model while retaining the core stochastic structure, enabling the application of conventional diffusion methods. By comparing with simulations, we confirmed that our method accurately describes stochastic evolutionary dynamics in various spatial models when migration is sufficiently high. This method is then applied to examine the effect of the pattern of environmental variation in 2D space. Assuming landscapes with different levels of the spatial autocorrelation of the environment, we found that the maintenance of locally adaptive alleles is significantly promoted when the spatial autocorrelation is high. These results highlight how complex spatial heterogeneity, as seen in nature, could affect the qualitative outcome of evolution.

局部适应发生在物种适应空间异质环境的时候。局部适应的稳定性是由迁移-选择-漂平衡决定的:选择有利于适应性分化,而迁移和随机遗传漂则导致分化的崩溃。这种平衡的进化动力学已经得到了广泛的研究,但大多数先前的理论使用的模型都是简单的种群结构和环境变化,这使得它们无法适用于自然界的复杂情况。为了解决这个问题,我们开发了一种新的理论方法来分析复杂的多种群模型,允许选择、迁移和种群密度的异质性。从本质上讲,我们的方法在保留核心随机结构的同时,近似于一个具有泛种群模型的复杂空间模型,从而使传统扩散方法的应用成为可能。通过与模拟结果的比较,我们证实了当迁移量足够大时,我们的方法准确地描述了各种空间模型中的随机进化动力学。然后将该方法应用于检查二维空间中环境变化模式的影响。假设环境空间自相关程度不同的景观,我们发现当空间自相关程度高时,局部自适应等位基因的维持显著促进。这些结果突出了复杂的空间异质性,正如在自然界中看到的那样,可以影响进化的定性结果。
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引用次数: 0
Predicting hybrid fitness: the effects of ploidy and complex ancestry. 预测杂交适应性:倍性和复杂祖先的影响。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf242
Hilde Schneemann, John J Welch

Hybridization between divergent populations places alleles in novel genomic contexts. This can inject adaptive variation-which is useful for breeders and conservationists-or reduce fitness, leading to reproductive isolation. Most theoretical work on hybrids involves haploid or diploid hybrids between two parental lineages, but real-world hybridization is often more complex. We introduce a simple fitness landscape model to predict hybrid fitness with arbitrary ploidy and an arbitrary number of hybridizing lineages. We test our model on published data from maize (Zea mays) and rye (Secale cereale), including hybrids between multiple inbred lines, both as diploids and synthetic tetraploids. Quantitative predictions for the effects of inbreeding, and the strength of progressive heterosis, are well supported. Results suggest that the model captures the important properties of dosage and genetic interactions, and may help to unify theories of heterosis and reproductive isolation.

不同种群之间的杂交将等位基因置于新的基因组环境中。这可以注入适应性变异——这对育种者和保护主义者很有用——或者降低适应性,导致生殖隔离。大多数关于杂交的理论工作涉及两个亲本谱系之间的单倍体或二倍体杂交,但现实世界的杂交通常更复杂。我们引入了一个简单的适应度景观模型来预测具有任意倍性和任意数量杂交谱系的杂交适应度。我们用已发表的玉米(Zea mays)和黑麦(Secale cereale)的数据来测试我们的模型,包括多个自交系之间的杂交种,包括二倍体和合成四倍体。对近交效应和进行性杂种优势强度的定量预测得到了很好的支持。结果表明,该模型捕获了剂量和遗传相互作用的重要特性,可能有助于统一杂种优势和生殖隔离理论。
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引用次数: 0
Statistical analysis of correlated expression data from high throughput experiments. 高通量实验相关表达数据的统计分析。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf060
Peng Wang, Pengfei Lyu, Shyamal Peddada, Hongyuan Cao

Data obtained from high throughput experiments often exhibit complex dependencies among features. These dependencies arise from various sources, including genetic correlation, batch effects, technical replicates, and shared biological pathways. Ignoring these dependencies can lead to inflated false discovery rate (FDR), reduced statistical power, and biased biological interpretations. Properly accounting for these dependencies is crucial for accurate detection of biological signals. We propose a new method called Analysis of Correlated Expressions (ACE) to compare the mean expression of features between two groups. ACE is based on a factor analytic model that accounts for dependence among features and also incorporates heterogeneity of variances between groups, a common feature of high throughput data. Furthermore, ACE does not require the data to be normally distributed. It is scalable and free of any unknown tuning parameters. Extensive simulation studies indicate that it is more powerful than many existing methods while controlling the FDR. Application of ACE to a microRNA dataset, a neuroblastoma gene expression dataset, and a Huntington's disease dataset resulted in some novel findings that were missed by existing methods.

从高通量实验中获得的数据往往表现出特征之间复杂的依赖关系。这些依赖性来自各种来源,包括遗传相关性、批效应、技术复制和共享的生物途径。忽略这些依赖关系可能会导致错误发现率(FDR)的膨胀,统计能力的降低,以及有偏见的生物学解释。正确考虑这些依赖关系对于准确检测生物信号至关重要。我们提出了一种新的方法,称为相关表达式分析(ACE)来比较两组之间特征的平均表达。ACE基于因子分析模型,该模型考虑了特征之间的依赖性,并结合了组间差异的异质性,这是高通量数据的共同特征。此外,ACE不要求数据是正态分布。它是可伸缩的,没有任何未知的调优参数。大量的仿真研究表明,在控制FDR时,它比许多现有的方法更强大。ACE应用于microRNA数据集、神经母细胞瘤基因表达数据集和亨廷顿舞蹈病数据集,得到了一些现有方法所遗漏的新发现。
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引用次数: 0
Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning. 通过无监督和可解释的表征学习从基因组序列推断真菌顺式调控网络。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf209
Alan M Moses, Jason E Stajich, Audrey P Gasch, David A Knowles

Gene expression patterns are determined to a large extent by transcription factor (TF) binding to noncoding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because nonfunctional matches to the sequence patterns (motifs) recognized by TFs occur frequently throughout the genome. Large-scale functional genomics data for many TFs has enabled characterization of regulatory networks in experimentally accessible cells such as budding yeast. Beyond yeast, fungi are important industrial organisms and pathogens, but large-scale functional data is only sporadically available. Uncharacterized regulatory networks control key pathways and gene expression programs associated with fungal phenotypes. Here, we explore a sequence-only approach to inferring regulatory networks by leveraging the 100s of genomes now available for many clades of fungi. We use gene orthology as the learning signal to infer interpretable, TF motif-based representations of noncoding regulatory regions. Using these representations to identify conserved signals for motifs, comparative genomics can be scaled to evolutionary comparisons where sequence similarity cannot be detected. We show that similarity of these conserved motif signals predicts gene expression and regulation better than using experimental data, and that we can infer known and novel regulatory connections in diverse fungi. Our new predictions include a pathway for recombination in Candida albicans and pathways for mating and an RNAi immune response in Neurospora. Taken together, our results indicate that specific hypotheses about transcriptional regulation in fungi can be obtained for many genes from genome sequence analysis alone.

基因表达模式在很大程度上取决于转录因子与基因组中非编码调控区域的结合。然而,基因表达还不能从基因组序列中系统地预测,部分原因是与转录因子(TFs)识别的序列模式(基序)的非功能性匹配在整个基因组中经常发生。许多tf的大规模功能基因组学数据使实验可获得的细胞(如出芽酵母)的调节网络特性成为可能。除了酵母,真菌也是重要的工业生物和病原体,但大规模的功能数据只是零星的。未表征的调控网络控制与真菌表型相关的关键途径和基因表达程序。在这里,我们探索了一种仅限序列的方法,通过利用目前可用于许多真菌分支的100个基因组来推断调控网络。我们使用基因同源学作为学习信号来推断非编码调控区域可解释的、基于TF基序的表征。使用这些表征来识别基序的保守信号,比较基因组学可以扩展到无法检测序列相似性的进化比较。我们发现这些保守基序信号的相似性比使用实验数据更好地预测基因表达和调控,并且我们可以推断出不同真菌中已知的和新的调控联系。我们的新预测包括白色念珠菌的重组途径和神经孢子菌的交配途径和RNAi免疫反应。综上所述,我们的研究结果表明,仅通过基因组序列分析就可以获得真菌中许多基因转录调控的特定假设。
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引用次数: 0
Chimeric reference panels for genomic imputation. 基因组代入的嵌合参考板。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf212
Meikun Zhou, Maddie E James, Jan Engelstädter, Daniel Ortiz-Barrientos

Despite transformative advances in genomic technologies, missing data remain a fundamental constraint that limits the full potential of genomic research across biological systems. Genotype imputation offers a remedy by inferring unobserved genotypes from observed data. However, conventional imputation methods typically rely on external reference panels constructed from complete genome sequences of hundreds of individuals, a costly approach largely inaccessible for nonmodel organisms. Moreover, these methods generally overlook novel genomic positions not captured in existing panels. To overcome these limitations, we developed Retriever, a method for constructing a chimeric reference panel that enables genotype imputation without the need for an external reference panel. Retriever constructs a chimeric reference panel directly from the target samples using a sliding window approach to identify and retrieve genomic partitions with complete data. By exploiting the complementary distribution of missing data across samples, Retriever assembles a panel that preserves local patterns of linkage disequilibrium and captures novel variants. When the Retriever-constructed panels are used with Beagle for genotype imputation, Retriever consistently achieves accuracy exceeding 95% across diverse datasets, including plants, animals, and fungi. By eliminating the need for costly external panels, Retriever provides an accessible and cost-effective solution that broadens the application of genomic analyses across various species.

尽管基因组技术取得了变革性的进步,但数据缺失仍然是限制跨生物系统基因组研究充分发挥潜力的根本制约因素。基因型插入通过从观察数据推断未观察到的基因型提供了一种补救措施。然而,传统的归算方法通常依赖于由数百个个体的完整基因组序列构建的外部参考面板,这是一种昂贵的方法,非模式生物基本上无法使用。此外,这些方法通常忽略了在现有面板中未捕获的新基因组位置。为了克服这些限制,我们开发了retriver,这是一种构建嵌合参考面板的方法,无需外部参考面板即可进行基因型插入。检索者使用滑动窗口方法直接从目标样本中构建嵌合参考面板,以识别和检索具有完整数据的基因组分区。通过利用样本中缺失数据的互补分布,retriver组装了一个面板,该面板保留了链接不平衡的局部模式并捕获了新的变体。当猎犬构建的面板与Beagle一起用于基因型插入时,猎犬在不同的数据集(包括植物、动物和真菌)中始终达到超过95%的准确性。通过消除对昂贵的外部面板的需要,retriver提供了一种可访问且具有成本效益的解决方案,扩大了跨物种基因组分析的应用。
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引用次数: 0
Impact of erroneous marker data on the accuracy of narrow-sense heritability. 错误标记资料对狭义遗传力准确性的影响。
IF 5.1 3区 生物学 Q2 GENETICS & HEREDITY Pub Date : 2026-01-07 DOI: 10.1093/genetics/iyaf230
Christi Sagariya, Václav Bittner, Torsten Pook, Amit Roy, Milan Lstibůrek

Genomic relationship matrices computed from single nucleotide polymorphism (SNP) data are now widely used to estimate narrow-sense heritability (h2), yet the impact of genotyping error on these estimates is not well understood. We used stochastic simulation and supporting algebra to examine this impact and its interplay with marker density. Starting from a diploid founder population with 300 additive quantitative trait loci, we simulated SNP panels with densities ranging from 6.25 to 50 SNPs per cM and traits with true h2 of either 0.2 or 0.6. Genotypes were then altered at error rates ε=0-1 under three error kernels. For each of 100 simulation replicates, we calculated the genomic relationship matrix using VanRaden's method and estimated h2 with restricted maximum likelihood (REML). In the absence of error, low-density marker panels underestimated h2. Sparse panels were also the most tolerant up to ε≈0.1 yet still underestimated h2. Conversely, the densest panel recovered the true h2 when ε=0, but even a small error ε>0.01 caused an upward bias. The analysis reveals that all distortions are attributable to: (i) a shift in the mean off-diagonal elements of the genomic relationship matrix with magnitude (1-ε)2 and (ii) a change in the ratio between the mean diagonal and mean off-diagonal elements of the genomic relationship matrix. When ε≳0.6, every kernel pushed h2 toward zero. Thus, even modest genotyping error can inflate or deflate additive genetic variance estimates. SNP panels therefore require rigorous laboratory quality control, error-aware imputation, and statistical models that account for genotype uncertainty when estimating h2.

从单核苷酸多态性(SNP)数据计算的基因组关系矩阵现在广泛用于估计狭义遗传力(h2),但基因分型误差对这些估计的影响尚未得到很好的理解。我们使用随机模拟和支持代数来检验这种影响及其与标记密度的相互作用。从具有300个可加性数量性状位点的二倍体创始群体开始,我们模拟了密度为每厘米6.25至50个SNP,真h2为0.2或0.6的SNP面板。然后在三个错误核下以错误率ε = 0-1改变基因型。对于每100个模拟重复,我们使用VanRaden的方法计算基因组关系矩阵,并使用REML估计h2。在没有误差的情况下,低密度标记板低估了h2。当ε≈0.1时,稀疏板的耐受性最强,但h2仍被低估。相反,当ε = 0时,密度最大的面板恢复了真实的h2,但即使是很小的误差ε > 0.01也会导致向上偏差。分析表明,所有畸变都是由于:(i)基因组关系矩阵的平均非对角线元素以(1-ε2)的幅度发生了变化,(ii)基因组关系矩阵的平均对角线元素和平均非对角线元素之间的比率发生了变化。当ε > 0.6时,每个核都使h2趋近于零。因此,即使是适度的基因分型误差也会使加性遗传方差估计膨胀或缩小。因此,SNP面板需要严格的实验室质量控制,误差感知的输入,以及在估计h2时考虑基因型不确定性的统计模型。
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引用次数: 0
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