Exact Decoding of a Sequentially Markov Coalescent Model in Genetics

溯祖理论 频数推理 计算机科学 推论 马尔可夫链 隐马尔可夫模型 马尔可夫模型 Python(编程语言) 群体遗传学 算法 贝叶斯概率 人口 贝叶斯推理 机器学习 人工智能 生物 遗传学 人口学 社会学 基因 系统发育树 操作系统
作者
Caleb Ki,Jonathan Terhorst
标识
DOI:10.1080/01621459.2023.2252570
摘要

AbstractIn statistical genetics, the sequentially Markov coalescent (SMC) is an important family of models for approximating the distribution of genetic variation data under complex evolutionary models. Methods based on SMC are widely used in genetics and evolutionary biology, with significant applications to genotype phasing and imputation, recombination rate estimation, and inferring population history. SMC allows for likelihood-based inference using hidden Markov models (HMMs), where the latent variable represents a genealogy. Because genealogies are continuous, while HMMs are discrete, SMC requires discretizing the space of trees in a way that is awkward and creates bias. In this work, we propose a method that circumvents this requirement, enabling SMC-based inference to be performed in the natural setting of a continuous state space. We derive fast, exact procedures for frequentist and Bayesian inference using SMC. Compared to existing methods, ours requires minimal user intervention or parameter tuning, no numerical optimization or E-M, and is faster and more accurate. Supplementary materials for this article are available online.Keywords: ChangepointCoalescentHidden Markov modelPopulation genetics Supplementary MaterialsIn the supplement we present supporting lemmas, proofs of the theorems, and additional plots and tables. (pdf)Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementAll of the data analyzed in this article are either simulated, or publicly available. A Python package implementing our method is available at https://terhorst.github.io/xsmc. Code which reproduces all of the figures and tables in this article is available at https://terhorst.github.io/xsmc/paper.Additional informationFundingThis research was supported by the National Science Foundation (grant number DMS-2052653, and a Graduate Research Fellowship), and the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM151145. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
七七完成签到 ,获得积分10
刚刚
Loooong发布了新的文献求助10
1秒前
内向音响完成签到,获得积分10
1秒前
2秒前
zmj完成签到,获得积分10
2秒前
无昵称发布了新的文献求助10
2秒前
3秒前
ma完成签到,获得积分10
3秒前
QQ完成签到,获得积分10
4秒前
zyz发布了新的文献求助10
4秒前
Kriten发布了新的文献求助10
4秒前
打打应助我想@科研采纳,获得10
4秒前
jhih完成签到,获得积分10
5秒前
DokiDoki完成签到,获得积分10
5秒前
cmy关闭了cmy文献求助
5秒前
llllhh完成签到,获得积分10
6秒前
6秒前
非泥完成签到,获得积分10
6秒前
oywt关注了科研通微信公众号
7秒前
一一一发布了新的文献求助10
7秒前
7秒前
7秒前
YANG完成签到 ,获得积分10
8秒前
星际圈完成签到,获得积分10
9秒前
10秒前
Chu_JH完成签到,获得积分10
10秒前
10秒前
10秒前
10秒前
Hyacinth完成签到,获得积分10
10秒前
爆米花应助wst采纳,获得10
10秒前
传奇3应助沉默的宛筠采纳,获得10
10秒前
凪白完成签到,获得积分10
10秒前
taoatao发布了新的文献求助10
11秒前
坚果发布了新的文献求助10
11秒前
11秒前
漫漫完成签到,获得积分10
12秒前
12秒前
13秒前
cfghjj发布了新的文献求助10
13秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970683
求助须知:如何正确求助?哪些是违规求助? 3515337
关于积分的说明 11178055
捐赠科研通 3250580
什么是DOI,文献DOI怎么找? 1795357
邀请新用户注册赠送积分活动 875790
科研通“疑难数据库(出版商)”最低求助积分说明 805166