tooluniverse-polygenic-risk-score

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Polygenic Risk Score (PRS) Builder

多基因风险评分（PRS）构建工具

Build and interpret polygenic risk scores for complex diseases using genome-wide association study (GWAS) data.

利用全基因组关联研究（GWAS）数据构建并解读复杂疾病的多基因风险评分。

Overview

概述

Use Cases:

"Calculate my genetic risk for type 2 diabetes"
"Build a polygenic risk score for coronary artery disease"
"What's my genetic predisposition to Alzheimer's disease?"
"Interpret my PRS percentile for breast cancer risk"

What This Skill Does:

Extracts genome-wide significant variants (p < 5e-8) from GWAS Catalog
Builds weighted PRS models using effect sizes (beta coefficients)
Calculates individual risk scores from genotype data
Interprets PRS as population percentiles and risk categories

What This Skill Does NOT Do:

Diagnose disease (PRS is probabilistic, not deterministic)
Replace clinical assessment or genetic counseling
Account for non-genetic factors (lifestyle, environment)
Provide treatment recommendations

适用场景：

"计算我患2型糖尿病的遗传风险"
"构建冠心病的多基因风险评分"
"我患阿尔茨海默病的遗传易感性如何？"
"解读我乳腺癌风险的PRS百分位数"

本工具功能：

从GWAS Catalog中提取全基因组显著变异（p < 5e-8）
利用效应量（β系数）构建加权PRS模型
根据基因型数据计算个体风险评分
将PRS解读为人群百分位数和风险类别

本工具不具备的功能：

诊断疾病（PRS是概率性的，而非确定性的）
替代临床评估或遗传咨询
考虑非遗传因素（生活方式、环境）
提供治疗建议

Methodology

方法学

PRS Calculation Formula

PRS计算公式

A polygenic risk score is calculated as a weighted sum across genetic variants:

PRS = Σ (dosage_i × effect_size_i)

Where:

dosage_i: Number of effect alleles at SNP i (0, 1, or 2)
effect_size_i: Beta coefficient or log(odds ratio) from GWAS

多基因风险评分通过对多个遗传变异的加权求和计算得出：

PRS = Σ (dosage_i × effect_size_i)

其中：

dosage_i：SNP i上的效应等位基因数量（0、1或2）
effect_size_i：来自GWAS的β系数或对数优势比

Standardization

标准化

Raw PRS is standardized to z-scores for interpretation:

z-score = (PRS - population_mean) / population_std

This allows comparison to population distribution and percentile calculation.

原始PRS会被标准化为z分数以便解读：

z-score = (PRS - population_mean) / population_std

这使得评分可与人群分布进行比较并计算百分位数。

Significance Thresholds

显著性阈值

Genome-wide significance: p < 5×10⁻⁸ (default threshold)
This corrects for ~1 million independent tests across the genome
Relaxed thresholds (e.g., p < 1×10⁻⁵) can include more SNPs but may add noise

全基因组显著性：p < 5×10⁻⁸（默认阈值）
该阈值针对全基因组约100万个独立检验进行了校正
宽松阈值（如p < 1×10⁻⁵）可纳入更多变异，但可能引入噪声

Effect Size Handling

效应量处理

Continuous traits (e.g., height, BMI): Beta coefficient (units of trait per allele)
Binary traits (e.g., disease): Odds ratio converted to log-odds (beta = ln(OR))
Missing effect sizes or non-significant SNPs are excluded

连续性状（如身高、BMI）：使用β系数（每等位基因对应的性状变化单位）
二元性状（如疾病）：将优势比转换为对数优势比（β = ln(OR)）
缺失效应量或不显著的SNP会被排除

Data Sources

数据来源

This skill uses ToolUniverse GWAS tools to query:

GWAS Catalog (EMBL-EBI)
- Curated GWAS associations
- 5000+ studies, millions of variants
- Tools:
```
gwas_get_associations_for_trait
```
  ,
```
gwas_get_snp_by_id
```
Open Targets Genetics
- Integrated genetics platform
- Fine-mapped credible sets
- Tools:
```
OpenTargets_search_gwas_studies_by_disease
```
  ,
```
OpenTargets_get_variant_info
```

本工具使用ToolUniverse GWAS工具查询以下数据源：

GWAS Catalog（EMBL-EBI）
- 经过整理的GWAS关联数据
- 包含5000+项研究、数百万个变异
- 使用工具：
```
gwas_get_associations_for_trait
```
  ,
```
gwas_get_snp_by_id
```
Open Targets Genetics
- 集成遗传学平台
- 包含精细定位的可信变异集
- 使用工具：
```
OpenTargets_search_gwas_studies_by_disease
```
  ,
```
OpenTargets_get_variant_info
```

Key Concepts

核心概念

Polygenic Risk Scores (PRS)

多基因风险评分（PRS）

Polygenic risk scores aggregate the effects of many genetic variants to estimate an individual's genetic predisposition to a trait or disease. Unlike Mendelian diseases caused by single mutations, complex diseases involve hundreds to thousands of variants, each with small effects.

Key Properties:

Continuous distribution: PRS forms a bell curve in populations
Relative risk: Compares individual to population average
Probabilistic: High PRS doesn't guarantee disease, low PRS doesn't guarantee protection
Ancestry-specific: PRS accuracy depends on matching GWAS and target ancestry

多基因风险评分汇总了多个遗传变异的效应，用于估计个体对某一性状或疾病的遗传易感性。与由单个突变引起的孟德尔疾病不同，复杂疾病涉及数百至数千个变异，每个变异的效应都很小。

核心特性：

连续分布：PRS在人群中呈钟形曲线分布
相对风险：将个体与人群平均水平进行比较
概率性：高PRS不代表一定会患病，低PRS也不代表一定不会患病
祖先特异性：PRS的准确性取决于GWAS数据与目标人群的祖先匹配度

GWAS (Genome-Wide Association Studies)

GWAS（全基因组关联研究）

GWAS compare allele frequencies between cases and controls (or correlate with trait values) across millions of SNPs to identify disease-associated variants.

Study Design:

Discovery cohort: Initial identification of associations
Replication cohort: Validation in independent samples
Sample size: Larger studies detect smaller effects (power ∝ √N)
Multiple testing correction: Bonferroni-type correction for ~1M tests

GWAS通过比较病例组与对照组的等位基因频率（或与性状值的相关性），在数百万个SNP中识别与疾病相关的变异。

研究设计：

发现队列：初步识别关联关系
验证队列：在独立样本中验证关联
样本量：样本量越大，越能检测到更小的效应（功效 ∝ √N）
多重检验校正：针对约100万个检验进行Bonferroni型校正

Effect Sizes and Odds Ratios

效应量与优势比

Beta (β): Change in trait per copy of effect allele
- Example: β = 0.5 kg/m² means each allele increases BMI by 0.5 units
Odds Ratio (OR): Multiplicative change in disease odds
- OR = 1.5 means 50% increased odds per allele
- Convert to beta: β = ln(OR)

β系数：每拷贝效应等位基因对应的性状变化
- 示例：β = 0.5 kg/m²表示每个等位基因会使BMI增加0.5个单位
优势比（OR）：疾病发生几率的倍数变化
- OR = 1.5表示每拷贝等位基因使患病几率增加50%
- 转换为β系数：β = ln(OR)

Linkage Disequilibrium (LD) and Clumping

连锁不平衡（LD）与聚类

Nearby variants are often inherited together (LD). To avoid double-counting:

LD clumping: Select independent variants (r² < 0.1 within 1 Mb windows)
Fine-mapping: Statistical methods to identify causal variants
This skill uses raw associations; production PRS should include LD pruning

邻近变异通常会一起遗传（LD）。为避免重复计算：

LD聚类：选择独立变异（1Mb窗口内r² < 0.1）
精细定位：识别因果变异的统计方法
本工具使用原始关联数据；生产环境中的PRS应包含LD修剪步骤

Population Stratification

人群分层

GWAS and PRS are most accurate when ancestries match:

Population structure: Different ancestries have different allele frequencies
Transferability: European-trained PRS perform worse in non-European populations
Solution: Train PRS on diverse cohorts or use ancestry-matched references

GWAS和PRS在祖先匹配时准确性最高：

人群结构：不同祖先的等位基因频率存在差异
可转移性：基于欧洲人群训练的PRS在非欧洲人群中的表现较差
解决方案：在多样化队列上训练PRS，或使用匹配祖先的参考数据

Applications

应用场景

Clinical Risk Assessment

临床风险评估

PRS can stratify individuals for:

Screening programs: Target high-risk individuals (e.g., mammography, colonoscopy)
Prevention strategies: Lifestyle interventions for high genetic risk
Drug response: Pharmacogenomics based on metabolism genes

Example: Khera et al. (2018) showed PRS identifies 3× more individuals at >3-fold coronary artery disease risk than monogenic mutations.

PRS可用于对个体进行分层：

筛查项目：针对高风险个体（如乳腺X线摄影、结肠镜检查）
预防策略：对高遗传风险个体进行生活方式干预
药物反应：基于代谢基因的药物基因组学

示例：Khera等人（2018年）的研究表明，PRS识别出的冠心病高风险个体数量是单基因突变的3倍以上。

Research Applications

研究应用

Gene discovery: PRS-based phenome-wide association studies (PheWAS)
Genetic correlation: Compare PRS across traits
Causal inference: Mendelian randomization using PRS as instruments
Simulation studies: Model polygenic architecture

基因发现：基于PRS的表型组全关联研究（PheWAS）
遗传相关性：比较不同性状的PRS
因果推断：将PRS作为工具变量进行孟德尔随机化分析
模拟研究：建模多基因架构

Personal Genomics

个人基因组学

Consumer genetic testing (23andMe, Ancestry DNA) provides raw genotypes. Users can:

Calculate PRS for traits not reported
Compare to published PRS models
Understand genetic contribution vs. lifestyle factors

Caution: Personal PRS should not replace medical advice. Results may cause anxiety if not properly contextualized.

消费级基因检测（如23andMe、Ancestry DNA）可提供原始基因型数据。用户可以：

计算未报告性状的PRS
与已发表的PRS模型进行比较
了解遗传因素与生活方式因素的贡献

注意：个人PRS不能替代医疗建议。若未得到恰当解读，结果可能引发焦虑。

Limitations and Considerations

局限性与注意事项

Scientific Limitations

科学局限性

Heritability Gap: PRS explains a fraction of genetic heritability
- Type 2 diabetes: ~50% heritable, PRS explains ~10-20%
- Rare variants, epistasis, and gene-environment interactions not captured
Ancestry Bias: Most GWAS are European ancestry
- PRS accuracy drops in non-European populations
- Need for diverse cohort recruitment
Winner's Curse: Discovery effect sizes often overestimated
- Replication studies show smaller effects
- Meta-analyses provide better estimates
Missing Heritability: Unexplained genetic contribution from:
- Rare variants not captured by SNP arrays
- Structural variants (CNVs, inversions)
- Epigenetic factors

遗传力缺口：PRS仅能解释部分遗传力
- 2型糖尿病：约50%的遗传力，PRS仅能解释10-20%
- 未涵盖罕见变异、上位性及基因-环境交互作用
祖先偏差：大多数GWAS基于欧洲人群
- PRS在非欧洲人群中的准确性下降
- 需要招募多样化队列
胜者偏差：发现阶段的效应量往往被高估
- 验证研究显示效应量更小
- 荟萃分析能提供更准确的估计
缺失的遗传力：未解释的遗传贡献来自：
- SNP芯片未捕获的罕见变异
- 结构变异（CNV、倒位）
- 表观遗传因素

Clinical Limitations

临床局限性

Not Diagnostic: PRS is probabilistic, not deterministic
- High PRS doesn't mean you will get disease
- Low PRS doesn't mean you won't get disease
Environmental Factors: Many complex diseases are 50%+ environmental
- Smoking, diet, exercise, stress, pollution
- PRS doesn't account for these
Pleiotropy: Same variants affect multiple traits
- Genetic correlation between diseases
- Risk for one may protect against another
Actionability: Not all high-risk predictions have interventions
- Alzheimer's PRS has limited actionability currently
- Ethical considerations for testing

非诊断性：PRS是概率性的，而非确定性的
- 高PRS不代表一定会患病
- 低PRS不代表一定不会患病
环境因素：许多复杂疾病的50%+风险来自环境
- 吸烟、饮食、运动、压力、污染
- PRS未考虑这些因素
多效性：同一变异可能影响多种性状
- 疾病之间存在遗传相关性
- 对一种疾病的风险可能对另一种疾病有保护作用
可操作性：并非所有高风险预测都有对应的干预措施
- 目前阿尔茨海默病PRS的可操作性有限
- 检测存在伦理考量

Ethical Considerations

伦理考量

Privacy: Genetic data is identifiable and permanent
- Can't be changed like passwords
- Familial implications (relatives share genetics)
Discrimination: Potential for genetic discrimination
- GINA protects against health/employment discrimination (US)
- Life insurance and long-term care not protected
Psychological Impact: Knowledge of high risk can cause anxiety
- Need for genetic counseling
- Risk communication training
Equity: Ancestry bias means unequal benefits
- Europeans benefit most from current PRS
- Exacerbates health disparities

隐私：遗传数据具有可识别性且永久不变
- 无法像密码一样更改
- 对家族有影响（亲属共享遗传信息）
歧视：存在遗传歧视的潜在风险
- 美国GINA法案禁止健康和就业领域的遗传歧视
- 人寿保险和长期护理不受保护
心理影响：知晓高风险可能引发焦虑
- 需要遗传咨询
- 风险沟通培训
公平性：祖先偏差导致获益不均
- 欧洲人群从当前PRS中获益最多
- 加剧健康差异

References

参考文献

Key Publications

核心出版物

Lambert et al. (2021): "The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation"
- PGS Catalog: https://www.pgscatalog.org/
- Repository of published PRS models
Khera et al. (2018): "Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations"
- Nature Genetics, 50:1219–1224
- Demonstrated clinical utility of PRS
Torkamani et al. (2018): "The personal and clinical utility of polygenic risk scores"
- Nature Reviews Genetics, 19:581–590
- Comprehensive review of PRS applications
Martin et al. (2019): "Clinical use of current polygenic risk scores may exacerbate health disparities"
- Nature Genetics, 51:584–591
- Addresses ancestry bias and equity concerns
Choi et al. (2020): "Tutorial: a guide to performing polygenic risk score analyses"
- Nature Protocols, 15:2759–2772
- Practical guide to PRS calculation and evaluation

Lambert等人（2021）：《The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation》
- PGS Catalog：https://www.pgscatalog.org/
- 已发表PRS模型的存储库
Khera等人（2018）：《Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations》
- 《Nature Genetics》，50:1219–1224
- 证明了PRS的临床实用性
Torkamani等人（2018）：《The personal and clinical utility of polygenic risk scores》
- 《Nature Reviews Genetics》，19:581–590
- PRS应用的全面综述
Martin等人（2019）：《Clinical use of current polygenic risk scores may exacerbate health disparities》
- 《Nature Genetics》，51:584–591
- 探讨了祖先偏差和公平性问题
Choi等人（2020）：《Tutorial: a guide to performing polygenic risk score analyses》
- 《Nature Protocols》，15:2759–2772
- PRS计算与评估的实用指南

Resources

资源

PGS Catalog: https://www.pgscatalog.org/ - Published PRS models
LD Hub: http://ldsc.broadinstitute.org/ - Genetic correlations
PRSice: https://www.prsice.info/ - PRS calculation software
GWAS Catalog: https://www.ebi.ac.uk/gwas/ - Association database

PGS Catalog：https://www.pgscatalog.org/ - 已发表的PRS模型
LD Hub：http://ldsc.broadinstitute.org/ - 遗传相关性分析工具
PRSice：https://www.prsice.info/ - PRS计算软件
GWAS Catalog：https://www.ebi.ac.uk/gwas/ - 关联数据库

Workflow

工作流程

1. Trait Selection

1. 性状选择

Identify the disease or trait of interest:

Use standard terminology (e.g., "type 2 diabetes" not "T2D")
Check GWAS Catalog for availability
Verify sufficient GWAS studies exist (n > 10,000 samples ideal)

确定感兴趣的疾病或性状：

使用标准术语（如“2型糖尿病”而非“T2D”）
检查GWAS Catalog中的可用性
验证是否存在足够的GWAS研究（理想样本量n > 10,000）

2. Association Collection

2. 关联数据收集

Query GWAS databases for genome-wide significant associations:

python

prs = build_polygenic_risk_score(
    trait="coronary artery disease",
    p_threshold=5e-8,  # Genome-wide significance
    max_snps=1000
)

Considerations:

P-value threshold: 5e-8 is conservative, 1e-5 includes more variants
LD clumping: Production systems should prune correlated SNPs
Study quality: Prefer large meta-analyses over small studies

查询GWAS数据库获取全基因组显著关联数据：

python

prs = build_polygenic_risk_score(
    trait="coronary artery disease",
    p_threshold=5e-8,  # Genome-wide significance
    max_snps=1000
)

注意事项：

p值阈值：5e-8较为保守，1e-5可纳入更多变异
LD聚类：生产系统应对相关SNP进行修剪
研究质量：优先选择大型荟萃分析而非小型研究

3. Effect Size Extraction

3. 效应量提取

Extract beta coefficients or odds ratios:

Beta for continuous traits (direct use)
OR for binary traits (convert to log-odds)
Handle missing values (exclude or impute from meta-analysis)

提取β系数或优势比：

连续性状直接使用β系数
二元性状将优势比转换为对数优势比
处理缺失值（排除或从荟萃分析中插补）

4. SNP Filtering

4. SNP过滤

Quality control filters:

MAF filter: Exclude rare variants (MAF < 0.01) for robustness
Genotype QC: Remove SNPs with high missingness (> 10%)
Hardy-Weinberg: Exclude SNPs violating HWE (p < 1e-6)
Ambiguous SNPs: Remove A/T and G/C SNPs (strand ambiguity)

质量控制过滤：

MAF过滤：排除罕见变异（MAF < 0.01）以保证稳健性
基因型QC：去除缺失率高（> 10%）的SNP
哈迪-温伯格平衡：排除违反HWE的SNP（p < 1e-6）
模糊SNP：去除A/T和G/C型SNP（链模糊性）

5. Score Calculation

5. 评分计算

Calculate weighted sum of genotype dosages:

python

result = calculate_personal_prs(
    prs_weights=prs,
    genotypes=my_genotypes,
    population_mean=0.0,
    population_std=1.0
)

Genotype Sources:

23andMe raw data export
Ancestry DNA raw data
Whole genome sequencing (VCF files)
SNP array data (Illumina, Affymetrix)

计算基因型剂量的加权和：

python

result = calculate_personal_prs(
    prs_weights=prs,
    genotypes=my_genotypes,
    population_mean=0.0,
    population_std=1.0
)

基因型来源：

23andMe原始数据导出
Ancestry DNA原始数据
全基因组测序（VCF文件）
SNP芯片数据（Illumina、Affymetrix）

6. Risk Interpretation

6. 风险解读

Convert to percentiles and risk categories:

python

result = interpret_prs_percentile(result)
print(f"Percentile: {result.percentile:.1f}%")
print(f"Risk: {result.risk_category}")

Risk Categories:

Low risk: < 20th percentile (genetic protection)
Average risk: 20-80th percentile (typical genetic predisposition)
Elevated risk: 80-95th percentile (moderately increased risk)
High risk: > 95th percentile (substantially increased risk)

Clinical Interpretation:

Percentiles assume normal distribution
Relative risk vs. average (not absolute risk)
Combine with family history, clinical risk factors
PRS is NOT diagnostic - many high-risk individuals never develop disease

转换为百分位数和风险类别：

python

result = interpret_prs_percentile(result)
print(f"Percentile: {result.percentile:.1f}%")
print(f"Risk: {result.risk_category}")

风险类别：

低风险：< 20百分位数（遗传保护）
平均风险：20-80百分位数（典型遗传易感性）
升高风险：80-95百分位数（中度风险增加）
高风险：> 95百分位数（显著风险增加）

临床解读：

百分位数假设评分呈正态分布
相对风险是与人群平均水平相比，而非绝对风险
需结合家族史、临床风险因素
PRS并非诊断工具——许多高风险个体从未患病

Best Practices

最佳实践

PRS Construction

PRS构建

Use validated PRS from PGS Catalog when available
- Published models have been externally validated
- Include LD clumping and ancestry-specific weights
Match ancestries between GWAS and target population
- European GWAS for European individuals
- Use multi-ancestry GWAS when available
Include as many SNPs as practical
- More SNPs = better prediction (up to a point)
- Balance between coverage and genotyping cost
Consider trait architecture
- Highly polygenic traits (height, education): benefit from relaxed thresholds
- Oligogenic traits (IBD, T1D): few large-effect variants, strict thresholds

优先使用PGS Catalog中经过验证的PRS
- 已发表的模型经过外部验证
- 包含LD聚类和祖先特异性权重
匹配GWAS与目标人群的祖先
- 欧洲人群GWAS用于欧洲个体
- 尽可能使用多祖先GWAS
纳入尽可能多的SNP
- 更多SNP通常意味着更好的预测效果（达到一定程度后趋于稳定）
- 在覆盖范围和基因分型成本之间取得平衡
考虑性状架构
- 高度多基因性状（身高、教育程度）：受益于宽松阈值
- 寡基因性状（IBD、T1D）：存在少量大效应变异，需使用严格阈值

Clinical Use

临床应用

Combine with clinical risk scores
- Add PRS to Framingham Risk Score, QRISK, etc.
- Integrated models improve prediction
Stratify screening and prevention
- Intensify surveillance for high PRS (e.g., earlier mammography)
- Lifestyle interventions for modifiable risk
Provide genetic counseling
- Explain probabilistic nature of PRS
- Discuss limitations and uncertainty
- Address psychological impact
Consider actionability
- Is there an intervention for high risk?
- Benefits vs. harms of knowing genetic risk

与临床风险评分结合使用
- 将PRS添加到Framingham风险评分、QRISK等工具中
- 集成模型可提升预测能力
分层筛查与预防
- 对高PRS个体加强监测（如更早进行乳腺X线摄影）
- 对可改变风险因素进行生活方式干预
提供遗传咨询
- 解释PRS的概率性质
- 讨论局限性和不确定性
- 应对心理影响
考虑可操作性
- 针对高风险是否有干预措施？
- 权衡知晓遗传风险的利弊

Research Use

研究应用

Report methods transparently
- Document SNP selection criteria
- Report LD clumping parameters
- Specify ancestry of GWAS and target
Validate in held-out cohorts
- Split data: training vs. testing
- Report out-of-sample prediction accuracy (R², AUC)
Compare to existing PRS
- Benchmark against PGS Catalog models
- Report incremental improvement
Test across ancestries
- Evaluate transferability to non-European populations
- Report performance stratified by ancestry

透明报告方法
- 记录SNP选择标准
- 报告LD聚类参数
- 明确GWAS和目标人群的祖先
在独立队列中验证
- 拆分数据：训练集 vs 测试集
- 报告样本外预测准确性（R²、AUC）
与现有PRS进行比较
- 与PGS Catalog中的模型进行基准测试
- 报告增量改进
跨祖先测试
- 评估在非欧洲人群中的可转移性
- 按祖先分层报告性能

Disclaimer

免责声明

This skill is for educational and research purposes only.

Not for clinical diagnosis or treatment decisions
Not validated for clinical use - use PGS Catalog models for clinical-grade PRS
Requires genetic counseling - interpretation requires expertise
Does not account for family history, environment, or lifestyle factors
Ancestry-specific - accuracy depends on matching GWAS ancestry

For clinical genetic testing, consult:

Genetic counselors (certified by ABGC/ABMGG)
Medical geneticists
Healthcare providers with genomics training

PRS is a rapidly evolving field. Guidelines and best practices will continue to change as research progresses.

Regulatory Status:

FDA does not currently regulate PRS (as of 2024)
Some countries restrict direct-to-consumer genetic risk reporting
Check local regulations before clinical implementation

本工具仅用于教育和研究目的。

不用于临床诊断或治疗决策
未经过临床使用验证 - 临床级PRS请使用PGS Catalog中的模型
需要遗传咨询 - 解读需专业知识
未考虑家族史、环境或生活方式因素
具有祖先特异性 - 准确性取决于GWAS与目标人群的祖先匹配度

如需临床基因检测，请咨询：

遗传咨询师（由ABGC/ABMGG认证）
医学遗传学家
具备基因组学培训的医疗保健提供者

PRS是一个快速发展的领域。随着研究进展，指南和最佳实践将不断更新。

监管状态：

截至2024年，FDA尚未对PRS进行监管
部分国家限制直接面向消费者的遗传风险报告
临床实施前请查阅当地法规