Back to Details

tooluniverse-polygenic-risk-score

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

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:
  1. GWAS Catalog (EMBL-EBI)
    • Curated GWAS associations
    • 5000+ studies, millions of variants
    • Tools: 
      gwas_get_associations_for_trait
      , 
      gwas_get_snp_by_id
  2. 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：
  1. 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
  2. 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

  1. 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
  2. Ancestry Bias: Most GWAS are European ancestry
    • PRS accuracy drops in non-European populations
    • Need for diverse cohort recruitment
  3. Winner's Curse: Discovery effect sizes often overestimated
    • Replication studies show smaller effects
    • Meta-analyses provide better estimates
  4. Missing Heritability: Unexplained genetic contribution from:
    • Rare variants not captured by SNP arrays
    • Structural variants (CNVs, inversions)
    • Epigenetic factors
  1. 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
  2. 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
  3. 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
  4. 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

  1. 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
  2. Environmental Factors: Many complex diseases are 50%+ environmental
    • Smoking, diet, exercise, stress, pollution
    • PRS doesn't account for these
  3. Pleiotropy: Same variants affect multiple traits
    • Genetic correlation between diseases
    • Risk for one may protect against another
  4. Actionability: Not all high-risk predictions have interventions
    • Alzheimer's PRS has limited actionability currently
    • Ethical considerations for testing
  1. 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
  2. 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
  3. 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
  4. 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

  1. Privacy: Genetic data is identifiable and permanent
    • Can't be changed like passwords
    • Familial implications (relatives share genetics)
  2. Discrimination: Potential for genetic discrimination
    • GINA protects against health/employment discrimination (US)
    • Life insurance and long-term care not protected
  3. Psychological Impact: Knowledge of high risk can cause anxiety
    • Need for genetic counseling
    • Risk communication training
  4. Equity: Ancestry bias means unequal benefits
    • Europeans benefit most from current PRS
    • Exacerbates health disparities
  1. 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）
  2. 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
  3. 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
  4. 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

  1. Lambert et al. (2021): "The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation"
  2. 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
  3. Torkamani et al. (2018): "The personal and clinical utility of polygenic risk scores"
    • Nature Reviews Genetics, 19:581–590
    • Comprehensive review of PRS applications
  4. 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
  5. 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
  1. Lambert等人（2021）：《The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation》
  2. 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
  3. 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
  4. 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
  5. 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

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

  1. Use validated PRS from PGS Catalog when available
    • Published models have been externally validated
    • Include LD clumping and ancestry-specific weights
  2. Match ancestries between GWAS and target population
    • European GWAS for European individuals
    • Use multi-ancestry GWAS when available
  3. Include as many SNPs as practical
    • More SNPs = better prediction (up to a point)
    • Balance between coverage and genotyping cost
  4. Consider trait architecture
    • Highly polygenic traits (height, education): benefit from relaxed thresholds
    • Oligogenic traits (IBD, T1D): few large-effect variants, strict thresholds
  1. 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
  2. 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
  3. 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
  4. 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

  1. Combine with clinical risk scores
    • Add PRS to Framingham Risk Score, QRISK, etc.
    • Integrated models improve prediction
  2. Stratify screening and prevention
    • Intensify surveillance for high PRS (e.g., earlier mammography)
    • Lifestyle interventions for modifiable risk
  3. Provide genetic counseling
    • Explain probabilistic nature of PRS
    • Discuss limitations and uncertainty
    • Address psychological impact
  4. Consider actionability
    • Is there an intervention for high risk?
    • Benefits vs. harms of knowing genetic risk
  1. 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
  2. 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
  3. 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
  4. 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

  1. Report methods transparently
    • Document SNP selection criteria
    • Report LD clumping parameters
    • Specify ancestry of GWAS and target
  2. Validate in held-out cohorts
    • Split data: training vs. testing
    • Report out-of-sample prediction accuracy (R², AUC)
  3. Compare to existing PRS
    • Benchmark against PGS Catalog models
    • Report incremental improvement
  4. Test across ancestries
    • Evaluate transferability to non-European populations
    • Report performance stratified by ancestry
  1. 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
  2. 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）
  3. 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
  4. 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