This paper outlines guidelines for investigating the causality of sequence variants in human disease. The authors emphasize the need for rigorous standards to distinguish disease-causing variants from other potentially functional variants. They propose a framework for assessing variant pathogenicity, integrating gene-level and variant-level evidence. The paper discusses challenges in interpreting sequence variants, particularly in rare and complex diseases, and highlights the importance of robust statistical methods and experimental validation. The authors recommend that genome-wide analyses of rare variants for both Mendelian and complex disorders should use formal statistical significance to evaluate the strength of evidence. They stress the importance of distinguishing between true and false positive findings, especially in the context of clinical diagnostics. The paper also addresses the need for improved public databases of genetic variants, better incentives for sharing genetic and phenotypic data, and the development of standardized statistical approaches for assessing causality. Key challenges include the high rate of false positives in disease-mutation databases, the difficulty in interpreting rare variants, and the need for accurate assessment of variant penetrance. The authors emphasize the importance of integrating genetic, informatic, and experimental evidence to support variant causality. They also highlight the need for large-scale studies to confirm the pathogenicity of variants and to reduce biases in assessing penetrance and phenotypic heterogeneity. The paper concludes that high-throughput sequencing technologies offer unprecedented opportunities to discover new genes and variants underlying human disease, but these discoveries must be rigorously validated to prevent the proliferation of false-positive findings. The authors call for the development of standardized, quantitative statistical approaches to objectively assign probability of causation to new candidate disease genes and variants. They also emphasize the importance of sharing sequence and phenotype data to maximize the chances of correctly identifying disease-causing genetic variants.This paper outlines guidelines for investigating the causality of sequence variants in human disease. The authors emphasize the need for rigorous standards to distinguish disease-causing variants from other potentially functional variants. They propose a framework for assessing variant pathogenicity, integrating gene-level and variant-level evidence. The paper discusses challenges in interpreting sequence variants, particularly in rare and complex diseases, and highlights the importance of robust statistical methods and experimental validation. The authors recommend that genome-wide analyses of rare variants for both Mendelian and complex disorders should use formal statistical significance to evaluate the strength of evidence. They stress the importance of distinguishing between true and false positive findings, especially in the context of clinical diagnostics. The paper also addresses the need for improved public databases of genetic variants, better incentives for sharing genetic and phenotypic data, and the development of standardized statistical approaches for assessing causality. Key challenges include the high rate of false positives in disease-mutation databases, the difficulty in interpreting rare variants, and the need for accurate assessment of variant penetrance. The authors emphasize the importance of integrating genetic, informatic, and experimental evidence to support variant causality. They also highlight the need for large-scale studies to confirm the pathogenicity of variants and to reduce biases in assessing penetrance and phenotypic heterogeneity. The paper concludes that high-throughput sequencing technologies offer unprecedented opportunities to discover new genes and variants underlying human disease, but these discoveries must be rigorously validated to prevent the proliferation of false-positive findings. The authors call for the development of standardized, quantitative statistical approaches to objectively assign probability of causation to new candidate disease genes and variants. They also emphasize the importance of sharing sequence and phenotype data to maximize the chances of correctly identifying disease-causing genetic variants.