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Genetic mapping provides a powerful approach to identify genes and biological processes underlying any trait influenced by inheritance, including human diseases. We discuss the intellectual foundations of genetic mapping of Mendelian and complex traits in humans, examine lessons emerging from linkage analysis of Mendelian diseases and genome-wide association studies of common diseases, and discuss questions and challenges that lie ahead.

Key Points Existing preclinical models have a limited ability to test 'therapeutic hypotheses'; that is, whether perturbing a target in a given manner would benefit patients and have minimal toxicity. 'Experiments of nature', including human genetics, provide an estimate of dose–response curves at the time of target validation. There is an increasing number of studies in the literature demonstrating that genes with a series of disease-associated alleles represent promising drug targets. Here, we provide objective criteria to help prioritize research on the most promising targets and ultimately nominate a gene product as the target for a drug development programme. We highlight important limitations of human genetics in target validation, including a commentary on the genetic architecture of common diseases. We also discuss the role of genome-wide association studies (GWASs) and large-scale sequencing projects in drug discovery, emphasizing the importance of precompetitive collaborations that make clinical and genetic data available in a responsible manner. Many clinical trial failures can be traced back to the limited predictive value of preclinical models of disease. Plenge and colleagues discuss how knowledge from human genetics, such as naturally occurring mutations in humans that affect the activity of particular proteins, can be used as a tool to more effectively prioritize molecular targets in drug development. More than 90% of the compounds that enter clinical trials fail to demonstrate sufficient safety and efficacy to gain regulatory approval. Most of this failure is due to the limited predictive value of preclinical models of disease, and our continued ignorance regarding the consequences of perturbing specific targets over long periods of time in humans. 'Experiments of nature' — naturally occurring mutations in humans that affect the activity of a particular protein target or targets — can be used to estimate the probable efficacy and toxicity of a drug targeting such proteins, as well as to establish causal rather than reactive relationships between targets and outcomes. Here, we describe the concept of dose–response curves derived from experiments of nature, with an emphasis on human genetics as a valuable tool to prioritize molecular targets in drug development. We discuss empirical examples of drug–gene pairs that support the role of human genetics in testing therapeutic hypotheses at the stage of target validation, provide objective criteria to prioritize genetic findings for future drug discovery efforts and highlight the limitations of a target validation approach that is anchored in human genetics.

The central goal of human genetics is to understand the inherited basis of human variation in phenotypes, elucidating human physiology, evolution and disease. Rare mutations have been found underlying two thousand mendelian diseases; more recently, it has become possible to assess systematically the contribution of common SNPs to complex disease. The known role of copy-number alterations in sporadic genomic disorders, combined with emerging information about inherited copy-number variation, indicate the importance of systematically assessing copy-number variants (CNVs), including common copy-number polymorphisms (CNPs), in disease. Here we discuss evidence that CNVs affect phenotypes, directions for basic knowledge to support clinical study of CNVs, the challenge of genotyping CNPs in clinical cohorts, the use of SNPs as markers for CNPs and statistical challenges in testing CNVs for association with disease. Critical needs are high-resolution maps of common CNPs and techniques that accurately determine the allelic state of affected individuals.

As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111 x in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of -313 genes per genome, and -95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Mark DePristo and colleagues report an analytical framework to discover and genotype variation using whole exome and genome resequencing data from next-generation sequencing technologies. They apply these methods to low-pass population sequencing data from the 1000 Genomes Project. Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

Resequencing of genes from individuals of European and African American ancestry indicates that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years, and that European Americans carry an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans. Recent genetic change in a human population As part of the NHLBI Exome Sequencing Project, the exomes of more than 6,500 individuals of European American and African American ancestry have been sequenced. Using these data, the authors estimate that about 73% of all protein-coding single nucleotide variants (SNVs) and 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years, a short span in evolutionary time that coincides with a period of accelerated population growth. Around 86% of changes predicted to be harmful arose within the same timeframe, with European Americans harbouring more harmful variants in essential and Mendelian disease genes than African Americans. The data suggest that the increased mutational capacity of recent human populations has influenced the burden of Mendelian disorders, but is also likely to promote beneficial genetic changes that will be selected in future generations to come. More practically, the results will be of use in prioritizing potential disease-causing variants in gene-mapping studies. Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history 1 , 2 and will help to facilitate the development of new approaches for disease-gene discovery 3 . Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth 4 , 5 , 6 , notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

Several single-nucleotide polymorphisms (SNPs) associated with lipid levels have been identified. In a cohort of 5414 study subjects, 11 such SNPs were tested and their relationship with lipid levels confirmed. A genotype score comprising nine of these SNPs was independently associated with incident cardiovascular disease, even after adjustment for baseline lipid levels. Eleven single-nucleotide polymorphisms (SNPs) were tested and their relationship with lipid levels confirmed. A genotype score comprising nine of these SNPs was independently associated with incident cardiovascular disease, even after adjustment for baseline lipid levels. Plasma levels of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol are associated with a future risk of cardiovascular disease. 1 , 2 It has been estimated that roughly 50% of variation in LDL and HDL cholesterol levels is heritable. 3 Several common DNA sequence variants have been related to blood LDL or HDL cholesterol levels. 4 – 11 In light of the increased practicality of conducting genomewide association studies, it is likely that additional polymorphisms associated with lipid levels will be identified. These observations suggest two hypotheses. First, a DNA sequence variant that is related to blood lipoprotein levels may influence the risk of . . .

Amit Majithia and colleagues employ a pooled assay in human macrophages to assess the functional effects of all possible missense variants in PPARG . Their study shows the value of saturation mutagenesis and prospective experimental characterization to support diagnostic interpretation of newly discovered missense variants in disease-related genes. Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty 1 , 2 . For example, mutations in PPARG cause Mendelian lipodystrophy 3 , 4 and increase risk of type 2 diabetes (T2D) 5 . Although approximately 1 in 500 people harbor missense variants in PPARG , most are of unknown consequence. To prospectively characterize PPARγ variants, we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single–amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning. When applied to 55 new missense variants identified in population-based and clinical sequencing, the classifier annotated 6 variants as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.

High plasma HDL cholesterol is associated with reduced risk of myocardial infarction, but whether this association is causal is unclear. Exploiting the fact that genotypes are randomly assigned at meiosis, are independent of non-genetic confounding, and are unmodified by disease processes, mendelian randomisation can be used to test the hypothesis that the association of a plasma biomarker with disease is causal. We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol. Carriers of the LIPG 396Ser allele (2·6% frequency) had higher HDL cholesterol (0·14 mmol/L higher, p=8×10−13) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with non-carriers. This difference in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84–0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88–1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58–0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68–1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterol associated with OR 1·54, 95% CI 1·45–1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69–2·69, p=2×10−10). Some genetic mechanisms that raise plasma HDL cholesterol do not seem to lower risk of myocardial infarction. These data challenge the concept that raising of plasma HDL cholesterol will uniformly translate into reductions in risk of myocardial infarction. US National Institutes of Health, The Wellcome Trust, European Union, British Heart Foundation, and the German Federal Ministry of Education and Research.

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.