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Purpose: VEGF receptor (VEGFR) kinases are important drug targets in oncology that affect function of systemic endothelial cells. To discover genetic markers that affect VEGFR inhibitor pharmacodynamics, we performed a genome-wide association study of serum soluble vascular VEGFR2 concentrations [sVEGFR2], a pharmacodynamic biomarker for VEGFR2 inhibitors.Experimental Design: We conducted a genome-wide association study (GWAS) of [sVEGFR2] in 736 healthy Old Order Amish volunteers. Gene variants identified from the GWAS were genotyped serially in a cohort of 128 patients with advanced solid tumor with baseline [sVEGFR2] measurements, and in 121 patients with renal carcinoma with [sVEGFR2] measured before and during pazopanib therapy.Results: rs34231037 (C482R) in KDR, the gene encoding sVEGFR2 was found to be highly associated with [sVEGFR2], explaining 23% of the variance (P = 2.7 × 10−37). Association of rs34231037 with [sVEGFR2] was replicated in 128 patients with cancer with comparable effect size (P = 0.025). Furthermore, rs34231037 was a significant predictor of changes in [sVEGFR2] in response to pazopanib (P = 0.01).Conclusion: Our findings suggest that genome-wide analysis of phenotypes in healthy populations can expedite identification of candidate pharmacogenetic markers. Genotyping for germline variants in KDR may have clinical utility in identifying patients with cancer with unusual sensitivity to effects of VEGFR2 kinase inhibitors. Clin Cancer Res; 21(2); 365–72. ©2014 AACR.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community., Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

While GWAS of common diseases has delivered thousands of novel genetic findings, prioritizing genes for translation to therapeutics has been challenging. Here, we propose an approach to resolve that issue by identifying genes that have both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). Bidirectionality is a desirable feature of the best targets because it implies both a causal role on the phenotype in one direction and that modulating the target activity might be safe and therapeutically beneficial in the other.We used height, a highly heritable trait and a model of complex diseases, to test our approach. Using 34,231 individuals with exome sequence data and height, we identified five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants significantly increased risk for idiopathic short stature (ISS) (OR=2.75, p= 3.99×10−8). These genes are key members of the only two pathways successfully targeted for short stature: Growth Hormone/Insulin-like growth factor 1 axis and C-type Natriuretic peptide (CNP) for Achondroplasia, a monogenic form of dwarfism. We assayed a subset of NPR2 mutations and identified those with elevated (GoF) and diminished (LoF) activity and found that a polygenic score for height modulates the penetrance of pathogenic variants. We also demonstrated that adding exogenous CNP (encoded by NPPC) rescues the NPR2 haploinsufficiency molecular phenotype in a CRISPR-engineered cell line, thus validating its potential therapeutic treatment for inherited forms of short stature. Finally, we found that these BEST targets increase the probability of success in clinical trials above and beyond targets with other genetic evidence. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

Support from human genetics increases the probability of success in drug development. However, few examples exist of successful genomically-driven drug repositioning. Given that a Mendelian form of severe enterocolitis is due to up-regulation of the interleukin-18 (IL18) signaling pathway, and pharmacologic inhibition of IL18 has been shown to reverse this enterocolitis, we undertook a Mendelian randomization study to test the causal effect of elevated IL18 levels on inflammatory bowel disease susceptibility (IBD) in 12,882 cases and 21,770 controls. Mendelian randomization is an established method to assess the role of biomarkers in disease etiology in a manner that minimizes confounding and prevents reverse causation. Using three SNPs that explained almost 7% of the variance in IL18 level, we found that each genetically predicted standard deviation increase in IL18 was associated with an increase in IBD susceptibility (odds ratio = 1.22, 95% CI = 1.11–1.34, P-value = 6 × 10−5). This association was further validated in 25,042 IBD cases and 34,915 controls (odds ratio = 1.13, 95% CI = 1.05–1.20). Recently, an anti-IL18 monoclonal antibody, which decreased free IL18 levels, was found to be safe, yet ineffective in a phase II trial for type 2 diabetes. Taken together, these genomic findings implicated IBD as an alternative indication for anti-IL18 therapy, which should be tested in randomized controlled trials.

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

The hope for precision medicine has long been on the drug discovery horizon, well before the Human Genome Project gave it promise at the turn of the 21st century. In oncology, the concept has finally been realized and is now firmly embedded in ongoing drug discovery programs, and with many recent therapies involving some level of patient/disease stratification, including some highly personalized treatments. In addition, several drugs for rare diseases have been recently approved or are in late-stage clinical development, and new delivery modalities in cell and gene therapy and oligonucleotide approaches are yielding exciting new medicines for rare diseases of unmet need. For common complex diseases, however, the GWAS-driven advances in annotation of the genetic architecture over the past decade have not led to a concomitant shift in refined treatments. Similarly, attempts to disentangle treatment responders from non-responders via genetic predictors in pharmacogenetics studies have not met their anticipated success. It is possible that common diseases are simply lagging behind due to the inherent time lag with drug discovery, but it is also possible that their inherent multifactorial nature and their etiological and clinical heterogeneity will prove more resistant to refined treatment paradigms. The emergence of population-based resources in electronic health records, coupled with the rapid expansion of mobile devices and digital health may help to refine the measurement of phenotypic outcomes to match the exquisite detail emerging at the molecular level.

This corrects the article DOI: 10.1038/nrd.2016.164.

Purpose Liver injury is a serious adverse event leading to permanent discontinuation of lapatinib in affected patients. This study aimed to validate previously associated major histocompatibility complex (MHC) variants as predictors of risk of liver injury by using a large, randomized, placebo-controlled trial of lapatinib in human epidermal growth factor receptor 2–positive, early-stage breast cancer (Tykerb Evaluation After Chemotherapy [TEACH]: Lapatinib Versus Placebo In Women With Early-Stage Breast Cancer). Patients and Methods The frequency of ALT elevation cases was compared among four MHC variants in 1,194 patients randomly assigned to lapatinib. Cumulative ALT elevation time courses during treatment were also compared between carriers and noncarriers of specified MHC variants. Results In lapatinib-treated patients, there was a significant difference in ALT case incidence between HLA carriers and noncarriers. The highly correlated alleles HLA-DRB1*07:01 and HLA-DQA1*02:01 (study frequency, 22.4%) were associated with ALT elevation (odds ratio, 14) between cases (n = 37) and controls (n = 1,071). These associations strengthened at higher ALT elevation thresholds and in Hy's Law cases. In lapatinib-treated patients, the overall risk for National Cancer Institute-Common Terminology Criteria for Adverse Events grade 3 ALT elevation (> 5× upper limit of normal) was 2.1%; HLA allele carriers had an increased risk of 7.7%; in noncarriers, risk was reduced to 0.5%, comparable to ALT elevation for all patients receiving placebo. The increase in ALT case incidence in the lapatinib arm showed no evidence of plateau during 1 year of lapatinib treatment. Conclusion These results validate HLA-DRB1*07:01 allele carriage as a predictor of increased risk of lapatinib-induced liver injury and implicate an immune pathology. The HLA association could support clinical management of patients experiencing hepatotoxicity during lapatinib treatment.

Supplemental Digital Content is available in the text., Objective Proteins involving absorption, distribution, metabolism, and excretion (ADME) play a critical role in drug pharmacokinetics. The type and frequency of genetic variation in the ADME genes differ among populations. The aim of this study was to systematically investigate common and rare ADME coding variation in diverse ethnic populations by exome sequencing. Materials and methods Data derived from commercial exome capture arrays and next-generation sequencing were used to characterize coding variation in 298 ADME genes in 251 Northeast Asians and 1181 individuals from the 1000 Genomes Project. Results Approximately 75% of the ADME coding sequence was captured at high quality across the joint samples harboring more than 8000 variants, with 49% of individuals carrying at least one ‘knockout’ allele. ADME genes carried 50% more nonsynonymous variation than non-ADME genes (P=8.2×10–13) and showed significantly greater levels of population differentiation (P=7.6×10–11). Out of the 2135 variants identified that were predicted to be deleterious, 633 were not on commercially available ADME or general-purpose genotyping arrays. Forty deleterious variants within important ADME genes, with frequencies of at least 2% in at least one population, were identified as candidates for future pharmacogenetic studies. Conclusion Exome sequencing was effective in accurately genotyping most ADME variants important for pharmacogenetic research, in addition to identifying rare or potentially de novo coding variants that may be clinically meaningful. Furthermore, as a class, ADME genes are more variable and less sensitive to purifying selection than non-ADME genes.

Introduction Fetal haemoglobin (Hb F) and fetal cell (FC) levels in adults show considerable variation and the heritability of FC levels has been estimated to be 89% in the healthy European population; measured genotype analyses have shown the trait to be influenced by several genetic factors. In addition to the β-globin gene complex on chromosome 11p, linkage analyses have reported loci affecting FC levels on chromosomes Xp22.2–p22.3 and 6q23. Methods We have genotyped a sample of approximately 300 unselected, same sex, dizygotic twin pairs from the St. Thomas’ UK Adult Twin Register for markers in these three regions and carried out a linkage analysis of FC levels. We also used a new method to simultaneously test for linkage and allelic association, and association caused by population stratification or admixture. Results There was no evidence for linkage of FC levels to chromosomes Xp22.2–p22.3 and 6q23. However, the β-globin cluster was shown to be significantly linked and to be responsible for approximately one-third of the additive genetic variance in FC levels in the sample. Conclusions The report represents the first application of this method to a sample of nonsimulated data and demonstrates the effectiveness of the approach. Combined linkage and association analysis of the β-globin complex showed a strong association between the XmnI-Gγ site and FC levels and that the observed association is not an artifact of recent population admixture or stratification.

The hope for precision medicine has long been on the drug discovery horizon, well before the Human Genome Project gave it promise at the turn of the 21st century. In oncology, the concept has finally been realized and is now firmly embedded in ongoing drug discovery programs, and with many recent therapies involving some level of patient/disease stratification, including some highly personalized treatments. In addition, several drugs for rare diseases have been recently approved or are in late-stage clinical development, and new delivery modalities in cell and gene therapy and oligonucleotide approaches are yielding exciting new medicines for rare diseases of unmet need. For common complex diseases, however, the GWAS-driven advances in annotation of the genetic architecture over the past decade have not led to a concomitant shift in refined treatments. Similarly, attempts to disentangle treatment responders from non-responders via genetic predictors in pharmacogenetics studies have not met their anticipated success. It is possible that common diseases are simply lagging behind due to the inherent time lag with drug discovery, but it is also possible that their inherent multifactorial nature and their etiological and clinical heterogeneity will prove more resistant to refined treatment paradigms. The emergence of population-based resources in electronic health records, coupled with the rapid expansion of mobile devices and digital health may help to refine the measurement of phenotypic outcomes to match the exquisite detail emerging at the molecular level.

A Deep Look Into Our Genes Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective by Casals and Bertranpetit ). Tennessen et al. (p. 64 , published online 17 May), looking at all of the protein-coding genes in the human genome, and Nelson et al. (p. 100 , published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health.

Genetic variation in LRRK2 predisposes to Parkinson disease (PD), which underpins its development as a therapeutic target. Here, we aimed to identify novel genotype-phenotype associations that might support developing LRRK2 therapies for other conditions. We sequenced the 51 exons of LRRK2 in cases comprising 12 common diseases (n = 9,582), and in 4,420 population controls. We identified 739 single nucleotide variants (SNVs), 62% of which were observed in only one person, including 316 novel exonic variants. We found evidence of purifying selection for the LRRK2 gene and a trend suggesting that this is more pronounced in the central (ROC-COR-kinase) core protein domains of LRRK2 than the flanking domains. Population genetic analyses revealed that LRRK2 is not especially polymorphic or differentiated in comparison to 201 other drug target genes. Amongst Europeans, we identified 17 carriers (0.13%) of pathogenic LRRK2 mutations that were not significantly enriched within any disease or in those reporting a family history of PD. Analysis of pathogenic mutations within Europe reveals that the p.Arg1628Pro (c4883G>C) mutation arose independently in Europe and Asia. Taken together, these findings demonstrate how targeted deep sequencing can help to reveal fundamental characteristics of clinically important loci.

Purpose Pazopanib, an oral angiogenesis inhibitor, is approved for the treatment of advanced renal cell carcinoma (RCC). Response to pazopanib monotherapy varies between patients, and no validated biomarkers predictive of treatment outcome have been identified. We tested the hypothesis that this variability is partially dependent on germline genetic variants that may affect pazopanib exposure or angiogenesis pathways. Patients and Methods Twenty-seven functional polymorphisms within 13 genes were evaluated in 397 patients with RCC. Genetic association with progression-free survival (PFS) and objective response rate (RR) was analyzed using the Cox proportional hazards model and proportional odds model, respectively. Results Three polymorphisms in IL8 and HIF1A and five polymorphisms in HIF1A, NR1I2, and VEGFA showed nominally significant association (P ≤ .05) with PFS and RR, respectively. Compared with the wild-type AA genotype (median PFS, 48 weeks), the IL8 2767TT variant genotype showed inferior PFS (27 weeks, P = .009). The HIF1A 1790AG genotype was associated with inferior PFS and reduced RR, compared with the wild-type GG genotype (median PFS, 20 v 44 weeks; P = .03; RR, 30% v 43%, P = .02). Reductions in RR were detected for the NR1I2 −25385TT genotype, compared with the wild-type CC genotype (37% v 50%, P = .03), and for the VEGFA −1498CC genotype compared with the TT genotypes (33% v 51%). Conclusion Germline variants in angiogenesis- and exposure-related genes may predict treatment response to pazopanib monotherapy in patients with RCC. If validated, these markers may explain why certain patients fail antiangiogenesis therapy and they may support the use of alternative strategies to circumvent this issue.

Background & Aims Pazopanib has demonstrated clinical benefit in patients with advanced renal cell carcinoma (RCC) and is generally well tolerated. However, transaminase elevations have commonly been observed. This 2-stage study sought to identify genetic determinants of alanine transaminase (ALT) elevations in pazopanib-treated white patients with RCC. Methods Data from two separate clinical studies were used to examine the association of genetic polymorphisms with maximum on-treatment ALT levels. Results Of 6852 polymorphisms in 282 candidate genes examined in an exploratory dataset of 115 patients, 92 polymorphisms in 40 genes were significantly associated with ALT elevation ( p 0.01). Two markers (rs2858996 and rs707889) in the HFE gene, which are not yet known to be associated with hemochromatosis, showed evidence for replication. Because of multiple comparisons, there was a 12% likelihood the replication occurred by chance. These two markers demonstrated strong linkage disequilibrium (r 2 =0.99). In the combined dataset, median (25–75th percentile) maximum ALT values were 1.2 (0.7–1.9), 1.1 (0.8–2.5), and 5.4 (1.9–7.6)×ULN for rs2858996 GG ( n =148), GT ( n =82), and TT ( n =1 2) genotypes, respectively. All 12 TT patients had a maximum ALT>ULN, and 8 (67%) had ALT⩾3×ULN. The odds ratio (95% CI) for ALT⩾3×ULN for TT genotype was 39.7 (2.2–703.7) compared with other genotypes. As a predictor of ALT⩾3×ULN, the TT genotype had a negative predictive value of 0.83 and positive predictive value of 0.67. No TT patients developed liver failure. Conclusions The rs2858996/rs707889 polymorphisms in the HFE gene may be associated with reversible ALT elevation in pazo-panib-treated patients with RCC.

Malaria is a devastating infection caused by protozoa of the genus Plasmodium. Drug resistance is widespread, no new chemical class of antimalarials has been introduced into clinical practice since 1996 and there is a recent rise of parasite strains with reduced sensitivity to the newest drugs. We screened nearly 2 million compounds in GlaxoSmithKline's chemical library for inhibitors of P. falciparum, of which 13,533 were confirmed to inhibit parasite growth by at least 80% at 2 microM concentration. More than 8,000 also showed potent activity against the multidrug resistant strain Dd2. Most (82%) compounds originate from internal company projects and are new to the malaria community. Analyses using historic assay data suggest several novel mechanisms of antimalarial action, such as inhibition of protein kinases and host-pathogen interaction related targets. Chemical structures and associated data are hereby made public to encourage additional drug lead identification efforts and further research into this disease.

Association studies can focus on candidate gene(s), a particular genomic region, or adopt a genome-wide association approach, each of which has implications for marker selection. The strategy for marker selection will affect the statistical power of the study to detect a disease association and is a crucial element of study design. The abundant single nucleotide polymorphisms (SNPs) are the markers of choice in genetic case-control association studies. The genotypes of neighboring SNPs are often highly correlated ('in linkage disequilibrium', LD) within a population, which is utilized for selecting specific 'tagSNPs' to serve as proxies for other nearby SNPs in high LD. General guidelines for SNP selection in candidate genes/regions and genome-wide studies are provided in this protocol, along with illustrative examples. Publicly available web-based resources are utilized to browse and retrieve data, and software, such as Haploview and Goldsurfer2, is applied to investigate LD and to select tagSNPs.

The instability of the CAG repeat size of the HD gene when transmitted intergenerationally has critical implications for genetic counseling practices. In particular, CAG repeats between 27 and 35 have been the subject of debate based on small samples. To address this issue, we analyzed allelic instability in the Venezuelan HD kindreds, the largest and most informative families ascertained for HD. We identified 647 transmissions. Our results indicate that repeats in the 27-35 CAG range are highly stable. Out of 69 transmitted alleles in this range, none expand into any penetrant ranges. Contrastingly, 14% of alleles transmitted from the incompletely penetrant range (36-39 CAGs) expand into the completely penetrant range, characterized by alleles with 40 or more CAG repeats. At least 12 of the 534 transmissions from the completely penetrant range contract into the incompletely penetrant range of 36-39 CAG repeats. In these kindreds, none of the individuals with 27-39 CAGs were symptomatic, even though they ranged in age from 11 to 82 years. We expect these findings to be helpful in updating genetic counseling practices.

Association replication studies have a poor track record and, even when successful, often claim association with different markers, alleles, and phenotypes than those reported in the primary study. It is unknown whether these outcomes reflect genuine associations or false-positive results. A greater understanding of these observations is essential for genomewide association (GWA) studies, since they have the potential to identify multiple new associations that that will require external validation. Theoretically, a repeat association with precisely the same variant in an independent sample is the gold standard for replication, but testing additional variants is commonplace in replication studies. Finding different associated SNPs within the same gene or region as that originally identified is often reported as confirmatory evidence. Here, we compare the probability of replicating a gene or region under two commonly used marker-selection strategies: an “exact” approach that involves only the originally significant markers and a “local” approach that involves both the originally significant markers and others in the same region. When a region of high intermarker linkage disequilibrium is tested to replicate an initial finding that is only weak association with disease, the local approach is a good strategy. Otherwise, the most powerful and efficient strategy for replication involves testing only the initially identified variants. Association with a marker other than that originally identified can occur frequently, even in the presence of real effects in a low-powered replication study, and instances of such association increase as the number of included variants increases. Our results provide a basis for the design and interpretation of GWA replication studies and point to the importance of a clear distinction between fine mapping and replication after GWA.

What constitutes replication of a genotype–phenotype association, and how best can it be achieved?

A recent study by Cheung et al. demonstrates how to identify expression quantitative trait loci (eQTLs) underlying gene expression phenotypes through a combination of genome-wide linkage analysis and subsequent fine mapping or by genome-wide association (GWA) analysis. This study emphasizes the complexity of human traits, highlighting the challenges faced by investigators – in particular, insufficient linkage disequilibrium between the trait and marker variant, genetic heterogeneity and correcting for multiple testing will all adversely impact the power to detect loci by association. These issues must be considered carefully if the GWA approach is to succeed in mapping complex phenotypes.

Transmission distortion refers to deviation from the normal 50:50 transmission of alleles from parents to offspring. Identification of genomic regions which undergo distortion is necessary for the correct interpretation of linkage and association studies, since tests of linkage using affected relative pairs and family based tests of association will yield spurious results in the presence of transmission distortion. With the increasing availability of genome-wide high density SNP data (e.g. from the International HapMap project), identification of these loci is now a real possibility. Here we present an analytical formula which demonstrates that the power to detect transmission distortion is a simple function of the number of heterozygous parents in the sample and the level of distortion at the locus. Our results indicate that whilst it will be possible to identify loci undergoing major levels of distortion using tens or hundreds of trios, large sample sizes in the order of tens of thousands of trios will be necessary to detect minor levels of distortion with appreciable power. The corollary is that genome-wide searches are unlikely to identify loci where the level of distortion is small, although they may serve to identify interesting regions worthy of follow up.

Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.

In the search for common genetic variants that contribute to prevalent human diseases, patterns of linkage disequilibrium (LD) among linked markers should be considered when selecting SNPs. Genotyping efficiency can be increased by choosing tagging SNPs (tagSNPs) in LD with other SNPs. However, it remains to be seen whether tagSNPs defined in one population efficiently capture LD in other populations; that is, how portable tagSNPs are. Indeed, tagSNP portability is a challenge for the applicability of HapMap results. We analyzed 144 SNPs in a 1-Mb region of chromosome 22 in 1055 individuals from 38 worldwide populations, classified into seven continental groups. We measured tagSNP portability by choosing three reference populations (to approximate the three HapMap populations), defining tagSNPs, and applying them to other populations independently on the availability of information on the tagSNPs in the compared population. We found that tagSNPs are highly informative in other populations within each continental group. Moreover, tagSNPs defined in Europeans are often efficient for Middle Eastern and Central/South Asian populations. TagSNPs defined in the three reference populations are also efficient for more distant and differentiated populations (Oceania, Americas), in which the impact of their special demographic history on the genetic structure does not interfere with successfully detecting the most common haplotype variation. This high degree of portability lends promise to the search for disease association in different populations, once tagSNPs are defined in a few reference populations like those analyzed in the HapMap initiative.

As part of a recent high-density linkage disequilibrium (LD) study of chromosome 20, we obtained genotypes for ∼30,000 SNPs at a density of 1 SNP/2 kb on four different population samples (47 CEPH founders; 91 UK unrelateds [unrelated white individuals of western European ancestry]; 97 African Americans; 42 East Asians). We observed that ∼50% of SNPs had at least one genetically indistinguishable partner; i.e., for every individual considered, their genotype at the first locus was identical to their genotype at the second locus, or in LD terms, the SNPs were in “perfect” LD (r2 = 1.0). These “genetically indistinguishable SNPs” (giSNPs) formed into clusters of varying size. The larger the cluster, the greater the tendency to be located within genes and to overlap with giSNP clusters in other population samples. As might be expected for this map density, many giSNPs were located close to one another, thus reflecting local regions of undetected recombination or haplotype blocks. However, ∼1/3 of giSNP clusters had intermingled, non-indistinguishable SNPs with incomplete LD (D′ and r2

Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution. © 2005 Nature Publishing Group., link_to_OA_fulltext

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype–phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.

Autoimmune, inflammatory, and infectious diseases present a major burden to human health and are frequently associated with loci in the human major histocompatibility complex (MHC). Here, we report a high-resolution (1.9 kb) linkage-disequilibrium (LD) map of a 4.46-Mb fragment containing the MHC in U.S. pedigrees with northern and western European ancestry collected by the Centre d'Etude du Polymorphisme Humain (CEPH) and the first generation of haplotype tag single-nucleotide polymorphisms (tagSNPs) that provide up to a fivefold increase in genotyping efficiency for all future MHC-linked disease-association studies. The data confirm previously identified recombination hotspots in the class II region and allow the prediction of numerous novel hotspots in the class I and class III regions. The region of longest LD maps outside the classic MHC to the extended class I region spanning the MHC-linked olfactory-receptor gene cluster. The extended haplotype homozygosity analysis for recent positive selection shows that all 14 outlying haplotype variants map to a single extended haplotype, which most commonly bears HLA-DRB1*1501. The SNP data, haplotype blocks, and tagSNPs analysis reported here have been entered into a multidimensional Web-based database (GLOVAR), where they can be accessed and viewed in the context of relevant genome annotation. This LD map allowed us to give coordinates for the extremely variable LD structure underlying the MHC.

After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci1, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.

Genomewide linkage scans have traditionally employed panels of microsatellite markers spaced at intervals of approximately 10 cM across the genome. However, there is a growing realization that a map of closely spaced single-nucleotide polymorphisms (SNPs) may offer equal or superior power to detect linkage, compared with low-density microsatellite maps. We performed a series of simulations to calculate the information content associated with microsatellite and SNP maps across a range of different marker densities and heterozygosities for sib pairs (with and without parental genotypes), sib trios, and sib quads. In the case of microsatellite markers, we varied density across 11 levels (1 marker every 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cM) and marker heterozygosity across 6 levels (2, 3, 4, 5, 10, or 20 equally frequent alleles), whereas, in the case of SNPs, we varied marker density across 4 levels (1 marker every 0.1, 0.2, 0.5, or 1 cM) and minor-allele frequency across 7 levels (0.5, 0.4, 0.3, 0.2, 0.1, 0.05, and 0.01). When parental genotypes were available, a map consisting of microsatellites spaced every 2 cM or a relatively sparse map of SNPs (i.e., at least 1 SNP/cM) was sufficient to extract most of the inheritance information from the map (95% in most cases). However, when parental genotypes were unavailable, it was important to use as dense a map of markers as possible to extract the greatest amount of inheritance information. It is important to note that the information content associated with a traditional map of microsatellite markers (i.e., 1 marker every ~10 cM) was significantly lower than the information content associated with a dense map of SNPs or microsatellites. These results strongly suggest that previous linkage studies that employed sparse microsatellite maps could benefit substantially from reanalysis by use of a denser map of markers.

Haplotype tagging is a means of retaining most of the information in high density marker maps, while reducing genotyping requirements. Estimates of the numbers of tagging SNPs required to cover the human genome have varied widely, ranging from 100,000 to 1,000,000. Tagging has been applied to a number of gene-based datasets but has not been evaluated in contexts reflecting those of genome-wide association studies--large chromosome regions and multiple samples drawn from the same population. We analysed 5000 common markers across a 10 Mb segment of human chromosome 20 in three samples (UK Caucasian, CEPH Caucasian, African American) to evaluate tagging efficiency and consistency. Overall, the results indicate a high degree of efficiency, yielding 3-5-fold savings in Caucasians and 2-3-fold savings in African Americans. These levels varied according to linkage disequilibrium (LD) levels, tagging thresholds and allele frequencies, but in high LD regions they did not vary markedly due to marker density. However, a strong positive relationship between marker density and tagging was observed, relating to the fact that increasing marker density yields greater sequence coverage in high LD, thus requiring more tag SNPs to cover a greater fraction of the genome. Encouragingly, whatever the density employed, a high level of robustness was observed between UK and CEPH samples, as most of the htSNPs selected in one sample were also appropriate as tags in the other.

The design and feasibility of genetic studies of complex diseases are critically dependent on the extent and distribution of linkage disequilibrium (LD) across the genome and between different populations. We have examined genomewide and region-specific LD in a young genetically isolated population identified in the Netherlands by genotyping approximately 800 Short Tandem Repeat markers distributed genomewide across 58 individuals. Several regions were analyzed further using a denser marker map. The permutation-corrected measure of LD was used for analysis. A significant (P

Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.

BACKGROUND: Endometriosis occurs in several non-human primate species that have menstrual cycles. This study investigated the prevalence and familial aggregation of endometriosis in one of those species, the rhesus macaque. METHODS: Between 1978 and 2001, 142 animals with endometriosis were identified from necropsy and surgical records and through the use of magnetic resonance imaging (MRI) at the Wisconsin National Primate Research Center, Madison, USA. All cases were used to build one large multigenerational pedigree and nine nuclear families comprising 1602 females in total. By 2002, the pedigrees contained 124 cases diagnosed at necropsy; 17 at surgery and three at MRI. Female animals that had died aged > or = 10 years without endometriosis, had both ovaries until at least 1 year prior to death, and had a full necropsy, were considered unaffected. RESULTS: The prevalence of endometriosis among necropsied animals aged > or = 10 years in the colony was 31.4% [95% confidence interval (CI) 26.9-35.9%]; prevalence increased with rising age and calendar age at death. Familial aggregation of endometriosis was strongly suggested by a significantly higher average kinship coefficient among affecteds compared with unaffecteds (P < 0.001) and a higher recurrence risk for full sibs (0.75; 95% CI 0.45-1.0) compared with maternal half sibs (0.26; 95% CI 0.10-0.41) and paternal half sibs (0.18; 95% CI 0.02-0.34). The segregation ratio among affected mothers (44.2%) was not significantly higher compared with unaffected mothers (36.6%). CONCLUSIONS: The results support familial aggregation of endometriosis in the rhesus macaque, and indicate that this is a promising animal model for the investigation of mode of inheritance, the location of potential genetic susceptibility loci and the influence of environmental factors.

Linkage disequilibrium (LD) is a measure of the degree of association between alleles in a population. The detection of disease-causing variants by association with neighbouring single nucleotide polymorphisms (SNPs) depends on the existence of strong LD between them. Previous studies have indicated that the extent of LD is highly variable in different chromosome regions and different populations, demonstrating the importance of genome-wide accurate measurement of LD at high resolution throughout the human genome. A uniform feature of these studies has been the inability to detect LD in regions of low marker density. To investigate the dependence of LD patterns on marker selection we performed a high-resolution study in African-American, Asian and UK Caucasian populations. We selected over 5000 SNPs with an average spacing of approximately 1 SNP per 2 kb after validating ca 12 000 SNPs derived from a dense SNP collection (1 SNP per 0.3 kb on average). Applications of different statistical methods of LD assessment highlight similar areas of high and low LD. However, at high resolution, features such as overall sequence coverage in LD blocks and block boundaries vary substantially with respect to marker density. Model-based linkage disequilibrium unit (LDU) maps appear robust to marker density and consistently influenced by marker allele frequency. The results suggest that very dense marker sets will be required to yield stable views of fine-scale LD in the human genome.

A dense set of 5,000 SNPs on a 10-Mb region of human chromosome 20 has been typed on samples of African Americans, East Asians, and United Kingdom Caucasians. There are departures from Hardy-Weinberg equilibrium beyond the level at which markers are often discarded because of possible genotyping errors. The observation that markers showing such departures are often close together on the chromosome confirms the result that Hardy-Weinberg tests at two loci are correlated to an extent that depends on the linkage disequilibrium between those two markers. Linkage disequilibrium can be described by the composite linkage disequilibrium coefficient, the parameter that determines the behavior of case-control allelic tests of association. A useful preliminary investigation of datasets of this type is provided by counting the numbers of distinct multi-locus genotypes in windows of a few markers.

Nature Reviews Drug Discovery 15, 596–597 (2016) The values shown on the y axis of Figure 1b were incorrect. These have now been corrected in the online versions of the article.

Lipoprotein-associated phospholipase A2 (Lp-PLA2) produces the proinflammatory mediators lysophosphatidylcholine and oxidized free fatty acids through hydrolysis of oxidized phospholipids carried on low-density lipoproteins in atherosclerotic plaques. Although increased Lp-PLA2 activity has been associated with higher risks of occlusive vascular diseases, recent phase III trials of the Lp-PLA2 inhibitor darapladib, which reduces Lp-PLA2 activity by ∼60%, failed to establish a protective role of darapladib for prevention of major vascular events in patients with stable coronary heart disease (CHD) or acute coronary syndrome 1, 2.

Differences in genetic control of BMD by skeletal sites and genders were examined by complex segregation analysis in 816 members of 147 families with probands with extreme low BMD. Spine BMD correlated more strongly in male-male comparisons and hip BMD in female-female comparisons, consistent with gender- and site-specificity of BMD heritability. Introduction: Evidence from studies in animals and humans suggests that the genetic control of bone mineral density (BMD) may differ at different skeletal sites and between genders. This question has important implications for the design and interpretation of genetic studies of osteoporosis. Methods: We examined the genetic profile of 147 families with 816 individuals recruited through probands with extreme low BMD (T-score < −2.5, Z-score < −2.0). Complex segregation analysis was performed using the Pedigree Analysis Package. BMD was measured by DXA at both lumbar spine (L1-L4) and femoral neck. Results: Complex segregation analysis excluded purely monogenic and environmental models of segregation of lumbar spine and femoral neck BMD in these families. Pure polygenic models were excluded at the lumbar spine when menopausal status was considered as a covariate, but not at the femoral neck. Mendelian models with a residual polygenic component were not excluded. These models were consistent with the presence of a rare Mendelian genotype of prevalence 3–19 %, causing high BMD at the hip and spine in these families, with additional polygenic effects. Total heritability range at the lumbar spine was 61–67 % and at the femoral neck was 44–67 %. Significant differences in correlation of femoral neck and lumbar spine BMD were observed between male and female relative pairs, with male-male comparisons exhibiting stronger lumbar spine BMD correlation than femoral neck, and female-female comparisons having greater femoral neck BMD correlation than lumbar spine. These findings remained true for parent-offspring correlations when menopausal status was taken into account. The recurrence risk ratio for siblings of probands of a Z-score < −2.0 was 5.4 at the lumbar spine and 5.9 at the femoral neck. Conclusions: These findings support gender- and site-specificity of the inheritance of BMD. These results should be considered in the design and interpretation of genetic studies of osteoporosis.

Atopic or immunoglobulin E (IgE)-mediated diseases include the common disorders of asthma, atopic dermatitis and allergic rhinitis1. Chromosome 13q14 shows consistent linkage to atopy and the total serum IgE concentration2,3,4,5,6. We previously identified association between total serum IgE levels and a novel 13q14 microsatellite (USAT24G1; ref. 7) and have now localized the underlying quantitative-trait locus (QTL) in a comprehensive single-nucleotide polymorphism (SNP) map. We found replicated association to IgE levels that was attributed to several alleles in a single gene, PHF11. We also found association with these variants to severe clinical asthma. The gene product (PHF11) contains two PHD zinc fingers and probably regulates transcription. Distinctive splice variants were expressed in immune tissues and cells.

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.

Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.

The evaluation of results from primary genomewide linkage scans of complex human traits remains an area of importance and considerable debate. Apart from the usual assessment of statistical significance by use of asymptotic and empirical calculations, an additional means of evaluation--based on counting the number of distinct regions showing evidence of linkage--is possible. We have explored the characteristics of such a locus-counting method over a range of experimental conditions typically encountered during genomewide scans for complex trait loci. Under the null hypothesis, factors that have an impact on the informativeness of the data--such as map density, availability of parental data, and completeness of genotyping--are seen to markedly influence the number of regions of excess allele sharing and the empirically derived genomewide significance of the associated LOD score thresholds. In some circumstances, the expected number of regions is less than one-quarter of that predicted under the assumption of a dense map and complete extraction of inheritance information. We have applied this method to a previously analyzed data set--the Warren 2 genome scan for type 2-diabetes susceptibility--and demonstrate that more regions showing evidence for linkage were observed in the primary genome scan than would be expected by chance, across the whole range of LOD scores, even though no single linkage result achieved empirical genomewide statistical significance. Locus counting may be useful in assessing the results from genome scans for complex traits in general, especially because relatively few scans generate evidence for linkage reaching genomewide significance by dense-map criteria. By taking account of the effects of reduced data informativeness on the expected number of regions showing evidence for linkage, a more meaningful, and less conservative, evaluation of the results from such linkage studies is possible.

Objective: Endometriosis is a complex trait, in which genetic and environmental factors act together to produce the phenotype. So far, research into candidate genes has largely been based on biological and clinical hypotheses. Results of these studies and the wealth of gene and marker sequence information from the Human Genome Project could—when brought together—provide the researcher with new etiological avenues to explore. Design: Online review. Setting: The Web site being developed draws together evidence of genetic variants associated with endometriosis and new etiological hypotheses. It incorporates links to up-to-date genomic information relevant to the candidates from a range of bioinformatics databases. Patient(s): Endometriosis cases and controls in association studies. Intervention(s): None. Main Outcome Measure(s): Allele and genotype frequencies. Result(s): The Web site summarizes the main hypotheses for endometriosis etiology that provide the basis for the search for genes involved, together with [1] the existing evidence of associations with candidate genes, with links to the relevant publications; [2] details of these candidate genes and the surrounding chromosomal area (location, function, polymorphisms, marker maps); [3] molecular biological findings, from studies of aberrant gene and protein expression in relevant tissues; and [4] chromosomal regions that have been implicated. Conclusion(s): This Web site should provide a useful information tool for the endometriosis researcher. We encourage researchers worldwide to use it, contribute to it, and share their knowledge about the condition.