Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
gene-gene interactions; gene-environment interactions; rare variants; next generation sequencing; complex phenotypes; simulations; computational resources
Telomere length has been associated with risk of many cancers, but results are inconsistent. Seven single nucleotide polymorphisms (SNPs) previously associated with mean leukocyte telomere length were either genotyped or well-imputed in 11108 case patients and 13933 control patients from Europe, Israel, the United States and Australia, four of the seven SNPs reached a P value under .05 (two-sided). A genetic score that predicts telomere length, derived from these seven SNPs, is strongly associated (P = 8.92x10-9, two-sided) with melanoma risk. This demonstrates that the previously observed association between longer telomere length and increased melanoma risk is not attributable to confounding via shared environmental effects (such as ultraviolet exposure) or reverse causality. We provide the first proof that multiple germline genetic determinants of telomere length influence cancer risk.
Common variants in two of the five genetic regions recently identified from genome-wide association studies (GWAS) of risk of glioma were reported to interact with a history of allergic symptoms. In a pooled analysis of five epidemiologic studies, we evaluated the association between the five GWAS implicated gene variants and allergies and autoimmune conditions (AIC) on glioma risk (851 adult glioma cases and 3,977 controls). We further evaluated the joint effects between allergies and AIC and these gene variants on glioma risk. Risk estimates were calculated as odds ratios (OR) and 95 % confidence intervals (95 % CI), adjusted for age, gender, and study. Joint effects were evaluated by conducting stratified analyses whereby the risk associations (OR and 95 % CI) with the allergy or autoimmune conditions for glioma were evaluated by the presence or absence of the ‘at-risk’ variant, and estimated p interaction by fitting models with the main effects of allergy or autoimmune conditions and genotype and an interaction (product) term between them. Four of the five SNPs previously reported by others were statistically significantly associated with increased risk of glioma in our study (rs2736100, rs4295627, rs4977756, and rs6010620); rs498872 was not associated with glioma in our study. Reporting any allergies or AIC was associated with reduced risks of glioma (allergy: adjusted OR = 0.71, 95 % CI 0.55–0.91; AIC: adjusted OR = 0.65, 95 % CI 0.47–0.90). We did not observe differential association between allergic or autoimmune conditions and glioma by genotype, and there were no statistically significant p interactions. Stratified analysis by glioma grade (low and high grade) did not suggest risk differences by disease grade. Our results do not provide evidence that allergies or AIC modulate the association between the four GWAS-identified SNPs examined and risk of glioma.
Single-nucleotide polymorphisms; Glioma; Allergies; Autoimmune conditions; Gene–environment interaction
The major factors individually reported to be associated with an increased frequency of CDKN2A mutations are increased number of patients with melanoma in a family, early age at melanoma diagnosis, and family members with multiple primary melanomas (MPM) or pancreatic cancer.
These four features were examined in 385 families with ⩾3 patients with melanoma pooled by 17 GenoMEL groups, and these attributes were compared across continents.
Overall, 39% of families had CDKN2A mutations ranging from 20% (32/162) in Australia to 45% (29/65) in North America to 57% (89/157) in Europe. All four features in each group, except pancreatic cancer in Australia (p = 0.38), individually showed significant associations with CDKN2A mutations, but the effects varied widely across continents. Multivariate examination also showed different predictors of mutation risk across continents. In Australian families, ⩾2 patients with MPM, median age at melanoma diagnosis ⩽40 years and ⩾6 patients with melanoma in a family jointly predicted the mutation risk. In European families, all four factors concurrently predicted the risk, but with less stringent criteria than in Australia. In North American families, only ⩾1 patient with MPM and age at diagnosis ⩽40 years simultaneously predicted the mutation risk.
The variation in CDKN2A mutations for the four features across continents is consistent with the lower melanoma incidence rates in Europe and higher rates of sporadic melanoma in Australia. The lack of a pancreatic cancer–CDKN2A mutation relationship in Australia probably reflects the divergent spectrum of mutations in families from Australia versus those from North America and Europe. GenoMEL is exploring candidate host, genetic and/or environmental risk factors to better understand the variation observed.
; multiple primary melanomas; pancreatic cancer
In contemporary oncology practices there is an increasing emphasis on concurrent evaluation of multiple genomic alterations within the biological pathways driving tumorigenesis. At the foundation of this paradigm shift are several commercially available tumor panels using next-generation sequencing to develop a more complete molecular blueprint of the tumor. Ideally, these would be used to identify clinically actionable variants that can be matched with available molecularly targeted therapy, regardless of the tumor site or histology. Currently, there is little information available on the post-analytic processes unique to next-generation sequencing platforms used by the companies offering these tests. Additionally, evidence of clinical validity showing an association between the genetic markers curated in these tests with treatment response to approved molecularly targeted therapies is lacking across all solid-tumor types. To date, there is no published data of improved outcomes when using the commercially available tests to guide treatment decisions. The uniqueness of these tests from other genomic applications used to guide clinical treatment decisions lie in the sequencing platforms used to generate large amounts of genomic data, which have their own related issues regarding analytic and clinical validity, necessary precursors to the evaluation of clinical utility. The generation and interpretation of these data will require new evidentiary standards for establishing not only clinical utility, but also analytical and clinical validity for this emerging paradigm in oncology practice.
Genetic and environmental factors jointly influence cancer risk. The National Institutes of Health (NIH) has made the study of gene-environment (GxE) interactions a research priority since the year 2000.
To assess the current status of GxE research in cancer, we analyzed the extramural grant portfolio of the National Cancer Institute (NCI) from Fiscal Years 2007 to 2009. Publications attributed to selected grants were also evaluated.
From the 1,106 research grants identified in our portfolio analysis, a random sample of 450 grants (40%) was selected for data abstraction; of these, 147 (33%) were considered relevant. The most common cancer type was breast (20%, n=29), followed by lymphoproliferative (10%, n=14), colorectal (9%, n=13), melanoma/other skin (9%, n=13), and lung/upper aero-digestive tract (8%, n=12) cancers. The majority of grants were studies of candidate genes (68%, n=100) compared to genome-wide association studies (GWAS) (8%, n=12). Approximately one third studied environmental exposures categorized as energy balance (37%, n=54) or drugs/treatment (29%, n=43). From the 147 relevant grants, 108 publications classified as GxE or pharmacogenomic were identified. These publications were linked to 37 of the 147 grant applications (25%).
The findings from our portfolio analysis suggest that GxE studies are concentrated in specific areas. There is room for investments in other aspects of GxE research, including, but not limited to developing alternative approaches to exposure assessment, broadening the spectrum of cancer types investigated, and performing GxE within GWAS.
This portfolio analysis provides a cross-sectional review of NCI support for GxE research in cancer.
Gene-Environment Interaction; Grants
Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry, and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one novel locus at 5q33 (GALNT10, rs7708584, p=3.4×10−11) and another at 7p15 when combined with data from the Giant consortium (MIR148A/NFE2L3, rs10261878, p=1.2×10−10). We also found suggestive evidence of an association at a third locus at 6q16 in the African ancestry sample (KLHL32, rs974417, p=6.9×10−8). Thirty-two of the 36 previously established BMI variants displayed directionally consistent effect estimates in our GWAS (binomial p=9.7×10−7), of which five reached genome-wide significance. These findings provide strong support for shared BMI loci across populations as well as for the utility of studying ancestrally diverse populations.
We report the results of an association study of melanoma based on the genome-wide imputation of the genotypes of 1,353 cases and 3,566 controls of European origin conducted by the GenoMEL consortium. This revealed a novel association between several single nucleotide polymorphisms (SNPs) in intron 8 of the FTO gene, including rs16953002, which replicated using 12,313 cases and 55,667 controls of European ancestry from Europe, the USA and Australia (combined p=3.6×10−12, per-allele OR for A=1.16). As well as identifying a novel melanoma susceptibility locus, this is the first study to identify and replicate an association with SNPs in FTO not related to body mass index (BMI). These SNPs are not in intron 1 (the BMI-related region) and show no association with BMI. This suggests FTO’s function may be broader than the existing paradigm that FTO variants influence multiple traits only through their associations with BMI and obesity.
The Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is develop scientific priorities for cancer epidemiology research in the next decade. We would like to engage the research community and other stakeholders in a planning effort that will include a workshop, in December, 2012, to help shape new foci for cancer epidemiology research. To facilitate the process of defining the future of cancer epidemiology, we invite the research community to join in an ongoing Web-based conversation at http://blog-epi.grants.cancer.gov/ to develop priorities and the next generation of high-impact studies.
In an analysis of 31,717 cancer cases and 26,136 cancer-free controls drawn from 13 genome-wide association studies (GWAS), we observed large chromosomal abnormalities in a subset of clones from DNA obtained from blood or buccal samples. Mosaic chromosomal abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of size >2 Mb were observed in autosomes of 517 individuals (0.89%) with abnormal cell proportions between 7% and 95%. In cancer-free individuals, the frequency increased with age; 0.23% under 50 and 1.91% between 75 and 79 (p=4.8×10−8). Mosaic abnormalities were more frequent in individuals with solid-tumors (0.97% versus 0.74% in cancer-free individuals, OR=1.25, p=0.016), with a stronger association for cases who had DNA collected prior to diagnosis or treatment (OR=1.45, p=0.0005). Detectable clonal mosaicism was common in individuals for whom DNA was collected at least one year prior to diagnosis of leukemia compared to cancer-free individuals (OR=35.4, p=3.8×10−11). These findings underscore the importance of the role and time-dependent nature of somatic events in the etiology of cancer and other late-onset diseases.
Advances in genomics and related fields are promising tools for risk assessment, early detection, and targeted therapies across the entire cancer care continuum. In this commentary, we submit that this promise cannot be fulfilled without an enhanced translational genomics research agenda firmly rooted in the population sciences. Population sciences include multiple disciplines that are needed throughout the translational research continuum. For example, epidemiologic studies are needed not only to accelerate genomic discoveries and new biological insights into cancer etiology and pathogenesis, but to characterize and critically evaluate these discoveries in well defined populations for their potential for cancer prediction, prevention and response to treatments. Behavioral, social and communication sciences are needed to explore genomic-modulated responses to old and new behavioral interventions, adherence to therapies, decision-making across the continuum, and effective use in health care. Implementation science, health services, outcomes research, comparative effectiveness research and regulatory science are needed for moving validated genomic applications into practice and for measuring their effectiveness, cost effectiveness and unintended consequences. Knowledge synthesis, evidence reviews and economic modeling of the effects of promising genomic applications will facilitate policy decisions, and evidence-based recommendations. Several independent and multidisciplinary panels have recently made specific recommendations for enhanced research and policy infrastructure to inform clinical and population research for moving genomic innovations into the cancer care continuum. An enhanced translational genomics and population sciences agenda is urgently needed to fulfill the promise of genomics in reducing the burden of cancer.
cancer; genetics; genomics; medicine; population sciences; public health; translation
Genome-wide association studies have broadened our understanding of the genetic architecture of cancer to include common variants, in addition to the rare variants previously identified by linkage analysis. We review current knowledge on the genetic architecture of four cancers—breast, lung, prostate and colorectal—for which the balance of common and rare alleles identified ranges from fewer common alleles (lung cancer) to more common alleles (prostate cancer). Although most variants are cancer specific, pleiotropy has been observed for several variants, for example, variants at the 8q24 locus and breast, ovarian and prostate cancers or variants in KITLG in relation to hair color and testicular cancer. Although few studies have been adequately powered to investigate heterogeneity among ancestry groups, effect sizes associated with common variants have been reported to be fairly homogenous among ethnic groups. Some associations appear to be ancestry specific, such as HNF1B, which is associated with prostate cancer in European Americans and Latinos but not in African-Americans. Studies of cancer and other complex diseases suggest that a simple dichotomy between rare and common allelic architectures may be too simplistic and that future research is needed to characterize a fuller spectrum of allele frequency (common (>5%), uncommon (1–5%) and rare (<<1%) alleles) and effect size. In addition, a broadening of the concept of genetic architecture to encompass both population architecture, which reflects differences in exposures, genetic factors and population level risk among diverse groups of people, and genomic architecture, which includes structural, epigenomic and somatic variation, is envisioned.
We report a genome-wide association study of melanoma, conducted by GenoMEL, of 2,981 cases, of European ancestry, and 1,982 study-specific controls, plus a further 6,426 French and UK population controls, all genotyped for 317,000 or 610,000 SNPs. The analysis confirmed previously known melanoma susceptibility loci. The 7 novel regions with at least one SNP with p<10−5 and further local imputed or genotyped support were selected for replication using two other genome-wide studies (from Australia and Houston, Texas). Additional replication came from UK and Dutch case-control series. Three of the 7 regions replicated at p<10−3: an ATM missense polymorphism (rs1801516, overall p=3.4×10−9); a polymorphism within MX2 (rs45430, p=2.9×10−9) and a SNP adjacent to CASP8 (rs13016963, p=8.6×10−10). A fourth region near CCND1 remains of potential interest, showing suggestive but inconclusive evidence of replication. Unlike the previously known regions, the novel loci showed no association with nevus or pigmentation phenotypes in a large UK case-control series.
Recombination, together with mutation, is the ultimate source of genetic variation in populations. We leverage the recent mixture of people of African and European ancestry in the Americas to build a genetic map measuring the probability of crossing-over at each position in the genome, based on about 2.1 million crossovers in 30,000 unrelated African Americans. At intervals of more than three megabases it is nearly identical to a map built in Europeans. At finer scales it differs significantly, and we identify about 2,500 recombination hotspots that are active in people of West African ancestry but nearly inactive in Europeans. The probability of a crossover at these hotspots is almost fully controlled by the alleles an individual carries at PRDM9 (P<10−245). We identify a 17 base pair DNA sequence motif that is enriched in these hotspots, and is an excellent match to the predicted binding target of African-enriched alleles of PRDM9.
A significant proportion of high-risk breast cancer families are not explained by mutations in known genes. Recent genome-wide searches (GWS) have not revealed any single major locus reminiscent of BRCA1 and BRCA2, indicating that still unidentified genes may explain relatively few families each or interact in a way obscure to linkage analyses. This has drawn attention to possible benefits of studying populations where genetic heterogeneity might be reduced. We thus performed a GWS for linkage on nine Icelandic multiple-case non-BRCA1/2 families of desirable size for mapping highly penetrant loci. To follow up suggestive loci, an additional 13 families from other Nordic countries were genotyped for selected markers.
GWS was performed using 811 microsatellite markers providing about five centiMorgan (cM) resolution. Multipoint logarithm of odds (LOD) scores were calculated using parametric and nonparametric methods. For selected markers and cases, tumour tissue was compared to normal tissue to look for allelic loss indicative of a tumour suppressor gene.
The three highest signals were located at chromosomes 6q, 2p and 14q. One family contributed suggestive LOD scores (LOD 2.63 to 3.03, dominant model) at all these regions, without consistent evidence of a tumour suppressor gene. Haplotypes in nine affected family members mapped the loci to 2p23.2 to p21, 6q14.2 to q23.2 and 14q21.3 to q24.3. No evidence of a highly penetrant locus was found among the remaining families. The heterogeneity LOD (HLOD) at the 6q, 2p and 14q loci in all families was 3.27, 1.66 and 1.24, respectively. The subset of 13 Nordic families showed supportive HLODs at chromosome 6q (ranging from 0.34 to 1.37 by country subset). The 2p and 14q loci overlap with regions indicated by large families in previous GWS studies of breast cancer.
Chromosomes 2p, 6q and 14q are candidate sites for genes contributing together to high breast cancer risk. A polygenic model is supported, suggesting the joint effect of genes in contributing to breast cancer risk to be rather common in non-BRCA1/2 families. For genetic counselling it would seem important to resolve the mode of genetic interaction.
We report a genome-wide association study of melanoma conducted by the GenoMEL consortium based on 317k tagging SNPs for 1650 genetically-enriched cases (from Europe and Australia) and 4336 controls and subsequent replication in 1149 genetically-enriched cases and 964 controls and a population-based case-control study of 1163 cases and 903 controls. The genome-wide screen identified five regions with genotyped or imputed SNPs reaching p < 5×10−7; three regions were replicated: 16q24 encompassing MC1R (overall p=2.54×10−27 for rs258322), 11q14-q21 encompassing TYR (p=2.41×10−14 for rs1393350) and 9p21 adjacent to MTAP and flanking CDKN2A (p=4.03×10−7 for rs7023329). MC1R and TYR are associated with pigmentation, freckling and cutaneous sun sensitivity, well-recognised melanoma risk factors, while the 9p21 locus is novel for common variants associated with melanoma. Despite wide variation in allele frequency, these genetic variants show notable homogeneity of effect across populations of European ancestry living at different latitudes and contribute independently to melanoma risk.
We conducted a genome-wide association pooling study for cutaneous melanoma and performed validation in samples totalling 2019 cases and 2105 controls. Using pooling we identified a novel melanoma risk locus on chromosome 20 (rs910873, rs1885120), with replication in two further samples (combined P <1 × 10-15). The odds ratio is 1.75 (1.53, 2.01), with evidence for stronger association in early onset cases.
By assaying hundreds of thousands of single nucleotide polymorphisms, genome wide association studies (GWAS) allow for a powerful, unbiased review of the entire genome to localize common genetic variants that influence health and disease. Although it is widely recognized that some correction for multiple testing is necessary, in order to control the family-wide Type 1 Error in genetic association studies, it is not clear which method to utilize. One simple approach is to perform a Bonferroni correction using all n single nucleotide polymorphisms (SNPs) across the genome; however this approach is highly conservative and would "overcorrect" for SNPs that are not truly independent. Many SNPs fall within regions of strong linkage disequilibrium (LD) ("blocks") and should not be considered "independent".
We proposed to approximate the number of "independent" SNPs by counting 1 SNP per LD block, plus all SNPs outside of blocks (interblock SNPs). We examined the effective number of independent SNPs for Genome Wide Association Study (GWAS) panels. In the CEPH Utah (CEU) population, by considering the interdependence of SNPs, we could reduce the total number of effective tests within the Affymetrix and Illumina SNP panels from 500,000 and 317,000 to 67,000 and 82,000 "independent" SNPs, respectively. For the Affymetrix 500 K and Illumina 317 K GWAS SNP panels we recommend using 10-5, 10-7 and 10-8 and for the Phase II HapMap CEPH Utah and Yoruba populations we recommend using 10-6, 10-7 and 10-9 as "suggestive", "significant" and "highly significant" p-value thresholds to properly control the family-wide Type 1 error.
By approximating the effective number of independent SNPs across the genome we are able to 'correct' for a more accurate number of tests and therefore develop 'LD adjusted' Bonferroni corrected p-value thresholds that account for the interdepdendence of SNPs on well-utilized commercially available SNP "chips". These thresholds will serve as guides to researchers trying to decide which regions of the genome should be studied further.
Several mutations in the PALB2 gene (partner and localizer of BRCA2) have been associated with an increased risk of breast cancer, including a founder mutation, 1592delT, reported in Finnish breast cancer families. Although most often the risk is moderate, it doesn't exclude families with high-risk mutations to exist and such observations have been reported. To see if high-risk PALB2-mutations may be present in the geographically confined population of Iceland, linkage analysis was done on 111 individuals, thereof 61 breast cancer cases, from 9 high-risk non-BRCA1/BRCA2 breast cancer families, targeting the PALB2 region. Also, screening for the 1592delT founder mutation in the 9 high-risk families and in 638 unselected breast cancer cases was performed. The results indicate no linkage in any of the high-risk families and screening for the 1592delT mutation was negative in all samples. PALB2 appears not to be a significant factor in high-risk breast cancer families in Iceland and the 1592delT mutation is not seen to be associated with breast cancer in Iceland.
Since most multipoint linkage analysis programs currently assume linkage equilibrium (LE) between markers when inferring parental haplotypes, ignoring linkage disequilibrium (LD) may inflate the Type I error rate. We investigated the effect of LD on the Type I error rate and power of nonparametric multipoint linkage analysis of two-generation and multigenerational multiplex families. Using genome wide single nucleotide polymorphism (SNP) data from the Collaborative Study of the Genetics of Alcoholism (COGA), we modified the original dataset into 30 total data sets in order to consider 6 different patterns of missing data for 5 different levels of SNP density. To assess power, we designed simulated traits based on existing marker genotypes. For the Type I error rate, we simulated 1,000 qualitative traits from random distributions, unlinked to any of the marker data. Overall, the different levels of SNP density examined here had only small effects on power (except sibpair data). Missing data had a substantial effect on power, with more completely genotyped pedigrees yielding the highest power (except sibpair data). Most of the missing data patterns did not cause large increases in the Type I error rate if the SNP markers were more than 0.3 cM apart. However, in a dense 0.25 cM map, removing genotypes on founders and/or founders and parents in the middle generation caused substantial inflation of the Type I error rate, which corresponded to the increasing proportion of persons with missing data. Results also showed that long high-LD blocks have severe effects on Type I error rates.
SNPs; Type I error rate; False Positives; Linkage Disequilibrium; Pedigree Structure
Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.
We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased.
In contrast to gene-mapping studies of simple Mendelian disorders, genetic analyses of complex traits are far more challenging, and high quality data management systems are often critical to the success of these projects. To minimize the difficulties inherent in complex trait studies, we have developed GeneLink, a Web-accessible, password-protected Sybase database.
GeneLink is a powerful tool for complex trait mapping, enabling genotypic data to be easily merged with pedigree and extensive phenotypic data. Specifically designed to facilitate large-scale (multi-center) genetic linkage or association studies, GeneLink securely and efficiently handles large amounts of data and provides additional features to facilitate data analysis by existing software packages and quality control. These include the ability to download chromosome-specific data files containing marker data in map order in various formats appropriate for downstream analyses (e.g., GAS and LINKAGE). Furthermore, an unlimited number of phenotypes (either qualitative or quantitative) can be stored and analyzed. Finally, GeneLink generates several quality assurance reports, including genotyping success rates of specified DNA samples or success and heterozygosity rates for specified markers.
GeneLink has already proven an invaluable tool for complex trait mapping studies and is discussed primarily in the context of our large, multi-center study of hereditary prostate cancer (HPC). GeneLink is freely available at .