Background

In spite of intensive efforts, understanding of the genetic aspects of familial prostate cancer remains largely incomplete. In a previous microsatellite-based linkage scan of 1233 prostate cancer (PC) families, we identified suggestive evidence for linkage (i.e. LOD≥1.86) at 5q12, 15q11, 17q21, 22q12, and two loci on 8p, with additional regions implicated in subsets of families defined by age at diagnosis, disease aggressiveness, or number of affected members.

Methods

In an attempt to replicate these findings and increase linkage resolution, we used the Illumina 6000 SNP linkage panel to perform a genome-wide linkage scan of an independent set of 762 multiplex PC families, collected by 11 ICPCG groups.

Results

Of the regions identified previously, modest evidence of replication was observed only on the short arm of chromosome 8, where HLOD scores of 1.63 and 3.60 were observed in the complete set of families and families with young average age at diagnosis, respectively. The most significant linkage signals found in the complete set of families were observed across a broad, 37 cM interval on 4q13-25, with LOD scores ranging from 2.02 to 2.62, increasing to 4.50 in families with older average age at diagnosis. In families with multiple cases presenting with more aggressive disease, LOD scores over 3.0 were observed at 8q24 in the vicinity of previously identified common PC risk variants, as well as MYC, an important gene in PC biology.

Conclusions

These results will be useful in prioritizing future susceptibility gene discovery efforts in this common cancer.

Background

We applied a new weighted pairwise shared genomic segment (pSGS) analysis for susceptibility gene localization to high-density genomewide SNP data in three extended high-risk breast cancer pedigrees.

Results

Using this method, four genomewide suggestive regions were identified on chromosomes 2, 4, 7 and 8, and a borderline suggestive region on chromosome 14. Seven additional regions with at least nominal evidence were observed. Of particular note among these total twelve regions were three regions that were identified in two pedigrees each; chromosomes 4, 7 and 14. Follow-up two-pedigree pSGS analyses further indicated excessive genomic sharing across the pedigrees in all three regions, suggesting that the underlying susceptibility alleles in those regions may be shared in common. In general, the pSGS regions identified were quite large (average 32.2 Mb), however, the range was wide (0.3 – 88.2 Mb). Several of the regions identified overlapped with loci and genes that have been previously implicated in breast cancer risk, including NBS1, BRCA1 and RAD51L1.

Conclusions

Our analyses have provided several loci of interest to pursue in these high-risk pedigrees and illustrate the utility of the weighted pSGS method and extended pedigrees for gene mapping in complex diseases. A focused sequencing effort across these loci in the sharing individuals is the natural next step to further map the critical underlying susceptibility variants in these regions.

Prostate cancer has a strong familial component but uncovering the molecular basis for inherited susceptibility for this disease has been challenging. Recently, a rare, recurrent mutation (G84E) in HOXB13 was reported to be associated with prostate cancer risk. Confirmation and characterization of this finding is necessary to potentially translate this information to the clinic. To examine this finding in a large international sample of prostate cancer families, we genotyped this mutation and 14 other SNPs in or flanking HOXB13 in 2,443 prostate cancer families recruited by the International Consortium for Prostate Cancer Genetics (ICPCG). At least one mutation carrier was found in 112 prostate cancer families (4.6 %), all of European descent. Within carrier families, the G84E mutation was more common in men with a diagnosis of prostate cancer (194 of 382, 51 %) than those without (42 of 137, 30 %), P = 9.9 × 10−8 [odds ratio 4.42 (95 % confidence interval 2.56–7.64)]. A family-based association test found G84E to be significantly over-transmitted from parents to affected offspring (P = 6.5 × 10−6). Analysis of markers flanking the G84E mutation indicates that it resides in the same haplotype in 95 % of carriers, consistent with a founder effect. Clinical characteristics of cancers in mutation carriers included features of high-risk disease. These findings demonstrate that the HOXB13 G84E mutation is present in ~5 % of prostate cancer families, predominantly of European descent, and confirm its association with prostate cancer risk. While future studies are needed to more fully define the clinical utility of this observation, this allele and others like it could form the basis for early, targeted screening of men at elevated risk for this common, clinically heterogeneous cancer.

Electronic supplementary material

The online version of this article (doi:10.1007/s00439-012-1229-4) contains supplementary material, which is available to authorized users.

Background

Multiple genome-wide and candidate gene association studies have been performed in search of common risk variants for breast cancer. Recent large meta analyses, consolidating evidence from these studies, have been consistent in highlighting the caspase-8 (CASP8) gene as important in this regard. In order to define a risk haplotype and map the CASP8 gene region with respect to underlying susceptibility variant/s, we screened four genes in the CASP8 region on 2q33-q34 for breast cancer risk.

Methods

Two independent data sets from the United Kingdom and the United States, including 3,888 breast cancer cases and controls, were genotyped for 45 tagging single nucleotide polymorphisms (tSNP) in the expanded CASP8 region. SNP and haplotype association tests were carried out using Monte Carlo based methods.

Results

We identified a three-SNP haplotype across rs3834129, rs6723097 and rs3817578 that was significantly associated with breast cancer (p<5×10−6), with a dominant risk ratio and 95% confidence interval of 1.28 (1.21–1.35) and frequency of 0.29 in controls. Evidence for this risk haplotype was extremely consistent across the two study sites and also consistent with previous data.

Conclusions

This three-SNP risk haplotype represents the best characterization so far of the chromosome upon which the susceptibility variant resides.

Impact

Characterization of the risk haplotype provides a strong foundation for re-sequencing efforts to identify the underlying risk variant, which may prove useful for individual-level risk prediction, and provide novel insights into breast carcinogenesis.

Background

Multiple genome-wide and candidate gene association studies have been performed in search of common risk variants for breast cancer. Recent large meta analyses, consolidating evidence from these studies, have been consistent in highlighting the caspase-8 (CASP8) gene as important in this regard. In order to define a risk haplotype and map the CASP8 gene region with respect to underlying susceptibility variant/s, we screened four genes in the CASP8 region on 2q33-q34 for breast cancer risk.

Methods

Two independent data sets from the United Kingdom and the United States, including 3,888 breast cancer cases and controls, were genotyped for 45 tagging single nucleotide polymorphisms (tSNP) in the expanded CASP8 region. SNP and haplotype association tests were carried out using Monte Carlo based methods.

Results

We identified a three-SNP haplotype across rs3834129, rs6723097 and rs3817578 that was significantly associated with breast cancer (p<5×10−6), with a dominant risk ratio and 95% confidence interval of 1.28 (1.21-1.35) and frequency of 0.29 in controls. Evidence for this risk haplotype was extremely consistent across the two study sites and also consistent with previous data.

Conclusions

This three-SNP risk haplotype represents the best characterization so far of the chromosome upon which the susceptibility variant resides.

Impact

Characterization of the risk haplotype provides a strong foundation for re-sequencing efforts to identify the underlying risk variant, which may prove useful for individual-level risk prediction, and provide novel insights into breast carcinogenesis.

Background

Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive.

Methods

Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed.

Results

Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded.

Conclusions

Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.

We applied a shared genomic segment (SGS) analysis, incorporating an error model, to identify complete, or near complete, selective sweeps in the HapMap phase II data sets. This method is based on detecting heterozygous sharing across all individuals within a population, to identify regions of sharing with at least one allele in common. We identified multiple interesting regions, many of which are concordant with positive selection regions detected by previous population genetic tests. Others are suggested to be novel regions. Our finding illustrates the utility of SGS as a method for identifying regions of selection, and some of these regions have been proposed to be candidate regions for harboring disease genes.

Compelling evidence supports a genetic component to prostate cancer (PC) susceptibility and aggressiveness. Recent genome-wide association studies (GWAS) have identified >30 single nucleotide polymorphisms associated with PC susceptibility. It remains unclear, however, whether such genetic variants are associated with disease aggressiveness—one of the most important questions in PC research today. To help clarify this and substantially expand research in the genetic determinants of PC aggressiveness, the first National Cancer Institute Prostate Cancer Genetics Workshop assembled researchers to develop plans for a large new research consortium and patient cohort. The workshop reviewed the prior work in this area and addressed the practical issues in planning future studies. With new DNA sequencing technology, the potential application of sequencing information to patient care is emerging. The workshop, therefore, included state-of-the-art presentations by experts on new genotyping technologies, including sequencing and associated bioinformatics issues, which are just beginning to be applied to cancer genetics.

Background

Total cholesterol was among the earliest identified risk factors for coronary heart disease (CHD). We sought to identify genetic variants in six genes associated with lipid metabolism and estimate their respective contribution to risk for CHD.

Methods

For 6 lipid-associated genes (LCAT, CETP, LIPC, LPL, SCARB1, and ApoF) we scanned exons, 5′ and 3′ untranslated regions, and donor and acceptor splice sites for variants using Hi-Res Melting® curve analysis (HRMCA) with confirmation by cycle sequencing. Healthy subjects were used for SNP discovery (n=64), haplotype determination/tagging SNP discovery (n=339), and lipid association testing (n=786).

Results

In 17,840 bases of interrogated sequence, 90 variant SNPs were identified; 19 (21.1%) previously unreported. Thirty-four variants (37.8%) were exonic(16 non-synonymous), 28 (31.1%) in intron-exon boundaries, and 28 (31.1%) in the 5′ and 3′ untranslated regions. Compared to cycle sequencing, HRMCA had sensitivity of 99.4% and specificity of 97.7%. Tagging SNPs (n=38) explained >90% of the variation in the 6 genes and identified linkage disequilibrium (LD) groups. Significant beneficial lipid profiles were observed for CETP LD group 2, LIPC LD groups 1 and 7, and SCARB1 LD groups 1, 3 and 4. Risk profiles worsened for CETP LD group 3, LPL LD group 4.

Conclusions

These findings demonstrate the feasibility, sensitivity, and specificity of HRMCA for SNP discovery. Variants identified in these genes may be used to predict lipid-associated risk and reclassification of clinical CHD risk.

We applied our method of pairwise shared genomic segment (pSGS) analysis to high-risk pedigrees identified from the Genetic Analysis Workshop 17 (GAW17) mini-exome sequencing data set. The original shared genomic segment method focused on identifying regions shared by all case subjects in a pedigree; thus it can be sensitive to sporadic cases. Our new method examines sharing among all pairs of case subjects in a high-risk pedigree and then uses the mean sharing as the test statistic; in addition, the significance is assessed empirically based on the pedigree structure and linkage disequilibrium pattern of the single-nucleotide polymorphisms. Using all GAW17 replicates, we identified 18 unilineal high-risk pedigrees that contained excess disease (p < 0.01) and at least 15 meioses between case subjects. Eighteen rare causal variants were polymorphic in this set of pedigrees. Based on a significance threshold of 0.001, 72.2% (13/18) of these pedigrees were successfully identified with at least one region that contains a true causal variant. The regions identified included 4 of the possible 18 polymorphic causal variants. On average, 1.1 true positives and 1.7 false positives were identified per pedigree. In conclusion, we have demonstrated the potential of our new pSGS method for localizing rare disease causal variants in common disease using high-risk pedigrees and exome sequence data.

Compelling evidence supports a genetic component to prostate cancer (PC) susceptibility and aggressiveness. Recent genome-wide association studies (GWAS) have identified >30 single nucleotide polymorphisms associated with PC susceptibility. It remains unclear, however, whether such genetic variants are associated with disease aggressiveness—one of the most important questions in PC research today. To help clarify this and substantially expand research in the genetic determinants of PC aggressiveness, the first National Cancer Institute Prostate Cancer Genetics Workshop assembled researchers to develop plans for a large new research consortium and patient cohort. The workshop reviewed the prior work in this area and addressed the practical issues in planning future studies. With new DNA sequencing technology, the potential application of sequencing information to patient care is emerging. The workshop, therefore, included state-of-the-art presentations by experts on new genotyping technologies, including sequencing and associated bioinformatics issues, which are just beginning to be applied to cancer genetics.

Summary

Monoclonal B-cell lymphocytosis (MBL) is an asymptomatic haematological condition characterized by low absolute levels of B-cell clones with a surface immunophenotype similar to that of chronic lymphocytic leukaemia (CLL). In the general population, MBL increases with age with a prevalence of 5–9% in individuals over age 60 years. It has been reported to be higher among first-degree relatives from CLL families. We report results of multi-parameter flow cytometry among 505 first-degree relatives with no personal history of lymphoproliferative disease from 140 families having at least two cases of CLL. Seventeen percent of relatives had MBL. Age was the most important determinant where the probability for developing MBL by age 90 years was 61%. MBL clustered in certain families but clustering was independent of the number of known CLL cases in a family. As is the case with CLL, males had a significantly higher risk for MBL than did females (p=0.04). MBL patients had significantly higher mean absolute lymphocyte counts (2.4 × 109/l) and B-cell counts (0.53 × 109/l) than those with a normal B-cell immunophenotype. Our findings show that MBL occurs at a very high rate in high risk CLL families. Both the age and gender distribution of MBL are parallel to CLL, implying a shared inherited risk.

Objective

To discover common variants in 6 lipid metabolic genes and construct and validate a genetic risk score (GRS) based on the joint effects of genetic variants in multiple genes from lipid and other pathobiologic pathways.

Background

Explaining the genetic basis of coronary artery disease (CAD) is incomplete. Discovery and aggregation of genetic variants from multiple pathways may advance this objective.

Methods

Premature CAD cases (N=1,918) and CAD-free controls (N=1,032) were selected from our angiographic registry. In a discovery phase, single nucleotide polymorphisms (SNPs) at 56 loci from internal discovery and external reports were tested for associations with biomarkers and CAD: 28 promising SNPs were then tested jointly for CAD associations, and a genetic risk score (GRS) consisting of SNPs contributing independently was constructed and validated in a replication set of familial cases and population-based controls (N=1,320).

Results

Five variants contributed jointly to CAD prediction in a multigenic GRS model: odds ratio (OR) =1.24 (95% CI 1.16–1.33) per risk allele, p=8.2×10−11, adjusted OR=2.03 (1.53–2.70), 4th vs. 1st quartile. GRS5 had minor impact on area under the receiver-operating characteristic curve (p>0.05) but resulted in substantial net reclassification improvement: 0.16 overall, 0.28 in intermediate risk patients (both p<0.0001). GRS5 predicted familial CAD with similar magnitude in the validation set.

Conclusions

CorGen demonstrates the ability of a multigenic, multipathway GRS to improve discrimination of angiographic CAD. Genetic risk scores promise to increase understanding of the genetic basis of CAD and improve identification of individuals at increased CAD risk.

Background

There is strong and consistent evidence that a genetic component contributes to the etiology of chronic lymphocytic leukemia (CLL). A recent genome-wide association study (GWAS) of CLL identified 7 genetic variants that increased the risk of CLL within a European population.

Methods

We evaluated the association of these variants, or variants in linkage disequilibrium (LD) with these variants, with CLL risk in an independent sample of 438 CLL cases and 328 controls.

Results

Of these 7 SNPs, 6 had p-trend < 0.05 and had estimated odds ratios (ORs) that were strikingly comparable to those of the previous study. Associations were seen for rs9378805 (OR = 1.47, 95% CI: 1.19, 1.80, p-trend = 0.0003) near IRF4 and rs735665 near GRAMD1B (OR= 1.47; 95% CI: 1.14, 1.89; p-trend = 0.003). However, no associations (P> 0.05) were found for rs11083846, nor were any found for any SNPs in LD with rs11083846.

Conclusions

Our results confirm the previous findings and further support the role of a genetic basis in the etiology of CLL; however, more research is needed to elucidate the causal SNP(s) and the potential manner in which these SNPs or linked SNPs function in CLL pathogenesis.

Tourette Syndrome (TS) is a neuropsychiatric disorder characterized by multiple motor and phonic tics. The heritability of TS has been well established, yet there is a lack of consensus in genome-wide linkage studies. The purpose of this study was to conduct a genome-wide linkage analysis on a unique, large, high-risk TS Utah pedigree. We examined a qualitative trait (TS1) where cases had a definitive diagnosis of TS as observed by a clinical interviewer (n=66) and a quantitative phenotype based on the total Yale global motor and phonic tic severity scores (n=102). Both parametric and non-parametric multipoint linkage analyses based on MCMC methods were performed using a 10cM spaced micro-satellite autosomal marker set. Two regions of interest were identified under affecteds-only recessive models; a LOD score of 3.3 on chromosome 1p for Yale tic severity and a LOD score of 3.1 on chromosome 3p for the TS1 phenotype. Twenty-seven individuals shared linked segregating haplotypes for the 1p region. They had significantly higher Yale tic phonic scores than non-sharers (p=0.01). There were 46 haplotype sharers on chromosome 3p with significantly higher percentage of females among these individuals compared to the non-sharers (p=0.03). The significant linkage peaks on chromosomes 1p and 3p are in new areas of the genome for TS, and replication of these findings is necessary.

Background

Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.

Results

Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size.

Conclusions

We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.

Summary: It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene–gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene–gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene–gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene–gene effects that otherwise is currently unavailable.

Availability: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html

Contact: ryan.abo@hsc.utah.edu

We examine the utility of high density genotype assays for predisposition gene localization using extended pedigrees. Results for the distribution of the number and length of genomic segments shared identical by descent among relatives previously derived in the context of genomic mismatch scanning are reviewed in the context of dense single nucleotide polymorphism maps. We use long runs of loci at which cases share a common allele identically by state to localize hypothesized predisposition genes. The distribution of such runs under the hypothesis of no genetic effect is evaluated by simulation. Methods are illustrated by analysis of an extended prostate cancer pedigree previously reported to show significant linkage to chromosome 1p23. Our analysis establishes that runs of simple single locus statistics can be powerful, tractable and robust for finding DNA shared between relatives, and that extended pedigrees offer powerful designs for gene detection based on these statistics.

Graft-versus-host disease (GVHD) is the major cause of morbidity and mortality after allogeneic hematopoietic cell transplantation. From a genetic perspective, GVHD is a complex phenotypic trait. While it is understood that susceptibility results from interacting polymorphisms of genes encoding histocompatibility antigens and immune regulatory molecules, a detailed and integrative understanding of the genetic background underlying GVHD remains lacking. To gain insight regarding these issues, we performed a forward genetic study. A MHC-matched mouse model was utilized in which irradiated recipient BALB.K and B10.BR mice demonstrate differential susceptibility to lethal GHVD when transplanted using AKR/J donors. Assessment of GVHD in (B10.BR x BALB.K)F1 mice revealed that susceptibility is a dominant trait and conferred by deleterious alleles from the BALB.K strain. In order to identify the alleles responsible GVHD susceptibility, a genome scanning approach was taken using (B10.BR x BALB.K)F1 × B10.BR backcross mice as recipients. A major susceptibility locus, termed the Gvh1 locus, was identified on chromosome 16 using linkage analysis (LOD = 9.1). A second locus was found on chromosome 13, named Gvh2, which had additive but protective effects. Further identification of Gvh genes by positional cloning may yield new insight into genetic control mechanisms regulating GVHD and potentially reveal novel approaches for effective GVHD therapy.

Background

Autism Spectrum Disorders (ASD) are phenotypically heterogeneous, characterized by impairments in the development of communication and social behaviour and the presence of repetitive behaviour and restricted interests. Dissecting the genetic complexity of ASD may require phenotypic data reflecting more detail than is offered by a categorical clinical diagnosis. Such data are available from the Social Responsiveness Scale (SRS) which is a continuous, quantitative measure of social ability giving scores that range from significant impairment to above average ability.

Methods

We present genome-wide results for 64 multiplex and extended families ranging from two to nine generations. SRS scores were available from 518 genotyped pedigree subjects, including affected and unaffected relatives. Genotypes from the Illumina 6 k single nucleotide polymorphism panel were provided by the Center for Inherited Disease Research. Quantitative and qualitative analyses were done using MCLINK, a software package that uses Markov chain Monte Carlo (MCMC) methods to perform multilocus linkage analysis on large extended pedigrees.

Results

When analysed as a qualitative trait, linkage occurred in the same locations as in our previous affected-only genome scan of these families, with findings on chromosomes 7q31.1-q32.3 [heterogeneity logarithm of the odds (HLOD) = 2.91], 15q13.3 (HLOD = 3.64), and 13q12.3 (HLOD = 2.23). Additional positive qualitative results were seen on chromosomes 6 and 10 in regions that may be of interest for other neuropsychiatric disorders. When analysed as a quantitative trait, results replicated a peak found in an independent sample using quantitative SRS scores on chromosome 11p15.1-p15.4 (HLOD = 2.77). Additional positive quantitative results were seen on chromosomes 7, 9, and 19.

Conclusions

The SRS linkage peaks reported here substantially overlap with peaks found in our previous affected-only genome scan of clinical diagnosis. In addition, we replicated a previous SRS peak in an independent sample. These results suggest the SRS is a robust and useful phenotype measure for genetic linkage studies of ASD. Finally, analyses of SRS scores revealed linkage peaks overlapping with evidence from other studies of neuropsychiatric diseases. The information available from the SRS itself may, therefore, reveal locations for autism susceptibility genes that would not otherwise be detected.

A recent genome-wide association (GWA) study suggested seven new loci as associated with prostate cancer (PRCA) susceptibility. The strongest associated SNP in each region was identified (rs2660753, rs9364554, rs6465657, rs10993994, rs7931342, rs2735839, rs5945619). We studied these seven SNPs in a replication study consisting of 169 familial PRCA cases selected from Utah high-risk PRCA pedigrees and 805 controls. We performed subset analyses for aggressive and early onset PRCA. At a nominal significance level, two SNPs were found to be associated with PRCA: rs10993994 on chromosome 10q11 (odds ratio (OR) =1.42 [95% confidence interval (CI), 1.05–1.90], p=0.022); and rs5945619 on chromosome Xp11 (OR=1.54 [95% CI, 1.03–2.31], p=0.035). Restricting analysis to familial PRCA cases with aggressive disease yielded very similar risk estimates at both SNPs. However, subset analysis for familial, early onset disease indicated highly significant association evidence and substantially higher risk estimates for rs10993994 (OR=2.20 [95% CI, 1.48–3.27], p<0.0001). This result suggests that the higher risk estimates from the stage 1 cohort in the original study for rs10993994 may have been due to the early-onset and familial nature of the PRCA cases in that cohort. In conclusion, in a small case-control study of PRCA cases from Utah high-risk pedigrees, we have significantly replicated association of PRCA with rs10993994 (10q11) upon study-wide correction for multiple comparisons. We also nominally replicated the association of PRCA with rs5945619 (Xp11). In particular, it appears that the susceptibility locus at 10q11 maybe involved in familial, early onset disease.

Polymorphisms in double-strand DNA repair gene XRCC2 may play an important role in colorectal cancer (CRC) etiology, specifically in disease subtypes. Associations of XRCC2 variants and CRC were investigated by tumor site and tumor instability status in a four-center collaboration including three U.K. case-control studies (Sheffield, Leeds, Dundee) and a U.S. case-control study of cases from high-risk Utah pedigrees (total: 1,252 cases, 1,422 controls). The 14 variants studied were tagging-SNPs selected from HapMap/NIEHS data, supplemented with SNPs identified from sequencing of 125 cases chosen to represent multiple CRC groups (familial, metastatic disease, and tumor subsite). Monte Carlo significance testing using Genie software provided valid meta analyses of the total resource that includes family-based data. Similar to reports of CRC and other cancer sites, the rs3218536 R188H allele was not associated with increased risk. However, we observed a novel, highly significant association of a common SNP, rs3218499G>C, with increased risk of rectal tumors (OR 2.1, 95%CI 1.3-3.3; pchisq. =0.0006) versus controls, with the largest risk found for female rectal cases (OR 3.1, 95%CI 1.6-6.1; pchisq. =0.0006). This difference was significantly different to that for proximal and distal colon cancers (pchisq. =0.02). Our investigation supports a role for XRCC2 in CRC tumorigenesis, conferring susceptibility to rectal tumors.

Background

Genomewide association studies of colorectal cancer (CRC) have identified genetic variants that reproducibly associate with CRC. Associations of twelve SNPs at 8q24, 9p24, and 18q21 (SMAD7) and CRC were investigated in a three center collaborative study including two UK case-control cohorts (Sheffield and Leeds) and an American case-control study of CRC cases from high-risk Utah pedigrees.

Methods

Our combined resource included 1,092 CRC case subjects and 1,060 age- and sex-matched controls. Meta-statistics and Monte Carlo significance testing using Genie software provided a valid combined analysis of our mixed independent and related case-control resource. We also evaluated whether these associations differed by sex, age at diagnosis, family history, or tumor site.

Results

At 8q24 we observed two independent significant associations at SNPs located in two different risk regions of 8q24: rs6983267 in region 3 (ptrend=0.01; per allele OR=1.17, 95%CI 1.03, 1.32) and rs10090154 in region 5 (ptrend=0.05; per allele OR=1.24, 95%CI 1.01, 1.51). At 18q21 associations were observed in distal colon tumors, but not in proximal or rectal cancers: rs4939827 (ptrend=0.007; per allele OR=0.77, 95%CI 0.64, 0.93; case-case pdiff=0.03) and rs12953717 (ptrend=0.01; per allele OR=1.27, 95%CI 1.06, 1.52). We were unable to detect any associations at 9p24 with CRC.

Conclusions

Our investigation confirms that variants across multiple risk regions of 8q24 are associated with CRC, and that associations at 18q21 differ by tumor site.

Background

Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. In this paper, we evaluate a weighted and an unweighted approach for estimating the combined effect of multiple markers (using genotypes and haplotypes) on lipid levels for a given individual.

Methods

Using data from the Framingham Heart Study SHARe genome-wide association study, we tested genome-wide genotypes and haplotypes for association with lipid levels and constructed genetic risk scores (GRS) based on multiple markers that were weighted according to their estimated effects on LDL-C, HDL-C, and TG. These scores (GRS-LDL, GRS-HDL, and GRS-TG) were then evaluated for associations with LDL-C, HDL-C, and TG, and compared with results of an unweighted method based on risk-allele counts. For comparability of metrics, GRS variables were divided into quartiles.

Results

GRS-LDL quartiles were associated with LDL-C levels (p = 2.1 × 10-24), GRS-HDL quartiles with HDL-C (p = 5.9 × 10-22), and GRS-TG quartiles with TG (p = 5.4 × 10-25). In comparison, these p-values were considerably lower than those for the associations of the unweighted GRS quartiles for LDL-C (p = 3.6 × 10-7), HDL-C (p = 6.4 × 10-16), and TG (p = 4.1 × 10-10).

Conclusion

GRS variables were highly predictive of LDL-C, HDL-C, and TG measurements, especially when weighted based on each marker's individual association with those intermediate risk phenotypes. The allele-count GRS approach that does not weight the GRS by individual marker associations was considerably less predictive of lipid and lipoprotein measures when the same genetic markers were utilized, suggesting that substantially more risk-associated genetic marker information is encapsulated by the weighted GRS variables.

Objective

Methods exist to appropriately perform association analyses in pedigrees. However, for genome-wide association analysis, these methods are computationally impractical. It is therefore important to determine alternate methods that can be efficiently used genome-wide. Here, we introduce a new algorithm that considers all relationships simultaneously in arbitrary-structured pedigrees and assigns weights to pedigree members that can be used in subsequent analyses to address relatedness. We compare this new method with an existing weighting algorithm, a naïve analysis (relatedness is ignored), and an empirical method that appropriately accounts for all relationships (the gold standard).

Methods

Framingham Heart Study Genetic Analysis Workshop 16 Problem 2 data were used with a dichotomous phenotype based on high-density lipoprotein cholesterol level (1,611 cases and 4,043 controls). New and existing algorithms for calculating weights were used. Cochran-Armitage trend tests were performed for 17,333 single-nucleotide polymorphisms on chromosome 8 using both weighting systems and the naïve approach; a subset of 500 single-nucleotide polymorphisms were tested empirically. Correlations of p-values from each method were determined.

Results

Results from the two weighting methods were strongly correlated (r = 0.96). Our new weighting method performed better than the existing weighting method (r = 0.89 vs. r = 0.83), which is due to a more moderate down-weighting. The naive analysis obtained the best correlation with the empirical gold standard results (r = 0.99).

Conclusion

Our results suggest that weighting methods do not accurately represent tests that account for familial relationships in genetic association analyses and are inferior to the naïve method as an efficient initial genome-wide screening tool.