Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current ‘next-generation’ DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.

Purpose

Despite many years of research, most of the genetic factors contributing to myopia development remain unknown. Genetic studies have pointed to a strong inherited component, but although many candidate regions have been implicated, few genes have been positively identified.

Methods

We have previously reported 2 genomewide linkage scans in a population of 63 highly aggregated Ashkenazi Jewish families that identified a locus on chromosome 22. Here we used ordered subset analysis (OSA), conditioned on non-parametric linkage to chromosome 22 to detect other chromosomal regions which had evidence of linkage to myopia in subsets of the families, but not the overall sample.

Results

Strong evidence of linkage to a 19-cM linkage interval with a peak OSA nonparametric allele-sharing logarithm-of-odds (LOD) score of 3.14 on 20p12-q11.1 (ΔLOD=2.39, empirical p=0.029) was identified in a subset of 20 families that also exhibited strong evidence of linkage to chromosome 22. One other locus also presented with suggestive LOD scores >2.0 on chromosome 11p14-q14 and one locus on chromosome 6q22-q24 had an OSA LOD score=1.76 (ΔLOD=1.65, empirical p=0.02).

Conclusions

The chromosome 6 and 20 loci are entirely novel and appear linked in a subset of families whose myopia is known to be linked to chromosome 22. The chromosome 11 locus overlaps with the known Myopia-7 (MYP7, OMIM 609256) locus. Using ordered subset analysis allows us to find additional loci linked to myopia in subsets of families, and underlines the complex genetic heterogeneity of myopia even in highly aggregated families and genetically isolated populations such as the Ashkenazi Jews.

In this study, we observed loss of heterozygosity (LOH) in human chromosomal fragment 6q25.1 in sporadic lung cancer patients. LOH was observed in 65% of the 26 lung tumors examined and was narrowed down to a 2.2-Mb region. Single-nucleotide polymorphism (SNP) analysis of genes located within this region identified a candidate gene, termed p34. This gene, also designated as ZC3H12D, C6orf95, FLJ46041, or dJ281H8.1, carries an A/G nonsynonymous SNP at codon 106, which alters the amino acid from lysine to arginine. Nearly 73% of heterozygous lung cancer tissues with LOH and the A/G SNP also exhibited loss of the A allele. In vitro clonogenic and in vivo nude mouse studies showed that overexpression of the A allele exerts tumor suppressor function compared with the G allele. p34 is located within a recently mapped human lung cancer susceptibility locus, and association of the p34 A/G SNP was tested among these families. No significant association between the less frequent G allele and lung cancer susceptibility was found. Our results suggest that p34 may be a novel tumor suppressor gene involved in sporadic lung cancer but it seems not to be the candidate familial lung cancer susceptibility gene linked to chromosomal region 6q23-25.

The use of tyrosine kinase inhibitors (TKI) has yielded great success in treatment of lung adenocarcinomas. However, patients who develop resistance to TKI treatment often acquire a somatic resistance mutation (T790M) located in the catalytic cleft of the epidermal growth factor receptor (EGFR) enzyme. Recently, a report describing EGFR-T790M as a germ-line mutation suggested that this mutation may be associated with inherited susceptibility to lung cancer. Contrary to previous reports, our analysis indicates that the T790M mutation confers increased Y992 and Y1068 phosphorylation levels. In a human bronchial epithelial cell line, overexpression of EGFR-T790M displayed a growth advantage over wild-type (WT) EGFR. We also screened 237 lung cancer family probands, in addition to 45 bronchoalveolar tumors, and found that none of them contained the EGFR-T790M mutation. Our observations show that EGFR-T790M provides a proliferative advantage with respect to WT EGFR and suggest that the enhanced kinase activity of this mutant is the basis for rare cases of inherited susceptibility to lung cancer.

Strong epidemiologic evidence links smoking and cancer. An increased understanding of the molecular biology of tobacco-related cancers could advance progress toward improving smoking cessation and patient management. Knowledge gaps between tobacco addiction, tumorigenesis, and cancer brought an interdisciplinary group of investigators together to discuss “The Biology of Nicotine and Tobacco: Bench to Bedside.” Presentations on the signaling pathways and pathogenesis in tobacco-related cancers, mouse models of addiction, imaging and regulation of nicotinic receptors, the genetic basis for tobacco carcinogenesis and development of lung cancer, and molecular mechanisms of carcinogenesis were heard. Importantly, new opportunities to use molecular biology to identify and abrogate tobacco-mediated carcinogenesis and to identify high-risk individuals were recognized.

Background

Prostate cancer is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity.

Methods

We performed a secondary analysis of 1233 prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics (ICPCG) using two novel statistics, the sumLINK and sumLOD. For both statistics, dominant and recessive genetic models were considered. False discovery rate (FDR) analysis was conducted to assess the effects of multiple testing.

Results

Our analysis identified significant linkage evidence at chromosome 22q12, confirming previous findings by the initial conventional analyses of the same ICPCG data. Twelve other regions were identified with genomewide suggestive evidence for linkage. Seven regions (1q23, 5q11, 5q35, 6p21, 8q12, 11q13, 20p11-q11) are near loci previously identified in the initial ICPCG pooled data analysis or the subset of aggressive prostate cancer (PC) pedigrees. Three other regions (1p12, 8p23, 19q13) confirm loci reported by others, and two (2p24, 6q27) are novel susceptibility loci. FDR testing indicates that over 70% of these results are likely true positive findings. Statistical recombinant mapping narrowed regions to an average of 9 cM.

Conclusions

Our results represent genomic regions with the greatest consistency of positive linkage evidence across a very large collection of high-risk prostate cancer pedigrees using new statistical tests that deal powerfully with heterogeneity. These regions are excellent candidates for further study to identify prostate cancer predisposition genes.

Recently, a nonsense alteration Trp149Stop in the ARLTS1 gene was found more frequently in familial cancer cases vs. sporadic cancer patients and healthy controls. Here, the role of Trp149Stop or any other ARLTS1 germline variant was evaluated on breast, prostate, and colorectal cancer risk. The whole gene was screened for germline alterations in 855 familial cancer patients. The five observed variants were further screened in 1169 non-familial cancer patients as well as in 809 healthy population controls. The Trp149Stop was found at low frequencies (0.5–1.2%) in all patient subgroups vs. 1.6% in controls, and the mutant allele did not co-segregate with disease status in families with multiple affected individuals. The CC genotype in the Cys148Arg variant was slightly more common among both familial and sporadic breast (OR=1.48, 95% CI 1.16–1.87, p=0.001) and prostate cancer patients (OR 1.50, 95% CI 1.13–1.99, p=0.005) when compared to controls. A novel ARLTS1 variant Gly65Val was found at higher frequency among familial prostate cancer patients (8/164, 4.9%) than in controls (13/809, 1.6%; OR 3.14, 95% CI 1.28–7.70, p=0.016). However, after adjusting for multiple testing, none of these results were still significant. No association was found with any of the variants and colorectal cancer risk. Our results suggest that Trp149Stop is not a predisposition allele in breast, prostate, or colorectal cancer in the Finnish population, and, while the Gly65Val variant may increase familial prostate cancer risk and the Cys148Arg change may affect both breast and prostate cancer risk, the evidence is not strong in these data.

Background

Prostate cancer is a genetically complex disease with locus and disease heterogeneity. The RNASEL gene and HPCX locus have been implicated in hereditary prostate cancer; however, their contributions to sporadic forms of this malignancy remain uncertain.

Methods

Associations of prostate cancer with two variants in the RNASEL gene (a founder mutation, 471delAAAG, and a non-synonymous SNP, rs486907), and with five microsatellite markers in the HPCX locus, were examined in 979 cases and 1,251 controls of Ashkenazi Jewish descent. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression models.

Results

There was an inverse association between RNASEL rs486907 and prostate cancer in younger men (<65 years) and those with a first-degree relative with prostate cancer; men with AA genotype had ORs of 0.64 and 0.47 (both p<0.05), respectively, in comparison to men with GG genotype. Within the HPCX region, there were positive associations for allele 135 of bG82i1.1 marker (OR=1.77, p=0.01) and allele 188 of DXS1205 (OR=1.65, p=0.02). In addition, allele 248 of marker D33 was inversely associated (OR=0.65, p=0.05) with Gleason score ≥7 tumors.

Conclusions

Results suggest that variants in RNASEL contribute to susceptibility to early onset and familial forms of prostate cancer, whereas HPCX variants are associated with prostate cancer risk and tumor aggressiveness. The observation that a mutation predicted to completely inactivate RNASEL protein was not associated with prostate cancer, but that a missense variant was associated, suggests that the effect is due to either partial inactivation of the protein, and/or acquisition of a new protein activity.

Purpose

Deficiency of prolyl 3-hydroxylase 1, encoded by LEPRE1, causes recessive osteogenesis imperfecta. We previously identified a LEPRE1 mutation, exclusively in African Americans and contemporary West Africans. We hypothesized that this allele originated in West Africa and was introduced to the Americas with the Atlantic slave trade. We aimed to determine the frequency of carriers for this mutation among African Americans and West Africans, and the mutation origin and age.

Methods

Genomic DNA was screened for the mutation using PCR and restriction digestion, and a custom TaqMan genomic SNP assay. The mutation age was estimated using microsatellites and short tandem repeats spanning 4.2 Mb surrounding LEPRE1 in probands and carriers.

Results

Approximately 0.4% of Mid-Atlantic African Americans carry this mutation, estimating recessive OI in 1/260,000 births in this population. In Nigeria and Ghana, 1.48% of unrelated individuals are heterozygous carriers, predicting 1/18,260 births will be affected with recessive OI, equal to the incidence of de novo dominant OI. The mutation was not detected in Africans from surrounding countries. All carriers shared a haplotype of 63-770 Kb, consistent with a single founder for this mutation. Using linkage disequilibrium analysis, the mutation was estimated to have originated between 650 and 900 years before present (1100-1350 C.E.).

Conclusions

We identified a West African founder mutation for recessive OI in LEPRE1. Nearly 1.5% of Ghanians and Nigerians are carriers. The age of this allele is consistent with introduction to North America via the Atlantic slave trade (1501 – 1867 C.E).

A common variant on chromosomal region 15q24–25.1, marked by rs1051730, was found to be associated with lung cancer risk. Here, we attempted to confirm the second variant on 15q24–25.1 in several large sporadic lung cancer populations and determined what percentage of additional risk for lung cancer is due to the genetic effect of the second variant. SNPs rs1051730 and rs481134 were genotyped in 2,818 lung cancer cases and 2,766 controls from four populations. Joint analysis of these two variants (rs1051730 and rs481134) on 15q24–25.1 identified three major haplotypes (G_T, A_C, and G_C) and provided stronger evidence for association of 15q24–25.1 with lung cancer (P = 9.72 × 10−9). These two variants represent three levels of risk associated with lung cancer. The most common haplotype G_T is neutral; the haplotype A_C is associated with increased risk for lung cancer with 5.0% higher frequency in cases than in controls [P = 1.68 × 10−7; odds ratio (OR), 1.24; 95% confidence interval (95% CI), 1.14–1.35]; whereas the haplotype G_C is associated with reduced risk for lung cancer with 4.4% lower frequency in cases than in controls (P = 7.39 × 10−7; OR, 0.80; 95% CI, 0.73–0.87). We further showed that these two genetic variants on 15q24–25.1 independently influence lung cancer risk (rs1051730: P = 4.42 × 10−11; OR, 1.60; 95% CI, 1.46–1.74; rs481134: P = 7.01 × 10−4; OR, 0.81; 95% CI, 0.72–0.92). The second variant on 15q24–25.1, marked by rs481134, explains an additional 13.2% of population attributable risk for lung cancer.

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.

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.

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.

Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment and a description of available software resources for these methods.

This study of refraction and refractive errors confirmed previously reported linkages on 3q36, 7p15, 7q36, and 22q1, verifying that genes in these regions play an important role in the development of refraction errors.

Purpose.

Refraction, as measured by spherical equivalent, is the need for an external lens to focus images on the retina. While genetic factors play an important role in the development of refractive errors, few susceptibility genes have been identified. However, several regions of linkage have been reported for myopia (2q, 4q, 7q, 12q, 17q, 18p, 22q, and Xq) and for quantitative refraction (1p, 3q, 4q, 7p, 8p, and 11p). To replicate previously identified linkage peaks and to identify novel loci that influence quantitative refraction and refractive errors, linkage analysis of spherical equivalent, myopia, and hyperopia in the Beaver Dam Eye Study was performed.

Methods.

Nonparametric, sibling-pair, genome-wide linkage analyses of refraction (spherical equivalent adjusted for age, education, and nuclear sclerosis), myopia and hyperopia in 834 sibling pairs within 486 extended pedigrees were performed.

Results.

Suggestive evidence of linkage was found for hyperopia on chromosome 3, region q26 (empiric P = 5.34 × 10−4), a region that had shown significant genome-wide evidence of linkage to refraction and some evidence of linkage to hyperopia. In addition, the analysis replicated previously reported genome-wide significant linkages to 22q11 of adjusted refraction and myopia (empiric P = 4.43 × 10−3 and 1.48 × 10−3, respectively) and to 7p15 of refraction (empiric P = 9.43 × 10−4). Evidence was also found of linkage to refraction on 7q36 (empiric P = 2.32 × 10−3), a region previously linked to high myopia.

Conclusions.

The findings provide further evidence that genes controlling refractive errors are located on 3q26, 7p15, 7p36, and 22q11.

Background

Genetic susceptibility for cancer can differ substantially among families. We use trait-related covariates to identify a genetically homogeneous subset of families with the best evidence for linkage in the presence of heterogeneity.

Methods

We performed a genome-wide linkage screen in 93 families. Samples and data were collected by the familial lung cancer recruitment sites of the Genetic Epidemiology of Lung Cancer Consortium. We estimated linkage scores for each family by the Markov chain Monte Carlo procedure using SimWalk2 software. We used ordered subset analysis (OSA) to identify genetically homogenous families by ordering families based on a disease-associated covariate. We performed permutation tests to determine the relationship between the trait-related covariate and the evidence for linkage.

Results

A genome-wide screen for lung cancer loci identified strong evidence for linkage to 6q23-25 and suggestive evidence for linkage to 12q24 using OSA, with peak logarithm of odds (LOD) scores of 4.19 and 2.79, respectively. We found other chromosomes also suggestive for linkages, including 5q31-q33, 14q11, and 16q24.

Conclusions

Our OSA results support 6q as a lung cancer susceptibility locus and provide evidence for disease linkage on 12q24. This study further increased our understanding of the inheritability for lung cancer. Validation studies using larger sample size are needed to verify the presence of several other chromosomal regions suggestive of an increased risk for lung cancer and/or other cancers.

Impact

OSA can reduce genetic heterogeneity in linkage study and may assist in revealing novel susceptibility loci.

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

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.

Purpose

To replicate the previous association of single nucleotide polymorphisms (SNPs) with risk of intracranial aneurysm (IA) and to examine the relationship of smoking with these variants and the risk of IA.

Methods

White probands with an IA from families with multiple affected members were identified by 26 clinical centers located throughout North America, New Zealand, and Australia. White controls free of stroke and IA were selected by random digit dialing from the Greater Cincinnati population. SNPs previously associated with IA on chromosome 2, 8, and 9 were genotyped using a TaqMan assay or were included in the Affymetrix 6.0 array that was part of a genome-wide association study of 406 IA cases and 392 controls. Logistic regression modeling tested whether the association of replicated SNPs with IA was modulated by smoking.

Results

The strongest evidence of association with IA was found with the 8q SNP rs10958409 (genotypic P = 9.2 × 10-5; allelic P = 1.3 × 10-5; OR = 1.86, 95% CI: 1.40−2.47). We also replicated association with both SNPs on chromosome 9p, rs1333040 and rs10757278, but were not able to replicate the previously reported association of the two SNPs on chromosome 2q. Statistical testing showed a multiplicative relationship between the risk alleles and smoking with regard to the risk of IA.

Conclusion

Our data provide complementary evidence that the variants on chromosome 8q and 9p are associated with IA and that the risk of IA in patients with these variants are greatly increased with cigarette smoking.

Refractive development is influenced by environmental and genetic factors. Genetic studies have identified several regions of linkage to ocular refraction, but none have been carried out in African-derived populations. We performed quantitative trait locus linkage analyses in African-American (AA) families to identify genomic regions responsible for refraction. We recruited 493 AA individuals in 96 families to participate in the Myopia Family Study. Genotyping of 387 microsatellite markers was performed on 398 participants. The mean refraction among genotyped individuals was −2.87 D (SD = 3.58) and myopia of at least 1 D was present in 267 (68%) participants. Multipoint, regression-based, linkage analyses were carried out on a logarithmic transformation of ocular refraction using the statistical package MERLIN-REGRESS. Empirical significance levels were determined via 4,898 whole-genome gene-dropping simulations. Linkage analyses were repeated after clustering families into two subgroups based on admixture proportions as determined by the software package STRUCTURE. Genomewide significant linkage was seen at 47 cM on chromosome 7 (logarithm of the odds ratio (LOD) = 5.87, P = 0.00005). In addition, three regions on chromosomes 2p, 3p and 10p showed suggestive evidence of linkage (LOD > 2, P < 0.005) for ocular refraction. We mapped the first quantitative trait locus for ocular refraction in an AA population to chr.7p15. Two previous studies in European-derived families reported some evidence of linkage to a nearby region, suggesting that this region may contain polymorphisms that mediate refraction across populations. The genomic region under our linkage peak spans ~17 Mb and contains ~170 genes. Further refinement of this region will be pursued in future studies.

Purpose

To assess the correlations between retinal nerve fiber layer (RNFL) thickness measured with scanning laser polarimetry (SLP) and visual field (VF) sensitivity in primary open angle glaucoma (POAG) and primary angle-closure glaucoma (PACG).

Design

Prospective, comparative, observational cases series

Methods

Fifty patients with POAG and 56 with PACG were examined using SLP with variable corneal compensation (GDx VCC) and Humphrey VF analyzer between August 2005 and July 2006 at Taipei Veterans General Hospital. Correlations between RNFL thickness and VF sensitivity, expressed as mean sensitivity (MS) in both decibel (dB) and 1/Lambert (L) scales, were estimated by Spearman's rank correlation coefficient (rs) and multivariate median regression models (pseudo R2). The correlations were determined globally and for six RNFL sectors and their corresponding VF regions.

Results

The correlation between RNFL thickness and MS (in dB) was weaker in the PACG group (rs = 0.38, P = 0.004, pseudo R2 = 0.17) than in the POAG group (rs = 0.51, P <0.001, pseudo R2 = 0.31), but the difference in the magnitude of correlation was not significant (P = 0.42).With Bonferroni correction, the structure-function correlation was significant in the superotemporal (rs = 0.62), superonasal (rs = 0.56), inferonasal (rs = 0.53), and inferotemporal (rs = 0.50) sectors in the POAG group (all P <0.001), while it was significant only in the superotemporal (rs = 0.53) and inferotemporal (rs = 0.48) sectors in the PACG group (both P <0.001). The results were similar when MS was expressed as 1/L scale.

Conclusions

Both POAG and PACG eyes had moderate structure-function correlations using SLP. Compared to eyes with POAG, fewer RNFL sectors have significant structure-function correlations in eyes with PACG.

The role of polymorphisms in matrix metalloproteinase genes with variations in refractive error among Orthodox Ashkenazi Jewish and Old Order Amish families was investigated. The results showed statistically significant genetic associations of refraction with two markers in MMP regions in the Amish population, but not among the Ashkenazim.

Purpose.

Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are involved in scleral extracellular matrix remodeling and have shown differential expression in experimental myopia. The genetic association of refractive error and polymorphisms in MMP and TIMP genes in Old Order Amish (AMISH) and Ashkenazi Jewish (ASHK) families was investigated.

Methods.

Individuals from 55 AMISH and 63 ASHK families participated in the study. Ascertainment was designed to enrich the families for myopia; the mean spherical equivalent (MSE) refractive error (SD) was −1.61 (2.72) D in the AMISH, and −3.56 (3.32) D in the ASHK. One hundred forty-six common haplotype tagging SNPs covering 14 MMP and 4 TIMP genes were genotyped in 358 AMISH and 535 ASHK participants. Association analyses of MSE and the spherical component of refraction (SPH) were performed separately for the AMISH and the ASHK. Bonferroni-corrected significance thresholds and local false discovery rates were used to account for multiple testing.

Results.

After they were filtered for quality-control, 127 SNPs were included in the analyses. No polymorphisms showed statistically significant association to refraction in the ASHK (minimum P = 0.0132). In AMISH, two SNPs showed evidence of association with refractive phenotypes: rs1939008 (P = 0.00016 for SPH); and rs9928731 (P = 0.00026 for SPH). These markers were each estimated to explain <5% of the variance of SPH in the AMISH sample.

Conclusions.

Statistically significant genetic associations of ocular refraction to polymorphisms near MMP1 and within MMP2 were identified in the AMISH but not among the ASHK families. The results suggest that the MMP1 and MMP2 genes are involved in refractive variation in the AMISH. Genetic and/or environmental heterogeneity most likely contribute to differences in association results between ethnic groups.

Obesity is an important cause of morbidity and mortality worldwide. In the U.S., the prevalence of obesity is higher in African Americans than whites, even after adjustment for socioeconomic status. This leads to the hypothesis that differences in genetic background may contribute to racial/ethnic differences in obesity-related traits. We tested this hypothesis by conducting a genome-wide admixture mapping scan using 1,350 ancestry-informative SNPs in 3,531 self-identified blacks from the Atherosclerosis Risk in Communities (ARIC) study. We used these markers to estimate the overall proportions of European ancestry (PEA) for each individual and then scanned for the association between PEA and obesity-related traits (both continuous and dichotomous) at each locus. The median (interquartile range) PEA was 0.151 (0.115). PEA was inversely correlated with continuous body mass index (BMI), weight, and subscapular skinfold thickness, even after adjusting for socioeconomic factors. In contrast, PEA was positively correlated with BMI-adjusted waist circumference. Using admixture mapping on dichotomized traits, we identified a locus on 2p23.3 to be suggestively associated with BMI (locus-specific LOD = 4.11) and weight (locus-specific LOD = 4.07). After adjusting for global PEA, each additional copy of a European ancestral allele at the 2p23.3 peak was associated with a BMI decrease of ∼0.92 kg/m2 (p = 2.9 × 10-5). Further mapping in this region on chromosome 2 may be able to uncover causative variants underlying obesity, which may offer insights into the control of energy homeostasis.

Background

Genetic factors play important roles in lung cancer susceptibility. In this study, we replicated the association of 5p15.33 and 6p21.33 with familial lung cancer. Taking into account the previously identified genetic susceptibility variants on 6q23-25/RGS17 and 15q24-25.1, we further determined the cumulative association of these four genetic regions and the population attributable risk percent of familial lung cancer they account for.

Methods

One hundred ninety-four case patients and 219 cancer-free control subjects from the Genetic Epidemiology of Lung Cancer Consortium were used for the association analysis. Each familial case was chosen from one high-risk lung cancer family that has three or more affected members. Single nucleotide polymorphisms (SNP) on chromosomal regions 5p15.33, 6p21.33, 6q23-25/RGS17, and 15q24-25.1 were assessed for their associations with familial lung cancer. The cumulative association of the four chromosomal regions with familial lung cancer was evaluated with the use of a linear logistic model. Population attributable risk percent was calculated for each SNP using risk ratio.

Results

SNP rs31489 showed the strongest evidence of familial lung cancer association on 5p15.33 (P = 2 × 10−4; odds ratio, 0.57; 95% confidence interval, 0.42-0.77), whereas rs3117582 showed a weak association on 6p21.33 (P = 0.09; odds ratio, 1.47; 95% confidence interval, 0.94-2.31). Analysis of a combination of SNPs from the four regions provided a stronger cumulative association with familial lung cancer (P = 6.70 × 10−6) than any individual SNPs. The risk of lung cancer was increased to 3- to 11-fold among those subjects who had at least one copy of risk allele at each region compared with subjects without any of the risk factors. These four genetic regions contribute to a total of 34.6% of familial lung cancer in smokers.

Conclusions

The SNPs in four chromosomal regions have a cumulative and significant association with familial lung cancer and account for about one-third of the population attributable risk for familial lung cancer.

The metabolic syndrome is highly complex disease and has become one of the major public-health challenges worldwide. We sought to identify genetic loci with potential influence on multiple metabolic factors in a white population in Beaver Dam, Wisconsin, and to explore the possibility of genetic heterogeneity by family history of diabetes (FHD). Three metabolic factors were generated using principal component factor analysis, and they represented: 1) glycemia, 2) blood pressure, and 3) combined (body mass index, high-density lipoprotein cholesterol and serum uric acid) factors. Multipoint model-free linkage analysis of these factors with 385 microsatellite marker was performed on 1,055 sib-pairs, using Haseman-Elston regression. Genome-wide suggestive evidence of linkage was found at 30cM on chromosome 22q (empirical P [Pe] = 0.0002) for the glycemia factor, at 188–191 cM on chromosome 1q (Pe = 0.0007) for the blood pressure factor, and at 82 cM on chromosome 17q (Pe = 0.0007) for the combined factor. Subset analyses of the families by FHD showed evidence of genetic heterogeneity, with divergent linkage signals in the subsets on at least 4 chromosomes. We found evidence of genetic heterogeneity by FHD for the three metabolic factors. The results also confirmed findings of previous studies that mapped components of the metabolic syndrome to a chromosome 1q region.