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1.  Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas 
Familial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC).
We genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model‐based and model‐free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model‐based linkage analysis. Model‐based and model‐free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome‐wide associations were also tested in these families.
Linkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female‐affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported.
Our linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.
PMCID: PMC4947860  PMID: 27468417
Barrett's esophagus; esophageal adenocarcinoma; familial; genetics; linkage
2.  Putative linkage signals identified for breast cancer in African American families 
Genome-wide association studies have identified polymorphisms associated with breast cancer subtypes and across multiple population subgroups; however, few studies to date have applied linkage analysis to other population groups.
We performed the first genome-wide breast cancer linkage analysis in 106 African American families (comprising 179 affected and 79 unaffected members) not known to be segregating BRCA mutations to search for novel breast cancer loci. We performed regression-based model-free multi-point linkage analyses of the sibling pairs using SIBPAL, and two-level Haseman-Elston linkage analyses of affected relative pairs using RELPAL.
We identified −log10p-values that exceed 4 on chromosomes 3q and 12q, as well as a region near BRCA1 on chromosome 17 (−log10p-values in the range of 3.0-3.2) using both sibling-based and relative-based methods, the latter observation may suggest that undetected BRCA1 mutations or other mutations nearby such as HOXB13 may be segregating in our sample.
In summary, these results suggest novel putative regions harboring risk alleles in African Americans that deserve further study.
We hope that our study will spur further family-based investigation into specific mechanisms for breast cancer disparities.
PMCID: PMC4323921  PMID: 25477366
breast cancer; family; linkage study; African American
3.  Novel approaches to the analysis of family data in genetic epidemiology 
PMCID: PMC4319458  PMID: 25705217
genome-wide association; family studies; study designs; genetic factors; environmental factors
4.  Analysis pipeline for the epistasis search – statistical versus biological filtering 
Frontiers in Genetics  2014;5:106.
Gene–gene interactions may contribute to the genetic variation underlying complex traits but have not always been taken fully into account. Statistical analyses that consider gene–gene interaction may increase the power of detecting associations, especially for low-marginal-effect markers, and may explain in part the “missing heritability.” Detecting pair-wise and higher-order interactions genome-wide requires enormous computational power. Filtering pipelines increase the computational speed by limiting the number of tests performed. We summarize existing filtering approaches to detect epistasis, after distinguishing the purposes that lead us to search for epistasis. Statistical filtering includes quality control on the basis of single marker statistics to avoid the analysis of bad and least informative data, and limits the search space for finding interactions. Biological filtering includes targeting specific pathways, integrating various databases based on known biological and metabolic pathways, gene function ontology and protein–protein interactions. It is increasingly possible to target single-nucleotide polymorphisms that have defined functions on gene expression, though not belonging to protein-coding genes. Filtering can improve the power of an interaction association study, but also increases the chance of missing important findings.
PMCID: PMC4012196  PMID: 24817878
epistasis; genetic interaction; biological interaction; filtering pipeline; optimal search
5.  A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma 
We propose a two-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma.
First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N=281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N=74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are re-estimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis.
Using the best fitting segregation models in model-based multipoint linkage analysis, we identified two separate peaks on chromosome 17; the first agreed with a region identified by Shete et al. who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD).
Our approach has the advantage of not requiring markers to be in linkage equilibrium unless the minor allele frequency is small (markers which tend to be uninformative for linkage), and of using more of the available information for LOD-based linkage analysis.
PMCID: PMC3518573  PMID: 22962404
Glioma; model-based linkage; segregation; age of onset; prevalence constraint
6.  Linkage analysis of plasma dopamine β-hydroxylase activity in families of patients with schizophrenia 
Human genetics  2011;130(5):635-643.
Dopamine β-hydroxylase (DβH) catalyzes the conversion of dopamine to norepinephrine. DβH enters the plasma after vesicular release from sympathetic neurons and the adrenal medulla. Plasma DβH activity (pDβH) varies widely among individuals, and genetic inheritance regulates that variation. Linkage studies suggested strong linkage of pDβH to ABO on 9q34, and positive evidence for linkage to the complement fixation locus on 19p13.2-13.3. Subsequent association studies strongly supported DBH, which maps adjacent to ABO, as the locus regulating a large proportion of the heritable variation in pDβH. Prior studies have suggested that variation in pDβH, or genetic variants at DβH, associate with differences in expression of psychotic symptoms in patients with schizophrenia and other idiopathic or drug-induced brain disorders, suggesting that DBH might be a genetic modifier of psychotic symptoms. As a first step toward investigating that hypothesis, we performed linkage analysis on pDβH in patients with schizophrenia and their relatives. The results strongly confirm linkage of markers at DBH to pDβH under several models (maximum multipoint LOD score, 6.33), but find no evidence to support linkage anywhere on chromosome 19. Accounting for the contributions to the linkage signal of three SNPs at DBH, rs1611115, rs1611122, and rs6271 reduced but did not eliminate the linkage peak, whereas accounting for all SNPs near DBH eliminated the signal entirely. Analysis of markers genome-wide uncovered positive evidence for linkage between markers at chromosome 20p12 (multi-point LOD = 3.1 at 27.2 cM). The present results provide the first direct evidence for linkage between DBH and pDβH, suggest that rs1611115, rs1611122, rs6271 and additional unidentified variants at or near DBH contribute to the genetic regulation of pDβH, and suggest that a locus near 20p12 also influences pDβH.
PMCID: PMC3193571  PMID: 21509519
7.  Capability of common SNPs to tag rare variants 
BMC Proceedings  2011;5(Suppl 9):S88.
Genome-wide association studies are based on the linkage disequilibrium pattern between common tagging single-nucleotide polymorphisms (SNPs) (i.e., SNPs having only common alleles) and true causal variants, and association studies with rare SNP alleles aim to detect rare causal variants. To better understand and explain the findings from both types of studies and to provide clues to improve the power of an association study with only common SNPs genotyped, we study the correlation between common SNPs and the presence of rare alleles within a region in the genome and look at the capability of common SNPs in strong linkage disequilibrium with each other to capture single rare alleles. Our results indicate that common SNPs can, to some extent, tag the presence of rare alleles and that including SNPs in strong linkage disequilibrium with each other among the tagging SNPs helps to detect rare alleles.
PMCID: PMC3287929  PMID: 22373521
8.  A Segregation Analysis of Barrett’s Esophagus and Associated Adenocarcinomas 
Familial aggregation of esophageal adenocarcinomas, esophagogastric junction adenocarcinomas, and their precursor Barrett’s esophagus has been termed Familial Barrett’s Esophagus (FBE). Numerous studies documenting increased familial risk for these diseases raise the hypothesis that there may be an inherited susceptibility to the development of BE and its associated cancers. In this study, using segregation analysis for a binary trait as implemented in S.A.G.E. 6.0.1, we analyzed data on 881singly ascertained pedigrees in order to determine whether FBE is caused by a common environmental or genetic agent and, if genetic, to identify the mode of inheritance of FBE. The inheritance models were compared by likelihood ratio tests and Akaike’s A Information Criterion. Results indicated that random environmental and/or multifactorial components were insufficient to fully explain the familial nature of FBE, but rather there is segregation of a major type transmitted from one generation to the next (p-value < 10−10). An incompletely dominant inheritance model together with a polygenic component fits the data best. For this dominant model, the estimated penetrance of the dominant allele is 0.1005 (95% confidence interval, CI: 0.0587 to 0.1667) and the sporadic rate is 0.0012 (95% CI: 0.0004 to 0.0042), corresponding to a relative risk of 82.53 (95% CI: 28.70 to 237.35), or odds ratio of 91.63 (95% CI: 32.01 to 262.29). This segregation analysis provides epidemiological evidence in support of one or more rare autosomally inherited dominant susceptibility allele(s) in FBE families, and hence motivates linkage analyses.
PMCID: PMC2838211  PMID: 20200424
familial esophageal adenocarcinomas; complex segregation analysis; dominant major gene inheritance; polygenic component; likelihood; AIC; unified model
9.  Comparison of a unified analysis approach for family and unrelated samples with the transmission-disequilibrium test to study associations of hypertension in the Framingham Heart Study 
BMC Proceedings  2009;3(Suppl 7):S22.
Population stratification is one of the major causes of spurious associations in association studies. A unified association approach based on principal-component analysis can overcome the effect of population stratification, as well as make use of both family and unrelated samples combined to increase power (family-case-control, or FamCC). In this study, we compared FamCC and the transmission-disequilibrium test (TDT) using data on hypertension, systolic blood pressure, and diastolic blood pressure in the Framingham Heart Study. Our study indicated FamCC has reasonable type I error for both the unrelated sample and the family sample for all three traits. For these three traits, we found results from FamCC were inconsistent with those from the TDT. We discuss the reasons for this inconsistency. After correcting for multiple tests, we did not detect any significant single-nucleotide polymorphisms by either FamCC or the TDT.
PMCID: PMC2795919  PMID: 20018012

Results 1-9 (9)