Biomedical subjects

Eleanor Feingold

Publications and source records attributed to Eleanor Feingold.

At least 19 recordsLinked to original sources

PubMed2026

Trio-based GWAS reveals loci associated with different forms of isolated cleft lip.

Orofacial clefts (OFCs) are the most common craniofacial birth defect and comprise a diverse group of traits with complex and heterogeneous etiologies. Genetic studies of OFCs typically approach this diversity by stratifying cases into broad diagnostic classes, including cleft lip (CL), cleft palate (CP), and cleft lip with palate (CLP). Although this strategy has yielded important insights into OFC risk, it ignores the phenotypic heterogeneity within each subtype. CL exhibits marked phenotypic variability, involving differences in alveolar involvement, laterality, and sidedness that may reflect distinct etiologies. Given this phenotypic diversity within CL, we assembled a multi-ancestry cohort of 837 nonsyndromic CL case-parent trios with whole-genome sequencing and detailed phenotyping. We performed genome-wide association scans (GWAS) via transmission disequilibrium tests for CL overall and for 14 CL subtypes defined by involvement of the alveolus (with and without), laterality (uni- and bilateral), and sidedness (left and right). We identified four genome-wide significant loci. Two loci, IRF6 and 8q24.21, were both detected in the overall CL GWAS. PLCB1/PLCB4 and MAFB were detected in GWASs of alveolar cleft involvement and CL left sidedness, respectively. These subtype-specific associations were followed by case-only comparisons that reflect the presence or absence of alveolus cleft or left-sided bias of CL to confirm the specificity of the association signal to the particular subtype. Our results provide evidence of within-class CL subtype-specific genetic links for loci previously discussed in the context of primary OFC classes and demonstrate the value of granular OFC subtype characterization to capture trait-specific associations.

Alveolus Cleft↗

PubMed2007

The elusive goal of pedigree weights.

Non-parametric linkage analysis methods generally involve calculating an allele-sharing statistic for each pedigree in a data set, then standardizing and summing the statistics over pedigrees. Pedigrees of different sizes can be weighted differently in the sum, though it is perhaps most common to weight all standardized pedigree statistics equally. Most other common weighting schemes are based on the number of affected individuals in the pedigree. It is also possible to derive optimal weights, which maximize power to detect linkage under particular trait models. We started by investigating three different analytical and simulation-based methods to calculate power and derive optimal weights. We found that simulation methods produce noticeably more accurate power calculations than the other methods. However, although the different calculation methods give different "optimal" weights, the power at those weights is very similar. That is, the analytical calculation methods are sufficient for finding good weights even though the simulation methods are most appropriate for calculating power. In comparing optimal weights for different trait models, we found that the weights vary quite a bit with the model, such that optimal weights for one model are not necessarily powerful at all for other models. Finally, we studied the power of a number of general weighting schemes, and of some new ones that incorporate information on how closely the affected individuals are related. We were able to find some schemes that performed well in the sense of giving reasonably powerful weights for most of the trait models and pedigree types we considered.

Chromosome Mapping↗

PubMed2006

Referral to cancer genetic counseling: are there stages of readiness?

As genetic awareness spreads among healthcare providers and the general public, and evidence mounts to show the efficacy of cancer control methods, referrals to cancer genetic counseling services for risk assessment are becoming more common. However, few studies have examined referral patterns to genetics and even less is known about referral uptake to clinical cancer genetic counseling. We investigated outcome of genetics referral in 43 affected women attending a breast cancer treatment program who were referred based on having BRCA mutation carrier risks > or =10%. Within 6 months, of the 36 women we were able to recontact, 13 (36%) came to an appointment at the cancer genetic counseling clinic (Acceptors), 10 (27%) said they intended to come in the future (Intenders), and 13 (36%) said they would not consider genetic counseling (Decliners). Referral uptake was framed by elements of the Transtheoretical model (TTM) to determine if decisional balance scores (DBSs), a summary of an individual's "Pro" and "Con" opinions related to genetic testing, correlated with their decision to follow through. Mean DBS's were strongly negative for the Decliner group (-7.4), weakly negative for the Intender group (-1.1), and positive for the Acceptor group (5.4). The difference in the DBS along the continuum was due more to the mean "Con" score decreasing, rather than the mean "Pro" score increasing. Theoretical frameworks are needed to study adherence to referral for cancer genetic counseling. Stage-based theories may have a role to play.

Adult↗

PubMed2006

Gender-specific association of ATP-binding cassette transporter 1 (ABCA1) polymorphisms with the risk of late-onset Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder caused by a complex interaction of genetic and environmental factors. Increasing evidence highlights a potential role for cholesterol in the pathophysiology of AD. The ABCA1 gene, located in close vicinity to the 9q linkage peaks identified by genome-wide AD linkage studies, plays an important role in cellular cholesterol efflux, and is likely a good candidate gene. However, results from published genetic association studies between ABCA1 and AD are ambiguous. In the present study, we examined the role of two ABCA1 polymorphisms, R219K (rs2230806) and G-17C (rs2740483) in modifying the risk of late-onset AD (LOAD) in a large American white cohort of 992 AD cases and 699 controls. We observed significant gender x R219K interaction (p=0.00008). Female carriers of the 219K allele showed a 1.75-fold increased risk of developing AD compared to non-219K carrier females (95% CI 1.34-2.29; p=0.00004). The overall two-site haplotype distribution was also significant between female AD cases and controls (p=0.017). The risk associated with the R219K polymorphism was independent of the recently reported significant association in the ubiquilin (UBQLN1) gene in this region on chromosome 9q. Our data suggest a gender-specific and APOE and UBQLN1 independent association between the ABCA1/R219K polymorphism and LOAD.

ATP Binding Cassette Transporter 1↗

PubMed2006

Treatment of uninformative families in mean allele sharing tests for linkage.

Using affected sibling pairs, the mean allele sharing statistic tests for linkage by testing if the mean proportion of alleles that are identical-by-descent (IBD) is equal to a half. The behavior of some versions of the mean allele sharing test statistic depends on whether or not families that are uninformative for their IBD status are included; the SIBPAL version provides less significant values when all families (informative and uninformative) are used than when only informative families are used. Here, we investigate this behavior both analytically and by simulation. Our investigation shows that the main issue is the choice of the variance estimator in the denominator of the statistic. The choice of the denominator is very important and is still not totally resolved. Our mathematical explanation supported by our simulation study might aid in the search for an optimum solution.

Alleles↗

PubMed2006

Increased expression of interferon-inducible genes in macaque lung tissues during simian immunodeficiency virus infection.

Pulmonary infections and dysfunction are frequent outcomes during the development of immunodeficiency associated with human immunodeficiency virus type 1 (HIV-1) infection, and obtaining a better understanding of the immunologic changes that occur in lungs following HIV-1 infection will provide a foundation for the development of further intervention strategies. We sought here to identify changes in the pulmonary immune environment that arise during simian immunodeficiency virus (SIV) infection of rhesus macaques, which serves as an excellent model system for HIV-1 infection and disease. To examine the gene expression profiles of macaque lung tissues following infection with the pathogenic SIV/DeltaB670 isolate, we performed cDNA microarray hybridizations with lung total RNAs using two commercially available cDNA arrays and a custom-fabricated, immunologically focused macaque cDNA microarray. In situ hybridization and real-time RT-PCR were performed to provide additional analyses of gene expression. Among the genes exhibiting the highest level of induction in lung tissues were the IFN-gamma-inducible chemokines, CXCL10/IP-10 and CXCL9/Mig. In situ hybridization and real-time RT-PCR strongly supported these findings. Correlation analyses revealed that the levels of expression of IFN-gamma, CXCL9/Mig, and CXCL10/IP-10 mRNAs were all strongly positively correlated, and that CXCL10/IP-10 mRNA and Pneumocystis carinii rRNA were positively correlated. Taken together, these findings demonstrate that inflammatory chemokines are among the most differentially expressed mRNAs in macaque lung tissues during systemic SIV infection of rhesus macaques, and provide insight into the complicated events occurring in the lung tissues during HIV-1 infection in humans.

Animals↗

PubMed2006

Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

In this study, we compare the statistical properties of a number of methods for estimating P-values for allele-sharing statistics in non-parametric linkage analysis. Some of the methods are based on the normality assumption, using different variance estimation methods, and others use simulation (gene-dropping) to find empirical distributions of the test statistics. For variance estimation methods, we consider the perfect variance approximation and two empirical variance estimates. The simulation-based methods are gene-dropping with and without conditioning on the observed founder alleles. We also consider the Kong and Cox linear and exponential models and a Monte Carlo method modified from a method for finding genome-wide significance levels. We discuss the analytical properties of these various P-value estimation methods and then present simulation results comparing them. Assuming that the sample sizes are large enough to justify a normality assumption for the linkage statistic, the best P-value estimation method depends to some extent on the (unknown) genetic model and on the types of pedigrees in the sample. If the sample sizes are not large enough to justify a normality assumption, then gene-dropping is the best choice. We discuss the differences between conditional and unconditional gene-dropping.

Alleles↗

PubMed2005

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting.

FOOTER is a newly developed algorithm that analyzes homologous mammalian promoter sequences in order to identify transcriptional DNA regulatory 'signals'. FOOTER uses prior knowledge about the binding site preferences of the transcription factors (TFs) in the form of position-specific scoring matrices (PSSMs). The PSSM models are generated from known mammalian binding sites from the TRANSFAC database. In a test set of 72 confirmed binding sites (most of them not present in TRANSFAC) of 19 TFs, it exhibited 83% sensitivity and 72% specificity. FOOTER is accessible over the web at http://biodev.hgen.pitt.edu/Footer/.

Algorithms↗

PubMed2005

Skeletal muscle fatigue, strength, and quality in the elderly: the Health ABC Study.

We examined the muscle fatigue characteristics in older men and women and determined whether these were related to the size, strength, or quality of muscle. A total of 1,512 men and women aged 70-79 yr from the Health, Aging, and Body Composition Study participated in this study. Muscle cross-sectional area and attenuation were determined with computed tomography. Skeletal muscle fatigue and strength (peak torque) of the knee extensors and flexors were measured using isokinetic dynamometry. Men were more fatigue resistant than women for both knee extension (fatigue index: 70.4 +/- 15.3 vs. 66.9 +/- 14.3%; P < 0.05) and knee flexion (67.9 +/- 16.4 vs. 64.9 +/- 17.6%; P < 0.05). Peak torque and muscle quality (specific torque) were higher in men than women for knee extension (99.6 +/- 28.2 vs. 63.0 +/- 16.8 N x m and 1.62 +/- 0.43 vs. 1.51 +/- 0.39 N x m/cm2; both P < 0.05) and for knee flexion (74.0 +/- 26.4 vs. 49.6 +/- 15.9 N x m and 2.47 +/- 1.29 vs. 2.22 +/- 0.78 N x m/cm2; both P < 0.05). Total work and power output was greater in men compared with women for both the quadriceps (1,353 +/- 451 vs. 832 +/- 264 J and 87.7 +/- 33.5 vs. 53.3 +/- 19.2 W; both P < 0.05) and the hamstrings (741 +/- 244 vs. 510 +/- 141 J and 35.4 +/- 16.0 vs. 23.7 +/- 10.2 W; both P < 0.05). In both genders, the quadriceps was able to perform more work with greater power compared with the hamstrings. Those who were stronger actually had greater fatigue after adjusting for age, race, physical activity, and total body fat. In conclusion, older men were more fatigue resistant than women, although in both men and women greater fatigue was not related to muscle weakness.

Aged↗

PubMed2005

QTL mapping with discordant and concordant sibling pairs: new statistics and new design strategies.

The term "extreme discordant and concordant" (EDAC) sampling has been used to describe a variety of strategies for quantitative trait locus mapping using sibling pairs sampled from the corners of the bivariate trait distribution. The principle of the design is to gain efficiency by genotyping only the most informative of the available sibling pairs. EDAC-type designs have been studied in a number of papers, and have been applied in a few others. This literature is somewhat out of date, however, because there are many new statistics that are appropriate for EDAC data. With newer statistics, the power of EDAC designs can be improved. Moreover, the relative power of different designs must be re-evaluated, because the newer statistics improve the power of some designs more than others. That is, there is a circular relationship between design and statistic choices. In this report, we review a number of available design and statistic choices for EDAC studies, and use simulation to show what statistics are most powerful for each design. We then use those more powerful statistics to suggest strategies for making design choices among various EDAC and non-EDAC designs that use sibling pairs. We find that when genotyping must be minimized, an EDAC design with predominantly discordant pairs is the best choice, and when a balance of genotyping and phenotyping effort must be achieved, single proband ascertainment can do better. We also show that moderately selected samples (as opposed to very extreme samples) can be an efficient choice for many studies.

Algorithms↗

PubMed2005

The C161-->T polymorphism in peroxisome proliferator-activated receptor gamma, but not P12A, is associated with insulin resistance in Hispanic and non-Hispanic white women: evidence for another functional variant in peroxisome proliferator-activated receptor gamma.

The P12A variant in the peroxisome proliferator-activated receptor gamma (PPARgamma) gene has been intensely studied for association with obesity-related or type-2 diabetes-related traits; however, the results have been somewhat inconsistent in different populations. We genotyped a large cohort of Hispanic and non-Hispanic white individuals from the San Luis Valley Diabetes Study for P12A and another common variant, C161-->T, in the PPARgamma gene to determine if these sites were associated with fasting glucose, insulin, free fatty acid levels, insulin sensitivity, or body fat. There were no statistically significant frequency differences at these two sites between Hispanic and non-Hispanic individuals. No significant association with the metabolic phenotypes was observed for either of the polymorphisms in men; however, in women, significant associations were shown between the C161-->T variant and fasting insulin (P=.008) and the homeostasis model assessment of insulin resistance (HOMA IR; P=.007). After adjusting for age, smoking, fat mass, and skin reflectance, linear regression showed that C161-->T explained 1.5% of the variation in both fasting insulin (P=.031) and HOMA IR (P=.028) whereas P12A contributed only 0.04% (fasting insulin, P=.268) and 0.02% (HOMA IR, P=.418) to the total trait variation. In the San Luis Valley Diabetes Study female patients, C161-->T appears to be a better predictor of fasting insulin levels and insulin resistance than P12A although the effect of this variant is small. These results support the hypothesis that C161-->T is in linkage disequilibrium with unidentified functional variation in PPARgamma or in a linked gene. This could explain some of the inconsistencies in the P12A association studies as the allele frequency and level of linkage disequilibrium of another functional polymorphism in the region could vary in different populations.

Colorado↗

PubMed2005

Footer: a quantitative comparative genomics method for efficient recognition of cis-regulatory elements.

The search for mammalian DNA regulatory regions poses a challenging problem in computational biology. The short length of the DNA patterns compared with the size of the promoter regions and the degeneracy of the patterns makes their identification difficult. One way to overcome this problem is to use evolutionary information to reduce the number of false-positive predictions. We developed a novel method for pattern identification that compares a pair of putative binding sites in two species (e.g., human and mouse) and assigns two probability scores based on the relative position of the sites in the promoter and their agreement with a known model of binding preferences. We tested the algorithm's ability to predict known binding sites on various promoters. Overall, it exhibited 83% sensitivity and the specificity was 72%, which is a clear improvement over existing methods. Our algorithm also successfully predicted two novel NF-kappaB binding sites in the promoter region of the mouse autotaxin gene (ATX, ENPP2), which we were able to verify by using chromatin immunoprecipitation assay coupled with quantitative real-time PCR.

Algorithms↗

PubMed2004

Association between maternal age and meiotic recombination for trisomy 21.

Altered genetic recombination has been identified as the first molecular correlate of chromosome nondisjunction in both humans and model organisms. Little evidence has emerged to link maternal age--long recognized as the primary risk factor for nondisjunction--with altered recombination, although some studies have provided hints of such a relationship. To determine whether an association does exist, chromosome 21 recombination patterns were examined in 400 trisomy 21 cases of maternal meiosis I origin, grouped by maternal age. These recombination patterns were used to predict the chromosome 21 exchange patterns established during meiosis I. There was no statistically significant association between age and overall rate of exchange. The placement of meiotic exchange, however, differed significantly among the age groups. Susceptible patterns (pericentromeric and telomeric exchanges) accounted for 34% of all exchanges among the youngest class of women but only 10% of those among the oldest class. The pattern of exchanges among the oldest age group mimicked the pattern observed among normally disjoining chromosomes 21. These results suggest that the greatest risk factor for nondisjunction among younger women is the presence of a susceptible exchange pattern. We hypothesize that environmental and age-related insults accumulate in the ovary as a woman ages, leading to malsegregation of oocytes with stable exchange patterns. It is this risk, due to recombination-independent factors, that would be most influenced by increasing age, leading to the observed maternal age effect.

Adult↗

PubMed2004

A powerful and robust new linkage statistic for discordant sibling pairs.

Previously, Szatkiewicz and colleagues evaluated the performance of a wide variety of statistics for quantitative-trait-locus linkage, using discordant sibling pairs. They found that the most powerful statistics, in general, were a score statistic and a "composite statistic." However, whereas these two statistics have equal power under ideal conditions, each has limitations that reduce its power in certain circumstances. The score statistic depends on estimates of trait parameters and can lose a lot of power if those estimates are incorrect. The composite statistic is not sensitive to trait-parameter estimates but does depend on arbitrary weights that must be chosen on the basis of the ascertainment scheme. In this report, we elucidate the algebraic relationship between the score and composite statistics and then use that relationship to suggest a new statistic that combines the best properties of both. We call our new statistic the "robust discordant pair" (RDP) statistic. We report simulation studies to show that the RDP statistic does, indeed, have all of the strengths and none of the weaknesses of the score and composite statistics.

Computer Simulation↗

PubMed2004

Efficient simulation of P values for linkage analysis.

In many genetic linkage analyses, the P value is obtained through simulation since the underlying distribution of the test statistic is complex and unknown. However, this can be very computationally intensive. A "bootstrap/replicate pool" approach has been suggested that generates P values more efficiently in terms of computation by resampling sums from a small set of simulated replicates for each pedigree. The replicate pool idea has been successfully applied, but, to our knowledge, has never been theoretically studied. An entirely different method for increasing the computational efficiency of P value simulation is Besag and Clifford's sequential sampling method. We propose an algorithm which combines Besag and Clifford's method with the replicate pool method to efficiently estimate P values for linkage studies. We derive variance expressions for the P value estimates from the replicate pool method and from our proposed hybrid method, and use these to show that the hybrid estimator has a substantial advantage over the other methods in most situations.

Algorithms↗

PubMed2004

A trisomic transmission disequilibrium test.

Certain congenital disorders that are rare in the general population are quite common in individuals with trisomic conditions. For example, complete atrioventricular septal defect occurs in about 20% of individuals with Down syndrome, an approximately 500-fold increase in risk as compared to individuals without Down syndrome. Genetic variation on the chromosome involved in the trisomy may affect susceptibility to these trisomy-specific disorders. That is, increased dosage of a variant may be directly involved in increasing the risk of a disorder, or it may be indirectly involved by causing up- or downregulation of other genes. As in standard disomic gene-mapping, one can search for genes using linkage or association methods. Within association methods, one can consider case-control methods or family-based control methods such as the transmission disequilibrium test (TDT). Most gene-mapping methods need to be substantially redesigned for use with trisomic data. In this paper, we present a "trisomic TDT", a statistical method of testing for nonrandom transmission of alleles from parents to trisomic children. We demonstrate the method on a dataset of parent-child trios in which the child has Down syndrome.

Adult↗

PubMed2004

Linkage disequilibrium mapping in trisomic populations: analytical approaches and an application to congenital heart defects in Down syndrome.

Many of the birth defects associated with trisomy exhibit both variable expressivity and incomplete penetrance. This variability suggests that it is allelic variation and not simply the presence of an additional chromosome that leads to the development of certain trisomy-associated birth defects. With the proper tools, one may use trisomic populations to identify genes involved in the development of specific birth defects. A trisomic population may be advantageous over a normal population if the defect is over-represented in the trisomic population. Alternatively, one can view the trisomic populations as a "model system" to offer insight into aspects of both normal and abnormal embryonic development. Standard disomic linkage disequilibrium mapping approaches need to be adjusted to account for the presence of the additional genetic material in the trisomic individuals. We present an approach for linkage disequilibrium mapping of variable phenotypes in a trisomic population that adequately accounts for the additional alleles and the pattern of non-independent inheritance. We establish the laboratory methods and statistical tools necessary to conduct an association study in a trisomic population. As an example, we have applied these tools to a pilot study of Down syndrome-associated congenital heart defects.

Alleles↗

PubMed2004

Genetic variation in fatty acid-binding protein-4 and peroxisome proliferator-activated receptor gamma interactively influence insulin sensitivity and body composition in males.

Obesity and type 2 diabetes are closely related, multifactorial metabolic conditions characterized by alterations in energy metabolism and glucose homeostasis, respectively. Peroxisome proliferator-activated receptor gamma (PPARgamma) is a ligand-dependent transcription factor that regulates genes involved in lipid and glucose homeostasis, including the adipocyte-specific fatty acid-binding protein (FABP4). In turn, FABP4 binds fatty acids and transports them to the nucleus where the FABP4/fatty acid complex activates PPARgamma in a positive feedback loop. In this study, we tested the hypothesis that the polymorphisms, FABP4-376 and PPARgamma Pro12Ala, interactively influence insulin sensitivity and body composition in nondiabetic, Hispanic and non-Hispanic white males (n = 314) participating in the San Luis Valley Diabetes Study (SLVDS). Although the individual sites were not statistically significantly associated with any of the outcomes, we found statistically significant interaction terms in 2-way analysis of covariance (ANCOVA) models for homeostasis model assessment of insulin resistance (HOMA-IR) (P =.014) and lean mass (P =.019). While the PPARgamma Pro12Ala site was the only statistically significant predictor of fat mass in the 2-way model (P =.012), the FABP4 and PPARgamma main effect terms individually became stronger when considered in one model compared with the analysis of each polymorphism separately. These findings provide evidence that FABP4 and PPARgamma work together to influence a biologic pathway affecting insulin sensitivity and body composition, illustrating the importance of investigating the joint effect of genes in determining susceptibility for complex disease.

Adult↗