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I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston–Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction—statistical and biological—are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is now freely available for use and further development. Finally, I describe methods to estimate and test hypotheses about familial correlations, and point out that the liability model often used to estimate disease heritability estimates the heritability of that liability, rather than of the disease itself, and so can be highly dependent on the assumed distribution of that liability. [ABSTRACT FROM AUTHOR]

Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. [ABSTRACT FROM AUTHOR]

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. Results: A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. Conclusions: The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations. [ABSTRACT FROM AUTHOR]

This paper is concerned with evaluating whether an interaction between two sets of risk factors for a binary trait is removable and, when it is removable, fitting a parsimonious additive model using a suitable link function to estimate the disease odds (on the natural logarithm scale). Statisticians define the term 'interaction' as a departure from additivity in a linear model on a specific scale on which the data are measured. Certain interactions may be eliminated via a transformation of the outcome such that the relationship between the risk factors and the outcome is additive on the transformed scale. Such interactions are known as removable interactions. We develop a novel test statistic for detecting the presence of a removable interaction in case-control studies. We consider the Guerrero and Johnson family of transformations and show that this family constitutes an appropriate link function for fitting an additive model when an interaction is removable. We use simulation studies to examine the type I error and power of the proposed test and to show that, when an interaction is removable, an additive model based on the Guerrero and Johnson link function leads to more precise estimates of the disease odds parameters and a better fit. We illustrate the proposed test and use of the transformation by using case-control data from three published studies. Finally, we indicate how one can check that, after transformation, no further interaction is significant. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To detect rare variants associated with a phenotype, we develop a novel statistical method that can use both family and unrelated case-control data. Unlike the currently existing methods, we first use family data to calculate weights to be given to rare variants, differentiating between concordantly affected and discordant sib pairs. These weights are then used in an association test applied to the unrelated case-control data. We applied the proposed method to the simulated sequencing data in Genetic Analysis Workshop 17 and identified two genes associated with the disease. [ABSTRACT FROM AUTHOR]

We found from our analysis of the Genetic Analysis Workshop 17 data that the population structure of the 697 unrelated individuals was an important confounding factor for association studies, even if it was not explicitly considered when simulating the phenotypes. We uncovered structures beyond the reported ethnicities and found ample evidence of phenotype-population structure associations. The first 10 principal components of the genotype data of the 697 individuals demonstrated much stronger associations with Q1, Q2, and the disease than did the individuals' ethnicities. In addition, we observed that population structure was a confounding factor for the Q1-gene association when identifying the significant genes both with and without adjusting for the causal singlenucleotide polymorphisms, the ethnicities, and the principal components. Many false discoveries remained after adjusting for the causal singlenucleotide polymorphisms. Adjusting for the principal components appeared more effective than did adjusting for ethnicity in terms of preventing false discoveries. This analysis was performed with knowledge of the causal loci. [ABSTRACT FROM AUTHOR]

The Genetic Analysis Workshop 17 data we used comprise 697 unrelated individuals genotyped at 24,487 singlenucleotide polymorphisms (SNPs) from a mini-exome scan, using real sequence data for 3,205 genes annotated by the 1000 Genomes Project and simulated phenotypes. We studied 200 sets of simulated phenotypes of trait Q2. An important feature of this data set is that most SNPs are rare, with 87% of the SNPs having a minor allele frequency less than 0.05. For rare SNP detection, in this study we performed a least absolute shrinkage and selection operator (LASSO) regression and F tests at the gene level and calculated the generalized degrees of freedom to avoid any selection bias. For comparison, we also carried out linear regression and the collapsing method, which sums the rare SNPs, modified for a quantitative trait and with two different allele frequency thresholds. The aim of this paper is to evaluate these four approaches in this mini-exome data and compare their performance in terms of power and false positive rates. In most situations the LASSO approach is more powerful than linear regression and collapsing methods. We also note the difficulty in determining the optimal threshold for the collapsing method and the significant role that linkage disequilibrium plays in detecting are causal SNPs. If a rare causal SNP is in strong linkage disequilibrium with a common marker in the same gene, power will be much improved. [ABSTRACT FROM AUTHOR]

The author comments on the significance of genomic research to understand the impact of gene interaction in maternal and perinatal diseases in the U.S. The authors mention that these studies not only furnishes understanding of the causes of various types of complex diseases, but the connection of gene-gene interactions of human traits. Moreover, they state that these researches also provide information on the risk factors associated with maternal and perinatal pregnancy complications.

Current extensive genetic research into common complex diseases, especially with the completion of genome-wide association studies, is bringing to light many novel genetic risk loci. These new discoveries, along with previously known genetic risk variants, offer an important opportunity for researchers to improve health care. We describe a method of quick evaluation of these new findings for potential clinical practice by designing a new predictive genetic test, estimating its classification accuracy, and determining the sample size required for the verification of this accuracy. The proposed predictive test is asymptotically more powerful than tests built on any other existing method and can be extended to scenarios where loci are linked or interact. We illustrate the approach for the case of type 2 diabetes. We incorporate recently discovered risk factors into the proposed test and find a potentially better predictive genetic test. The area under the receiver operating characteristic (ROC) curve (AUC) of the proposed test is estimated to be higher (AUC = 0.671) than for the existing test (AUC = 0.580). [ABSTRACT FROM AUTHOR]

The parent-of-origin phenomenon in humans is now well recognized, and the deregulation of imprinted genes has been implicated in a number of human diseases. Recently, several linkage analysis methods have been developed to allow for parent-of-origin effects in the analysis of pedigree data. However, in general, one does not know a priori if disease-causing loci are imprinted or not. Linkage methods that allow for imprinting can lose power if there is no imprinting. Conversely, linkage methods that do not allow for imprinting will lose power if there is imprinting, because of penetrance values not being correctly specified. Therefore, it is important to know whether imprinting is a possible mode of disease inheritance before performing linkage analyses. In this paper we describe a simple covariate-coding scheme to test for the presence of parent-of-origin effects, and provide a formula for calculating parent-specific penetrance values prior to any linkage analysis. In simulation studies our coding scheme successfully detected parent-of-origin effects and, when pedigrees were ascertained sequentially or through a single proband, inclusion of this covariate more accurately estimated penetrance values than when such a covariate was not included. The use of accurate penetrance values in a linkage analysis that allows for imprinting can provide higher power when the disease locus is imprinted. Finally, we applied our approach to 27 pedigrees affected with Beckwith-Wiedemann syndrome (BWS), an overgrowth syndrome, and found that a maternally expressed parent-of-origin model based on the likelihood ratio test was the most parsimonious, suggesting a role for paternally imprinted genes in BWS. [ABSTRACT FROM AUTHOR]

In the past, to study Mendelian diseases, segregating families have been carefully ascertained for segregation analysis, followed by collecting extended multiplex families for linkage analysis. This would then be followed by association studies, using independent case-control samples and/or additional family data. Recently, for complex diseases, the initial sampling has been for a genome-wide linkage analysis, often using independent sib-pairs or nuclear families, to identify candidate regions for follow-up with association studies, again using case-control samples and/or additional family data. We now have the ability to conduct genome-wide association studies using 100,000-500,000 diallelic genetic markers. For such studies we focus especially on efficient two-stage association sampling designs, which can retain nearly optimal statistical power at about half the genotyping cost. Similarly, beginning an association study by genotyping pooled samples may also be a viable option if the cost of accurately pooling DNA samples outweighs genotyping costs. Finally, we note that the sampling of family data for linkage analysis is not a practice that should be automatically discontinued. [ABSTRACT FROM AUTHOR]

Association studies offer an exciting approach to finding underlying genetic variants of complex human diseases. However, identification of genetic variants still includes difficult challenges, and it is important to develop powerful new statistical methods. Currently, association methods may depend on single-locus analysis—that is, analysis of the association of one locus, which is typically a single-nucleotide polymorphism (SNP), at a time—or on multilocus analysis, in which multiple SNPs are used to allow extraction of maximum information about linkage disequilibrium (LD). It has been shown that single-locus analysis may have low power because a single SNP often has limited LD information. Multilocus analysis, which is more informative, can be performed on the basis of either haplotypes or genotypes. It may lose power because of the often large number of degrees of freedom involved. The ideal method must make full use of important information from multiple loci but avoid increasing the degrees of freedom. Therefore, we propose a method to capture information from multiple SNPs but with the use of fewer degrees of freedom. When a set of SNPs in a block are correlated because of LD, we might expect that the genotype variation among the different phenotypic groups would extend across all the SNPs, and this information could be compressed into the low-frequency components of a Fourier transform. Therefore, we develop a test based on weighted Fourier transformation coefficients, with more weight given to the low-frequency components. Our simulation results demonstrate the validity and substantially higher power of the proposed method compared with other common methods. This method provides an additional tool to existing methods for identification of causative genetic variants underlying complex diseases. [ABSTRACT FROM AUTHOR]

Multivariate linkage analysis has been suggested for the analysis of correlated traits, such as blood pressure (BP) and body mass index (BMI), because it may offer greater power and provide clearer results than univariate analyses. Currently, the most commonly used multivariate linkage methods are extensions of the univariate variance component model. One concern about those methods is their inherent sensitivity to the assumption of multivariate normality which cannot be easily guaranteed in practice. Another problem possibly related to all multivariate linkage analysis methods is the difficulty in interpreting nominal p-values, because the asymptotic distribution of the test statistic has not been well characterized. Here we propose a regression-based multivariate linkage method in which a robust score statistic is used to detect linkage. The p-value of the statistic is evaluated by a simple and rapid simulation procedure. Theoretically, this method can be used for any number and type of traits and for general pedigree data. We apply this approach to a genome linkage analysis of blood pressure and body mass index data from the Beaver Dam Eye Study. [ABSTRACT FROM AUTHOR]

The past 25 years has seen an explosion in the number of genetic markers that can be measured on DNA samples at an ever decreasing cost. Although basic statistical methods for analysing such data gathered on samples of either independent individuals or family members, one or two markers at a time, were already well developed before this explosion occurred, there has been a corresponding burst in activity to develop multiple marker models to find disease-causing gene variants, capitalizing on the data that have become available, to increase the power of such methods. This has required the concomitant development of faster algorithms to speed up the computation of various likelihoods. For linkage analysis, to obtain the approximate locations for genes of interest, Mendelian segregation models have been extended to be more realistic and statistical models that do not assume specific modes of inheritance have been extended to allow for the analysis of larger pedigree structures. For association analysis, to obtain more precise locations for genes of interest, the recent completion of the first stage of the HapMap project has spurred the development, still underway, of novel experimental designs and analytical methods to combat the curse of dimensionality and the resulting multiple testing problem. Perhaps the greatest current challenge concerns how best to gather and synthesize the many lines of evidence possible in order to discover the genetic determinants underlying complex diseases. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present a new method for fine-mapping a disease susceptibility locus using a case-control design. The new method, termed the 'weighted average (WA) statistic', averages the Cochran-Armitage (CA) trend test statistic and the difference between the Hardy Weinberg disequilibrium test statistics (the HWD trend) for cases and controls. The main features of the WA statistic are that it mitigates against the weaknesses, and maintains the strong points, of both the CA trend test and the HWD trend test. To allow for the extra variance induced by population structure and cryptic relatedness, the WA statistic can be adjusted for variance inflation. Based on the results of a simulation study, when there is no population structure the WA test statistic shows good performance under a variety of genetic disease models. When there is population structure, the adjusted WA statistic maintains the correct probability of type I error. Under all genetic disease models investigated, the adjusted WA statistic has better power than the adjusted CA trend test, the HWD trend test or the product of the adjusted CA trend test and the HWD trend test statistics. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Heritability of renal function in hypertensive families of African descent in the Seychelles (Indian Ocean).Background.We estimated the heritability of three measures of glomerular filtration rate (GFR) in hypertensive families of African descent in the Seychelles (Indian Ocean).Methods.Families with at least two hypertensive siblings and an average of two normotensive siblings were identified through a national hypertension register. Using the ASSOC program in SAGE (Statistical Analysis in Genetic Epidemiology), the age- and gender-adjusted narrow sense heritability of GFR was estimated by maximum likelihood assuming multivariate normality after power transformation. ASSOC can calculate the additive polygenic component of the variance of a trait from pedigree data in the presence of other familial correlations. The effects of body mass index (BMI), blood pressure, natriuresis, along with sodium to potassium ratio in urine and diabetes, were also tested as covariates.Results.Inulin clearance, 24-hour creatinine clearance, and GFR based on the Cockcroft-Gault formula were available for 348 persons from 66 pedigrees. The age- and gender-adjusted correlations (± SE) were 0.51 (± 0.04) between inulin clearance and creatinine clearance, 0.53 (± 0.04) between inulin clearance and Cockcroft-Gault formula and 0.66 (± 0.03) between creatinine clearance and Cockcroft-Gault formula. The age- and gender-adjusted heritabilities (± SE) of GFR were 0.41 (± 0.10) for inulin clearance, 0.52 (± 0.13) for creatinine clearance, and 0.82 (± 0.09) for Cockcroft-Gault formula. Adjustment for BMI slightly lowered the correlations and heritabilities for all measurements whereas adjustment for blood pressure had virtually no effect.Conclusion.The significant heritability estimates of GFR in our sample of families of African descent confirm the familial aggregation of this trait and justify further analyses aimed at discovering genetic determinants of GFR. [ABSTRACT FROM AUTHOR]

Affected sib pair (ASP) analysis has become common ever since it was shown that, under very specific assumptions, ASPs afford a powerful design for linkage analysis. In 2003, Vieland and Huang, on the basis of a "fundamental heterogeneity equation," proved that heterogeneity and epistasis are confounded in ASP linkage analysis. A much more serious limitation of ASP linkage analysis is the implicit assumption that randomly sampled sib pairs share half their alleles identical by descent at any locus, whereas a critical assumption underlying Vieland and Huang's proof is that of joint Hardy-Weinberg equilibrium proportions at two trait loci. These are considered as examples of mathematical assumptions that may not always reflect biological reality. More-robust sib-pair designs and appropriate methods for their analysis have long been available. [ABSTRACT FROM AUTHOR]

Background: Maximum likelihood estimates of haplotype frequencies can be obtained from pooled DNA using the expectation maximization (EM) algorithm. Through simulation, we investigate the effect of genotyping error on the accuracy of haplotype frequency estimates obtained using this algorithm. We explore model parameters including allele frequency, intermarker linkage disequilibrium (LD), genotyping error rate, and pool size. Results: Pool sizes of 2, 5, and 10 individuals achieved comparable levels of accuracy in the estimation procedure. Common marker allele frequencies and no inter-marker LD result in less accurate estimates. This pattern is observed regardless of the amount of genotyping error simulated. Conclusion: Genotyping error slightly decreases the accuracy of haplotype frequency estimates. However, the EM algorithm performs well even in the presence of genotyping error. Overall, pools of 2, 5, and 10 individuals yield similar accuracy of the haplotype frequency estimates, while reducing costs due to genotyping. [ABSTRACT FROM AUTHOR]

To assess lumbar puncture (LP) stress as measured by Hypothalamic-Pituitary-Adrenal axis response, serum cortisols were measured before and after LP in Alzheimer's disease (AD) and healthy elderly individuals. There were no differences in baseline serum cortisol. AD group had significantly higher cortisols at 165 minutes post-LP. Growth curve analysis confirmed these findings and showed significant differences in cortisol levels overall. The AD males had higher cortisols at 105 minutes post-LP, but no other consistent gender differences emerged. The findings are consistent with the relative reduction in HPA negative feedback seen in AD. Overall, LP induces little change in group mean cortisol levels relative to inter-individual variation, indicating that while LP is an interesting model of neuroendocrine challenge, it needs to be tested in larger populations. [ABSTRACT FROM AUTHOR]

Genetic epidemiology is currently undergoing a paradigm shift. During most of the last half century, statistical human genetics focused on identifying and locating on the chromosomes genetic loci that control the segregation of rare monogenic diseases. The paradigm was one of first detecting Mendelian segregation in large families and then, with full knowledge of the mode of inheritance of the trait being studied, using linkage analysis to locate the segregating locus eventually leading to cloning of the gene involved. In linkage analysis we examine family data with a view to detecting the co-segregation of a disease trait with a well-defined genetic marker nowadays a piece of DNA whose chromosomal location is established-hence inferring that the disease causing allele is itself at a locus on a particular chromosome near that marker locus.

Introduction: Chronic hepatitis B virus (HBV) infection is associated with significant global morbidity and mortality. Low treatment rates are observed in patients living with HBV; the reasons for this are unclear. This study sought to describe patients' demographic, clinical and biochemical characteristics across three continents and their associated treatment need. Methods: This retrospective cross-sectional post hoc analysis of real-world data used four large electronic databases from the United States, United Kingdom and China (specifically Hong Kong and Fuzhou). Patients were identified by first evidence of chronic HBV infection in a given year (their index date) and characterized. An algorithm was designed and applied, wherein patients were categorized as treated, untreated but indicated for treatment and untreated and not indicated for treatment based on treatment status and demographic, clinical, biochemical and virological characteristics (age; evidence of fibrosis/cirrhosis; alanine aminotransferase [ALT] levels, HCV/HIV coinfection and HBV virology markers). Results: In total, 12,614 US patients, 503 UK patients, 34,135 patients from Hong Kong and 21,614 from Fuzhou were included. Adults (99.4%) and males (59.0%) predominated. Overall, 34.5% of patients were treated at index (range 15.9–49.6%), with nucleos(t)ide analogue monotherapy most commonly prescribed. The proportion of untreated-but-indicated patients ranged from 12.9% in Hong Kong to 18.2% in the UK; almost two-thirds of these patients (range 61.3–66.7%) had evidence of fibrosis/cirrhosis. A quarter (25.3%) of untreated-but-indicated patients were aged ≥ 65 years. Conclusion: This large real-world dataset demonstrates that chronic hepatitis B infection remains a global health concern; despite the availability of effective suppressive therapy, a considerable proportion of predominantly adult patients apparently indicated for treatment are currently untreated, including many patients with fibrosis/cirrhosis. Causes of disparity in treatment status warrant further investigation. [ABSTRACT FROM AUTHOR]

Pays homage to the late Jane M. Olson, a renowned figure in the field of biostatistical genetics.

Investigates the revision of the Haseman and Elston model for testing linkage between polymorphic marker and trait locus. Regression of the squared sib-pair trait difference; Calculation of the mean-corrected cross-product; Use of marker allele frequencies.

This paper is concerned with the estimation of the logarithm of disease odds (log odds) when evaluating two risk factors, whether or not interactions are present. Statisticians define interaction as a departure from an additive model on a certain scale of measurement of the outcome. Certain interactions, known as removable interactions, may be eliminated by fitting an additive model under an invertible transformation of the outcome. This can potentially provide more precise estimates of log odds than fitting a model with interaction terms. In practice, we may also encounter nonremovable interactions. The model must then include interaction terms, regardless of the choice of the scale of the outcome. However, in practical settings, we do not know at the outset whether an interaction exists, and if so whether it is removable or nonremovable. Rather than trying to decide on significance levels to test for the existence of removable and nonremovable interactions, we develop a Bayes estimator based on a squared error loss function. We demonstrate the favorable bias-variance trade-offs of our approach using simulations, and provide empirical illustrations using data from three published endometrial cancer case-control studies. The methods are implemented in an R program, and available freely at http://www.mskcc.org/biostatistics/~satagopj . [ABSTRACT FROM AUTHOR]

The article focuses on gene-gene interaction detection in diseases without statistical interaction. It references the article "Detecting gene-gene interactions that underlie human diseases," by H. J. Cordell in the 2009 issue. It tackles the need to jointly interpret statistical interaction and effect terms. It tells that statistical interaction may not be engaged with statistical models since it is population-level and since non-additivity's presence and magnitude are scale and model reliant.

Examines the relationship between the sibling disease recurrence-risk ratio and genotype relative risk. Variations of disease in siblings with respect to genotype relative risk; Introduction of restricted sibling recurrence-risk ratio (RSRRR); Use of RSRRR in testing the posited susceptibility allele.

A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete-time survival model. Results from both a study on the occurrence of tuberculosis and simulated data show that the joint model is superior to the other models in discrimination ability, especially as the latent variables related to both survival times and the longitudinal measurements depart from 0. [ABSTRACT FROM AUTHOR]

Background: The growing advances in DNA sequencing tools have made analyzing the human genome cheaper and faster. While such analyses are intended to identify complex variants, related to disease susceptibility and efficacy of drug responses, they have blurred the definitions of mutation and polymorphism. Discussion: In the era of personal genomics, it is critical to establish clear guidelines regarding the use of a reference genome. Nowadays DNA variants are called as differences in comparison to a reference. In a sequencing project Single Nucleotide Polymorphisms (SNPs) and DNA mutations are defined as DNA variants detectable in >1 % or <1 % of the population, respectively. The alternative use of the two terms mutation or polymorphism for the same event (a difference as compared with a reference) can lead to problems of classification. These problems can impact the accuracy of the interpretation and the functional relationship between a disease state and a genomic sequence. Summary: We propose to solve this nomenclature dilemma by defining mutations as DNA variants obtained in a paired sequencing project including the germline DNA of the same individual as a reference. Moreover, the term mutation should be accompanied by a qualifying prefix indicating whether the mutation occurs only in somatic cells (somatic mutation) or also in the germline (germline mutation). We believe this distinction in definition will help avoid confusion among researchers and support the practice of sequencing the germline and somatic tissues in parallel to classify the DNA variants thus defined as mutations. [ABSTRACT FROM AUTHOR]

Background: The growing advances in DNA sequencing tools have made analyzing the human genome cheaper and faster. While such analyses are intended to identify complex variants, related to disease susceptibility and efficacy of drug responses, they have blurred the definitions of mutation and polymorphism. Discussion: In the era of personal genomics, it is critical to establish clear guidelines regarding the use of a reference genome. Nowadays DNA variants are called as differences in comparison to a reference. In a sequencing project Single Nucleotide Polymorphisms (SNPs) and DNA mutations are defined as DNA variants detectable in >1 % or <1 % of the population, respectively. The alternative use of the two terms mutation or polymorphism for the same event (a difference as compared with a reference) can lead to problems of classification. These problems can impact the accuracy of the interpretation and the functional relationship between a disease state and a genomic sequence. Summary: We propose to solve this nomenclature dilemma by defining mutations as DNA variants obtained in a paired sequencing project including the germline DNA of the same individual as a reference. Moreover, the term mutation should be accompanied by a qualifying prefix indicating whether the mutation occurs only in somatic cells (somatic mutation) or also in the germline (germline mutation). We believe this distinction in definition will help avoid confusion among researchers and support the practice of sequencing the germline and somatic tissues in parallel to classify the DNA variants thus defined as mutations. [ABSTRACT FROM AUTHOR]

Summary: Immunoglobulin (Ig) GM and KM allotypes—genetic markers of γ and κ chains, respectively—are associated with the outcome of hepatitis C virus (HCV) infection, but the underlying mechanisms are not well understood. We hypothesized that GM and KM allotypes could contribute to the outcome of HCV infection by influencing the levels of IgG antibodies to the HCV glycoproteins E1E2. We serologically allotyped 100 African American individuals with persistent HCV infection for GM and KM markers and measured anti-E1E2 antibodies. Subjects with the GM 1,17 5,13 phenotype had significantly higher levels of anti-E1E2 antibodies than subjects who lacked this phenotype (p = 0.008). Likewise, subjects with the KM 1–carrying phenotypes had higher levels of anti-E1E2 antibodies than subjects who lacked these phenotypes (p = 0.041). Median titers were fourfold higher in persons expressing both GM 1,17 5,13 and KM 1–carrying phenotypes compared with those who lacked these phenotypes (p = 0.011). Interactive effects of these GM-KM phenotypes were previously found to be highly significantly associated with spontaneous HCV clearance. Results presented here show that Ig allotypes contribute to the interindividual differences in humoral immunity to the HCV epitopes, a finding that may provide a mechanistic explanation for their involvement in the outcome of HCV infection. [Copyright &y& Elsevier]

Genetic association studies offer an opportunity to find genetic variants underlying complex human diseases. The success of this approach depends on the linkage disequilibrium (LD) between markers and the disease variant(s) in a local region of the genome. Because, in the region with a disease mutation, the LD pattern among markers may differ between cases and controls, in some scenarios, it is useful to compare a measure of this LD, to map disease mutations. For example, using the composite correlation to characterize the LD among markers, Zaykin et al. recently suggested an ‘LD contrast’ test and showed that it has high power under certain haplotype-driven disease models. Furthermore, it is likely that individual variants observed at different positions in a gene act jointly with each other to influence the phenotype, and the LD contrast test is also a useful method to detect such joint action. However, the LD among markers introduced by mutations and their joint action is usually confounded by background LD, which is measured at the population level, especially in a local region with disease mutations. Because the measures of LD that are usually used, such as the composite correlation, represent both effects, they may not be optimal for the purpose of detecting association when high background LD exists. Here, we describe a test that improves the LD contrast test by taking into account the background LD. Because the proposed test is developed in a regression framework, it is very flexible and can be extended to continuous traits and to incorporate covariates. Our simulation results demonstrate the validity and substantially higher power of the proposed method over current methods. Finally, we illustrate our new method by applying it to real data from the International Collaborative Study on Hypertension in Blacks. [ABSTRACT FROM AUTHOR]

Previously, we reported an algorithm for scanning a large number of tightly linked single nucleotide polymorphisms (SNPs) for LD mapping of functional sites or regions from a family-based association design. In the present study, we extend our method to a case-control design. We first use the expectation maximization (EM) algorithm to estimate haplotype frequencies of multiple linked SNPs, and follow this by constructing a contingency table statisticSfor LD analysis, based on the estimated haplotype frequencies. An empiricalp-value is obtained based on the null distribution of the maximum ofS(S *) from a large number (e.g., 1,000 or more) of randomized permutations. The proposed algorithm has been implemented in a computer program in which window searching for functional SNP sites can cover any number of loci without limitation, except that of computer storage. Unlike other programs for a case-control design that always conduct tests at a fix window width, in our program after setting a maximum size of haplotype window width, for a given maximum window width all possible widths of haplotypes are utilized to find the maximum statisticS * for each locus under investigation. The sensitivity of the proposed algorithm has been examined with simulated and real genotyping datasets. Association analyses indicate that our program is powerful enough to detect most, if not all, functional SNPs simulated in the original model or identified in the original report. Moreover, the program is very flexible and can be used in either regional or genome-wide scanning for association analysis with SNP markers. [ABSTRACT FROM AUTHOR]

Linkage disequilibrium arising from the recent admixture of genetically distinct populations can be potentially useful in mapping genes for complex diseases. McKeigue has proposed a method that conditions on parental admixture to detect linkage. We show that this method tests for linkage only under specific assumptions, such as equal admixture in the parental generation and admixture that occurs in a single generation. In practice, these assumptions are unlikely to hold for natural populations, resulting in an inflation of the type I error rate when testing for linkage by this method. In this article, we generalize McKeigue's approach of testing for linkage to allow two different admixture models: (1) intermixture admixture and (2) continuous gene flow. We calculate the sample size required for a genomewide search by this method under different disease models: multiplicative, additive, recessive, and dominant. Our results show that the sample size required to obtain 90% power to detect a putative mutant allele at a genomewide significance level of 5 % can usually be achieved in practice if informative markers are available at a density of 2 cM. [ABSTRACT FROM AUTHOR]

There is currently a great deal of interest in using linkage disequilibrium (LD) mapping to locate both disease and quantitative-trait loci on a genomewide scale. Recent findings suggest that much of the human genome is organized in discrete "blocks" of low haplotype diversity, but the utility of such blocks in identifying genes influencing complex traits is not yet known and must ultimately be tested empirically through use of real data. We recently identified a putative functional polymorphism (-1021C→T) in the 5 upstream region of the DBH gene that accounted for 35%-52% of the total phenotypic variance in plasma dopamine β-hydroxylase (DBH) activity in samples from three distinct populations. In the present study, we genotyped 11 diallelic markers at the DBH locus surrounding - 1021C→T in 386 unrelated individuals of European origin. We identified a single 10-kb block containing - 1021C→T, in which four haplotypes comprised 93% of the observed chromosomes. Only markers within the block were highly associated with phenotype (P ≤ 2.2 × 10[SUP-10]), with one exception. In general, association with phenotype was strongly correlated with the degree of LD between each marker and - 1021C→T. Of four LD measures assessed, d[SUP2] was the best predictor of this relationship. Had one attempted to map quantitative-trait loci for plasma DBH activity on a genomewide basis without prior knowledge of candidate regions and not included (by chance) markers within this haplotype block, the DBH locus might have been missed entirely. These results provide a direct example of the potential value of constructing a haplotype map of the human genome prior to embarking on large-scale association studies. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies." The original article was published without open access, but the error has been resolved and the article is now available under a Creative Commons Attribution 4.0 International License. The correction also includes the removal of a duplicate reference and the addition of an affiliation for one of the authors. The publisher, Springer Nature, remains neutral in terms of jurisdictional claims and institutional affiliations. [Extracted from the article]

In this review of 7,464 consecutive infants born at North Carolina Memorial Hospital, hemolytic disease from ABO incompatibility was found to be two to three times as common in black infants as in white infants. The statistical significance of the difference remained high as more restrictive criteria for ABO hemolytic disease were applied. ABO disease, serious enough to cause an indirect serum bilirubin of 15 mg/100ml or higher, had an incidence in black newborns as great as the incidence of Rh hemolytic disease in whites. In contrast, the general prevalence and severity of hyperbilirubinemia was not found to be higher in black newborns than in white infants. The difference cannot be attributed to differences in the prevalence of ABO blood groups between the two races. Policies of early discharge of newborns could be affected by the finding that ABO erythroblastosis is two to three times as common in black infants as in white infants. [ABSTRACT FROM AUTHOR]

Local ancestry, defined as the genetic ancestry at a genomic location of an admixed individual, is widely used as a genetic marker in genetic association and evolutionary genetics studies. Many methods have been developed to infer the local ancestries in a set of unrelated individuals, a few of them have been extended to small nuclear families, but none can be applied to large (e.g. three-generation) pedigrees. In this study, we developed a method, FamANC, that can improve the accuracy of local ancestry inference in large pedigrees by: (1) using an existing algorithm to infer local ancestries for all individuals in a family, assuming (contrary to fact) they are unrelated, and (2) improving its accuracy by correcting inference errors using pedigree structure. Applied on African-American pedigrees from the Cleveland Family Study, FamANC was able to correct all identified Mendelian errors and most of double crossovers. [ABSTRACT FROM AUTHOR]

Background: Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest. Results: For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples. Conclusions: We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers. [ABSTRACT FROM AUTHOR]

We tested for germline variants showing association to colon cancer metastasis using a genome-wide association study that compared Ashkenazi Jewish individuals with stage IV metastatic colon cancers versus those with stage I or II non-metastatic colon cancers. In a two-stage study design, we demonstrated significant association to developing metastatic disease for rs60745952, that in Ashkenazi discovery and validation cohorts, respectively, showed an odds ratio (OR) = 2.3 (P = 2.73E-06) and OR = 1.89 (P = 8.05E-04) (exceeding validation threshold of 0.0044). Significant association to metastatic colon cancer was further confirmed by a meta-analysis of rs60745952 in these datasets plus an additional Ashkenazi validation cohort (OR = 1.92; 95% CI: 1.28–2.87), and by a permutation test that demonstrated a significantly longer haplotype surrounding rs60745952 in the stage IV samples. rs60745952, located in an intergenic region on chromosome 4q31.1, and not previously associated with cancer, is, thus, a germline genetic marker for susceptibility to developing colon cancer metastases among Ashkenazi Jews. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD. [ABSTRACT FROM AUTHOR]

For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated—in particular the amount of variance attributable to common environment in twins can be substantial—which indicates heritability estimates should be interpreted with caution. [ABSTRACT FROM AUTHOR]

We used whole-exome and targeted sequencing to characterize somatic mutations in 103 colorectal cancers (CRC) from African Americans, identifying 20 new genes as significantly mutated in CRC. Resequencing 129 Caucasian derived CRCs confirmed a 15-gene set as a preferential target for mutations in African American CRCs. Two predominant genes, ephrin type A receptor 6 (EPHA6) and folliculin (FLCN), with mutations exclusive to African American CRCs, are by genetic and biological criteria highly likely African American CRC driver genes. These previously unsuspected differences in the mutational landscapes of CRCs arising among individuals of different ethnicities have potential to impact on broader disparities in cancer behaviors. [ABSTRACT FROM AUTHOR]

Background Age-of-onset (AO) is increasingly used in molecular genetics of bipolar I disorder (BP-I) as a phenotypic specifier with the goal of reducing genetic heterogeneity. However, questions regarding the cut-off age for defining early onset (EO), as well as the number of onset groups characterizing BP-I have emerged over the last decade with no definite conclusion. The aims of this paper are: 1) to see whether a mixture of three distributions better describes the AO of BP-I than a mixture of two distributions in different independent samples; 2) to compare the morbid risk (MR) for BP-I and for major affective disorders and schizophrenia in first degree relatives of BP-I probands by proband onset group derived from commingling analysis, since the MR to relatives is a trait with strong genetic background. Methods We applied commingling (admixture) analysis to the AO of three BP-I samples from Romania (n=621), Germany (n=882), and Poland (n=354). Subsequently, the morbid risk (MR) for BP-I and for major psychoses (BP-I, BP-II, Mdd-UP, schizoaffective disorders, schizophrenia) was estimated in first degree relatives by proband AO-group derived from admixture analysis in the Romanian sample. Results In the three independent samples and in the combined sample two- and three-AO-group distributions fitted the empirical data equally well. The upper EO limit varied between 21 and 25 years from sample to sample. The MR for both BP-I and for all major psychoses was similar in first degree relatives of EO probands (AO?21) and in relatives of intermediate-onset probands (AO=22-34). Significant MR differences appeared only when comparing the EO group to the late-onset (LO) group (AO>34). Similar to Mdd-UP and schizophrenia, a significant MR decrease in proband first degree relatives was visible after proband AO of 34 years. Under the three-AO-group classification the MR for both BP-I and all major psychoses in first degree relatives did not differ by relative sex in any proband AO-group. Under the two-AO-group classification female relatives of LO probands (AO>24) had a significantly higher MR for all major psychoses than male relatives, while there was no sex difference for the relatives of EO probands. Limitations MR was not computed in the German and Polish samples because family data were not available and 34% of the relatives of the Romanian probands were not available for direct interview. Conclusion Similar to other clinical traits, the MR for major psychoses to relatives failed to support a three-AO-group classification in BP-I suggesting that this is not more useful for the molecular analysis than a two-AO-group classification. [ABSTRACT FROM AUTHOR]

High blood pressure (BP) is the most common cardiovascular risk factor worldwide and a major contributor to heart disease and stroke. We previously discovered a BP-associated missense SNP (single nucleotide polymorphism)–rs2272996–in the gene encoding vanin-1, a glycosylphosphatidylinositol (GPI)-anchored membrane pantetheinase. In the present study, we first replicated the association of rs2272996 and BP traits with a total sample size of nearly 30,000 individuals from the Continental Origins and Genetic Epidemiology Network (COGENT) of African Americans (P = 0.01). This association was further validated using patient plasma samples; we observed that the N131S mutation is associated with significantly lower plasma vanin-1 protein levels. We observed that the N131S vanin-1 is subjected to rapid endoplasmic reticulum-associated degradation (ERAD) as the underlying mechanism for its reduction. Using HEK293 cells stably expressing vanin-1 variants, we showed that N131S vanin-1 was degraded significantly faster than wild type (WT) vanin-1. Consequently, there were only minimal quantities of variant vanin-1 present on the plasma membrane and greatly reduced pantetheinase activity. Application of MG-132, a proteasome inhibitor, resulted in accumulation of ubiquitinated variant protein. A further experiment demonstrated that atenolol and diltiazem, two current drugs for treating hypertension, reduce the vanin-1 protein level. Our study provides strong biological evidence for the association of the identified SNP with BP and suggests that vanin-1 misfolding and degradation are the underlying molecular mechanism. [ABSTRACT FROM AUTHOR]