Definition of the phenotype

Computational phenotyping of obstructive airway diseases: protocol for a systematic review

BACKGROUND

Over the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults.

METHODS AND ANALYSIS

We will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological studies that used a computational approach to derive phenotypes of chronic airway diseases, whether in a general population or in a clinical setting. Two reviewers will independently screen the retrieved studies for eligibility, extract relevant data, and perform quality appraisal of included studies. A third reviewer will arbitrate any disagreements in these processes. Quality appraisal of the studies will be undertaken using the Effective Public Health Practice Project quality assessment tool. We will use summary tables to describe the included studies. We will narratively synthesize the generated evidence, providing critical assessment of the populations, variables, and computational approaches used in deriving the phenotypes across studies CONCLUSION: As progress continues to be made in the area of computational phenotyping of chronic obstructive airway diseases, this systematic review, the first on this topic, will provide the state of the art on the field and highlight important perspectives for future works.

ETHICS AND DISSEMINATION

No ethical approval is needed for this work is based only on the published literature and does not involve collection of any primary or human data.

REGISTRATION AND REPORTING

SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020164898.

MD, Eckel-Passow J, Acosta A. Precision Medicine for Obesity

Obesity is a multifactorial disease with a variable and underwhelming weight loss response to current treatment approaches. Precision medicine proposes a new paradigm to improve disease classification based on the premise of human heterogeneity, with the ultimate goal of maximizing treatment effectiveness, tolerability, and safety. Recent advances in high-throughput biochemical assays have contributed to the partial characterization of obesity's pathophysiology, as well as to the understanding of the role that intrinsic and environmental factors, and their interaction, play in its development and progression. These data have led to the development of biological markers that either are being or will be incorporated into strategies to develop personalized lines of treatment for obesity. There are currently many ongoing initiatives aimed at this; however, much needs to be resolved before precision obesity medicine becomes common practice. This review aims to provide a perspective on the currently available data of high-throughput technologies to treat obesity.

Data-driven questionnaire-based cluster analysis of asthma in Swedish adults

The aim of this study was to identify asthma phenotypes through cluster analysis. Cluster analysis was performed using self-reported characteristics from a cohort of 1291 Swedish asthma patients. Disease burden was measured using the Asthma Control Test (ACT), the mini Asthma Quality of Life Questionnaire (mini-AQLQ), exacerbation frequency and asthma severity. Validation was performed in 748 individuals from the same geographical region. Three clusters; early onset predominantly female, adult onset predominantly female and adult onset predominantly male, were identified. Early onset predominantly female asthma had a higher burden of disease, the highest exacerbation frequency and use of inhaled corticosteroids. Adult onset predominantly male asthma had the highest mean score of ACT and mini-AQLQ, the lowest exacerbation frequency and higher proportion of subjects with mild asthma. These clusters, based on information from clinical questionnaire data, might be useful in primary care settings where the access to spirometry and biomarkers is limited.

Moving toward consensus on diagnosis and management of severe asthma in children

Children with severe asthma continue to experience symptoms despite long-term treatment with high doses of corticosteroids. Moreover, the heterogeneous nature of asthma and the presence of several phenotypes have limited our ability to develop an optimized management strategy for these patients. Adequate management of severe asthma in children necessitates a detailed understanding of what makes asthma difficult to control, knowledge of the causal factors, review of diagnosis for accurate identification of pediatric patients with severe asthma and a precise definition of the phenotypes to be able to better target the therapy. Advancement in all these aspects is likely to improve childhood asthma treatment in the future. Although our understanding of severe pediatric asthma has grown in recent years, there remains a lack of consensus and clarity around critical aspects of this condition. This review attempts to present a harmonized view on the definition of severe asthma in the pediatric age group, identification of phenotypes and diagnosis, the inflammatory cascade, pharmacological and non-pharmacological treatment strategies, considerations for follow-up and referral to specialists, and disease prevention strategies.

Novel polymorphisms in TICAM2 and NOD1 associated with tuberculosis progression phenotypes in Ethiopian populations

BACKGROUND

Infection by Mycobacterium tuberculosis (Mtb) is a necessary but not sufficient cause for tuberculosis (TB). Although numerous studies suggest human genetic variation may influence TB pathogenesis, there is a conspicuous lack of replication, likely due to imprecise phenotype definition. We aimed to replicate novel findings from a Ugandan cohort in Ethiopian populations.

METHOD

We ascertained TB cases and household controls (n = 292) from three different ethnic groups. Latent Mtb infection was determined using Quantiferon to develop reliable TB progression phenotypes. We sequenced exonic regions of TICAM2 and NOD1.

RESULT

Significant novel associations were observed between two variants in NOD1 and TB: rs751770147 [unadjusted p = 7.28 × 10-5] and chr7:30477156(T), a novel variant, [unadjusted p = 1.04 × 10-4]. Two SNPs in TICAM2 were nominally associated with TB, including rs2288384 [unadjusted p = 0.003]. Haplotype-based association tests supported the SNP-based results.

CONCLUSION

We replicated the association of TICAM2 and NOD1 with TB and identified novel genetic associations with TB in Ethiopian populations.

Phenotyping Before Starting Treatment in COPD?

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous and complex disease with great morbidity and mortality. Despite the new developments in the managements of COPD, it was recognized that not all patients benefit from the available medications. Therefore, efforts to identify subgroups or phenotypes had been made in order to predict who will respond to a class of drugs for COPD. This review will discuss phenotypes, endotypes, and subgroups such as the frequent exacerbator, the one with systemic inflammation, the fast decliner, ACOS, and the one with co-morbidities and their impact on therapy. It became apparent, that the "inflammatory" phenotypes: frequent exacerbator, chronic bronchitic, and those with a number of co-morbidities need inhaled corticosteroids; in contrast, the emphysematous type with dyspnea and lung hyperinflation, the fast decliner, need dual bronchodilation (deflators). However, larger, well designed studies clustering COPD patients are needed, in order to identify the important subgroups and thus, to lead to personalize management in COPD.

Big Data Analytics for Genomic Medicine

Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Genomic variants, genes, and pathways of Alzheimer's disease: An overview

Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia. With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets. Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately ∼20 genes with common variants contributing to LOAD risk. In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses. Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple low-frequency and rare variant contributors to AD, and discuss on-going efforts with next-generation sequencing studies to develop statistically well- powered and comprehensive genomic studies of AD. Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD. © 2016 Wiley Periodicals, Inc.

A genome-wide association study identifies variants in KCNIP4 associated with ACE inhibitor-induced cough

The most common side effect of angiotensin-converting enzyme inhibitor (ACEi) drugs is cough. We conducted a genome-wide association study (GWAS) of ACEi-induced cough among 7080 subjects of diverse ancestries in the Electronic Medical Records and Genomics (eMERGE) network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1595 cases and 5485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (minor allele frequency=0.33, odds ratio (OR)=1.3 (95% confidence interval (CI): 1.2–1.4), P=1.0 × 10⁻⁸). Replication for six single-nucleotide polymorphisms (SNPs) in KCNIP4 was tested in a second eMERGE population (n=926) and in the Genetics of Diabetes Audit and Research in Tayside, Scotland (GoDARTS) cohort (n=4309). Replication was observed at rs7675300 (OR=1.32 (1.01–1.70), P=0.04) in eMERGE and at rs16870989 and rs1495509 (OR=1.15 (1.01–1.30), P=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 (1.15–1.32), P=1.9 × 10⁻⁹). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.

Association between AVPR1A, DRD2, and ASPM and endophenotypes of communication disorders

OBJECTIVES

Speech sound disorder (SSD) is one of the most common communication disorders, with a prevalence rate of 16% at 3 years of age, and an estimated 3.8% of children still presenting speech difficulties at 6 years of age. Several studies have identified promising associations between communication disorders and genes in brain and neuronal pathways; however, there have been few studies focusing on SSD and its associated endophenotypes. On the basis of the hypothesis that neuronal genes may influence endophenotypes common to communication disorders, we focused on three genes related to brain and central nervous system functioning: the dopamine D2 receptor (DRD2) gene, the arginine-vasopressin receptor 1a (AVPR1A) gene, and the microcephaly-associated protein gene (ASPM).

METHODS

We examined the association of these genes with key endophenotypes of SSD - phonological memory measured through multisyllabic and nonword repetition, vocabulary measured using the Expressive One Word Picture Vocabulary Test and Peabody Picture Vocabulary Test, and reading decoding measured using the Woodcock Reading Mastery Tests Revised - as well as with the clinical phenotype of SSD. We genotyped tag single nucleotide polymorphisms in these genes and examined 498 individuals from 180 families.

RESULTS

These data show that several single nucleotide polymorphisms in all three genes were associated with phonological memory, vocabulary, and reading decoding, with P less than 0.05. Notably, associations in AVPR1A (rs11832266) were significant after multiple testing correction. Gene-level tests showed that DRD2 was associated with vocabulary, ASPM with vocabulary and reading decoding, and AVPR1A with all three endophenotypes.

CONCLUSION

Endophenotypes common to SSD, language impairment, and reading disability are all associated with these neuronal pathway genes.

Relational machine learning for electronic health record-driven phenotyping

OBJECTIVE

Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping.

METHODS

Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance.

RESULTS

We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003).

DISCUSSION

ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.

CONCLUSION

Relational learning using ILP offers a viable approach to EHR-driven phenotyping.

Testing the effectiveness of a self-efficacy based exercise intervention for adults with venous leg ulcers: protocol of a randomised controlled trial

BACKGROUND

Exercise and adequate self-management capacity may be important strategies in the management of venous leg ulcers. However, it remains unclear if exercise improves the healing rates of venous leg ulcers and if a self-management exercise program based on self-efficacy theory is well adhered to.

METHOD/DESIGN

This is a randomised controlled in adults with venous leg ulcers to determine the effectiveness of a self-efficacy based exercise intervention. Participants with venous leg ulcers are recruited from 3 clinical sites in Australia. After collection of baseline data, participants are randomised to either an intervention group or control group. The control group receive usual care, as recommended by evidence based guidelines. The intervention group receive an individualised program of calf muscle exercises and walking. The twelve week exercise program integrates multiple elements, including up to six telephone delivered behavioural coaching and goal setting sessions, supported by written materials, a pedometer and two follow-up booster calls if required. Participants are encouraged to seek social support among their friends, self-monitor their weekly steps and lower limb exercises. The control group are supported by a generic information sheet that the intervention group also receive encouraging lower limb exercises, a pedometer for self-management and phone calls at the same time points as the intervention group. The primary outcome is the healing rates of venous leg ulcers which are assessed at fortnightly clinic appointments. Secondary outcomes, assessed at baseline and 12 weeks: functional ability (range of ankle motion and Tinetti gait and balance score), quality of life and self-management scores.

DISCUSSION

This study seeks to address a significant gap in current wound management practice by providing evidence for the effectiveness of a home-based exercise program for adults with venous leg ulcers. Theory-driven, evidence-based strategies that can improve an individual's exercise self-efficacy and self-management capacity could have a significant impact in improving the management of people with venous leg ulcers. Information gained from this study will provide much needed information on management of this chronic disease to promote health and independence in this population.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12612000475842.

Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory

BACKGROUND AND OBJECTIVE

Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical 'phenotypes' accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one 'gold standard' chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across different definitions.

MATERIALS AND METHODS

We used retrospective queries of hospitalizations (2002-2011) in the Indiana Network for Patient Care with any CHF ICD-9 diagnoses, a primary diagnosis, an echocardiogram performed, a B-natriuretic peptide (BNP) drawn, or BNP >500 pg/mL. We used a hybrid between proportional sampling by Venn zone and over-sampling non-overlapping zones. The acute CHF (presence/absence) outcome was based on expert chart review using a priori criteria.

RESULTS

Among 79,091 hospitalizations, we reviewed 908. A query for any ICD-9 code for CHF had PPV 42.8% (SE 1.5%) for acute CHF and sensitivity 94.3% (1.3%). Primary diagnosis of 428 and BNP >500 pg/mL had PPV 90.4% (SE 2.4%) and sensitivity 28.8% (1.1%). PPV was <10% when there was no echocardiogram, no BNP, and no primary diagnosis. 'False positive' hospitalizations were for other heart disease, lung disease, or other reasons.

CONCLUSIONS

This novel method successfully allowed flexible application and validation of queries for patients hospitalized with acute CHF.

Association of genetic risk severity with ADHD clinical characteristics

This study sought to examine the association between the cumulative risk severity conferred by the total number of attention-deficit/hyperactivity disorder (ADHD) risk alleles of the DAT1 3'UTR variable number tandem repeat (VNTR), DRD4 Exon 3 VNTR, and 5-HTTLPR with ADHD characteristics, clinical correlates, and functional outcomes in a pediatric sample. Participants were derived from case-control family studies of boys and girls diagnosed with ADHD, a genetic linkage study of families with children with ADHD, and a family genetic study of pediatric bipolar disorder. Caucasian children 18 and younger with and without ADHD and with available genetic data were included in this analysis (N = 591). The association of genetic risk severity with sociodemographic, clinical characteristics, neuropsychological, emotional, and behavioral correlates was examined in the entire sample, in the sample with ADHD, and in the sample without ADHD, respectively. Greater genetic risk severity was significantly associated with the presence of disruptive behavior disorders in the entire sample and oppositional defiant disorder in participants with ADHD. Greater genetic risk severity was also associated with the absence of anxiety disorders, specifically with the absence of agoraphobia in the context of ADHD. Additionally, one ADHD symptom was significantly associated with greater genetic risk severity. Genetic risk severity is significantly associated with ADHD clinical characteristics and co-morbid disorders, and the nature of these associations may vary on the type (externalizing vs. internalizing) of the disorder.

Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network

BACKGROUND

Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats.

OBJECTIVE

To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies.

MATERIALS AND METHODS

The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University.

RESULTS

By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results.

CONCLUSIONS

Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.

Mast cell infiltration discriminates between histopathological phenotypes of chronic obstructive pulmonary disease

RATIONALE

COPD is a complex disease with heterogeneous manifestations. Attempts have been made to define different phenotypes that could guide toward better disease understanding. We described before that smokers can develop either panlobular (PLE) or centrilobular emphysema (CLE). The latter has worse small airways remodeling and narrowing, which account for the airflow obstruction similar to asthma.

OBJECTIVES

Because of the small airways involvement in CLE similar to asthma, we hypothesized a role for mast cells in CLE but not in PLE. Hence, we investigated mast cell infiltration, along with overall inflammation, and their relation with hyperreactivity and emphysema type in COPD.

METHODS

We studied lung function, emphysema type, mast cells, and overall inflammation in small airways and alveolar walls, along with alveolar wall thickening in 67 subjects undergoing lung resection (59 smokers, 8 nonsmokers).

MEASUREMENTS AND MAIN RESULTS

Twenty-seven smokers had CLE, 24 had PLE, and 8 had no emphysema. Mast cells were significantly increased in CLE compared with PLE and control subjects. Especially relevant was the mast cell increase in airway smooth muscle in CLE, which related significantly to airway hyperreactivity. CD4(+)T cells, neutrophils, and macrophages, but not eosinophils and CD8(+)T cells, were significantly higher in CLE than PLE. Alveolar wall thickness was increased in all smokers, but significantly more in CLE.

CONCLUSIONS

The pathological phenotypes of COPD CLE and PLE show important differences in their overall inflammation with a protagonism of mast cells, which are related to airway reactivity. These findings highlight the distinctness of these COPD phenotypes and the role of mast cells in the pathophysiology of COPD.

The investigation of asthma phenotypes

PURPOSE OF REVIEW

Asthma is a heterogeneous disease constituted by overlapping separate syndromes. This review discusses recent published data relevant to asthma and severe asthma classification, resting either on the 'candidate' approach based on criteria chosen by experts or on the 'exploratory' approach based on unsupervised statistical methods.

RECENT FINDINGS

Following the 'candidate' approach, groups of experts recently reviewed the classification of severe asthma to be applicable in low-income, milddle-income, and high-income countries and provided a systematic algorithm to diagnose severe refractory asthma. The concept of asthma endotypes involving different biological mechanisms has recently being proposed. Results from the 'exploratory' approach support the clinical heterogeneity in severe asthma and the need for new approaches for the classification of asthma severity. The novel childhood and adult asthma phenotypes recently identified using the exploratory approach were supported by first evidence of validity (replication in independent studies, assessment of their discriminative properties, and effect on clinical prognosis).

SUMMARY

Research on asthma phenotypes has increased exponentially in the last year. The main focus has been on severe asthma, likely due to its clinical and socioeconomic burden. Interestingly, many of the phenotypes identified are stable across populations and methodological approaches. Further analyses on the asthma phenotypes are needed to address their stability over time and their relevance from clinical and etiological perspectives.

The future of chronic obstructive pulmonary disease treatment--difficulties of and barriers to drug development

Although chronic obstructive pulmonary disease (COPD) is a major global health problem with a rising incidence and morbidity, few pharmacotherapeutic advances have been made over the past several decades. The challenges of development of such agents are multifactorial and include rudimentary understanding of the biological genesis of human disease, inadequate in-vitro and in-vivo models, unvalidated biomarkers, inefficient physiological and clinical endpoints, and variable regulatory review worldwide. Blockade of various inflammatory pathways and mediators is a reasonable therapeutic strategy to alter the natural history of COPD. Substantial heterogeneity is evident with respect to clinical presentation, physiology, imaging, response to therapy, decline in lung function, and survival. Numerous endpoints have been proposed for clinical studies in COPD, with new approaches under study. The novel strategy that seems most promising is the use of biomarkers. We hope that with these approaches novel pharmacotherapies will be developed in the near future.

Chronic obstructive pulmonary disease phenotypes: the future of COPD

Significant heterogeneity of clinical presentation and disease progression exists within chronic obstructive pulmonary disease (COPD). Although FEV(1) inadequately describes this heterogeneity, a clear alternative has not emerged. The goal of phenotyping is to identify patient groups with unique prognostic or therapeutic characteristics, but significant variation and confusion surrounds use of the term "phenotype" in COPD. Phenotype classically refers to any observable characteristic of an organism, and up until now, multiple disease characteristics have been termed COPD phenotypes. We, however, propose the following variation on this definition: "a single or combination of disease attributes that describe differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, rate of disease progression, or death)." This more focused definition allows for classification of patients into distinct prognostic and therapeutic subgroups for both clinical and research purposes. Ideally, individuals sharing a unique phenotype would also ultimately be determined to have a similar underlying biologic or physiologic mechanism(s) to guide the development of therapy where possible. It follows that any proposed phenotype, whether defined by symptoms, radiography, physiology, or cellular or molecular fingerprint will require an iterative validation process in which "candidate" phenotypes are identified before their relevance to clinical outcome is determined. Although this schema represents an ideal construct, we acknowledge any phenotype may be etiologically heterogeneous and that any one individual may manifest multiple phenotypes. We have much yet to learn, but establishing a common language for future research will facilitate our understanding and management of the complexity implicit to this disease.

Factor structures of the neurocognitive assessments and familial analysis in first-episode schizophrenia patients, their relatives and controls

OBJECTIVES

Schizophrenia patients have a wide range of cognitive deficits. To explore the structure of these deficits and index their psychometric properties in order to define the major separable factors, exploratory factor analysis (EFA) was performed on a series of neuropsychological test results in a sample of Han Chinese first-episode schizophrenia patients, their relatives and controls without mental illness.

METHODS

The factors derived from the composite sample were tested with confirmatory factor analysis (CFA) in each of the subsamples. The heritability of each factor was estimated by using the mixed linear model in order to determine whether the common factor scores describe the familiarity of the data.

RESULTS

A six-factor model of general mental activity, sort and shift, attention and anti-interference, logical memory, reasoning and problem-solving/executive function, and visual reproduction were extracted and confirmed on EFA and CFA. There was a hierarchy in cognitive performance deficits in relatives depending on their relatedness to probands. Patients performed more poorly than siblings/offspring in general mental activity, sort and shift, attention and anti-interference, logical memory, planning and problem-solving, but not in visual reproduction. The logical memory domain was found to be significantly heritable (h(2)=0.34), and reasoning and problem-solving had a marginal heritability of around 0.19.

CONCLUSIONS

Generalized cognitive deficit accounts for the majority of the overall variance of cognitive deficits in schizophrenia. Estimates of heritability were modest. The present results also support the hypothesis that neurocognitive deficits of schizophrenia are familial and could serve as endophenotypes, especially logical memory, which is the most heritable.

Otitis media: a genome-wide linkage scan with evidence of susceptibility loci within the 17q12 and 10q22.3 regions

Background

Otitis media (OM) is a common worldwide pediatric health care problem that is known to be influenced by genetics. The objective of our study was to use linkage analysis to map possible OM susceptibility genes.

Methods

Using a stringent diagnostic model in which only those who underwent tympanostomy tube insertion at least once for recurrent/persistent OM are considered affected, we have carried out a genome-wide linkage scan using the 10K Affymetrix SNP panel. We genotyped 403 Caucasian families containing 1,431 genotyped individuals and 377 genotyped affected sib pairs, and 26 African American families containing 75 genotyped individuals and 27 genotyped affected sib pairs. After careful quality control, non-parametric linkage analysis was carried out using 8,802 SNPs.

Results

In the Caucasian-only data set, our most significant linkage peak is on chromosome 17q12 at rs226088 with a p-value of 0.00007. Other peaks of potential interest are on 10q22.3 (0.00181 at rs1878001), 7q33 (0.00105 at rs958408), 6p25.1 (0.00261 at rs554653), and 4p15.2 (0.00301 at rs2133507). In the combined Caucasian and African American dataset, the 10q22.3 peak becomes more significant, with a minimal p-value of 0.00026 at rs719871. Family-based association testing reveals signals near previously implicated genes: 513 kb from SFTPA2 (10q22.3), 48 kb from IFNG (12q14), and 870 kb from TNF (6p21.3).

Conclusion

Our scan does not provide evidence for linkage in the previously reported regions of 10q26.3 and 19q13.43. Our best-supported linkage regions may contain susceptibility genes that influence the risk for recurrent/persistent OM. Plausible candidates in 17q12 include AP2B1, CCL5, and a cluster of other CCL genes, and in 10q22.3, SFTPA2.

The relationship between serotonin receptor 1B polymorphisms A-161T and alcohol dependence

BACKGROUND

Several studies have suggested that the serotonin receptor 1B gene (5HT1B) may be important in the pathogenesis of alcohol dependence (alcoholism; ALC; AD). We examined whether 5HT1B gene A-161T polymorphisms (rs130058) are a susceptibility factor for total AD and subgroups of AD. We further explored correlation of this 5HT1B gene variant between anxiety-depression alcoholism (ANX/DEP ALC) and antisocial alcoholism (antisocial ALC) subgroups because of the high comorbidity of anxiety-depression, antisocial personality disorder, and AD.

METHODS

We recruited 522 Han Chinese in Taiwan for this study: 322 AD patients and 200 controls. The patient group was recruited primarily from medical teaching hospitals; patients with antisocial alcoholism were recruited from Taiwanese prisons. Individuals with AD were classified into 3 homogeneous clinical subgroups -- pure alcoholism (pure ALC), ANX/DEP ALC, and antisocial ALC -- using DSM-IV diagnosis. The 5HT1B gene A-161T polymorphism was determined using PCR-RFLP.

RESULTS

No significant differences in genotypic and allelic frequencies were found between controls and the total AD group or between controls and the 3 AD subgroups. However, there were significant differences in the 5HT1B gene A-161T polymorphism at both the genotype and allelic levels between the ANX/DEP ALC and antisocial ALC subgroups.

CONCLUSIONS

This study suggests that the 5HT1B gene A-161T polymorphism alone is not a risk factor for increasing susceptibility to either AD or its subtypes. However, 5HT1B gene A-161T polymorphisms might be one of the common genetic factors between the ANX/DEP ALC and antisocial ALC subgroups.

A testable prognostic model of nicotine dependence

Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.

The association between DRD2/ANKK1, 5-HTTLPR gene, and specific personality trait on antisocial alcoholism among Han Chinese in Taiwan

Cloninger suggested that type II alcoholism was associated with higher novelty seeking and less harm avoidance behaviors, which was similar to antisocial alcoholism. Most previous studies have failed to recruit subjects that have antisocial personality disorder without alcoholism due to the high coexisting likelihood of having antisocial personality disorder with alcoholism in the majority of the examined populations. Thus, recruitment of individuals with antisocial non-alcoholism (antisocial personality disorder) served as an important control group in examining Cloninger's hypothesis. Due to the documented protective effects against alcoholism of ALDH2*1/*2 or *2/*2 genotype among the Han Chinese population, we recruited antisocial non-alcoholics from the Han Chinese population in Taiwan to verify Cloninger's hypotheses. A total of 127 Han Chinese subjects were recruited who met the diagnosis of antisocial alcoholism (n = 43) or antisocial non-alcoholism (n = 84). We found that the antisocial alcoholism group scored higher on the novelty seeking behavior than did the antisocial non-alcoholism group (t = 2.61, P = 0.01), but no difference was observed on the harm avoidance dimension between these two groups (t = 0.15, P = 0.88). In the novelty seeking scores, after stratification of DRD2 TaqI A genotypes, only a significant difference in 5-HTTLPR polymorphisms between antisocial alcoholics and antisocial non-alcoholics was found, indicating an interaction between DRD2 TaqI A1+ (include A1/A1 or A1/A2) and 5-HTTLPR S/S genotype (t = 2.75, P = 0.01) However, no significant difference was found in the harm avoidance personality trait between these two groups of Han Chinese in Taiwan.

A genome-wide linkage study in families with major depression and co-morbid unexplained swelling

Major depressive disorder (MDD) is a common heritable condition. The diversity of the phenotype coupled with aetiological and genetic heterogeneity present formidable obstacles in the search for causative genetic loci. Studies of large families with many affected individuals, and the selection of well-defined clinical subgroups of depression, are two ways to reduce this complexity. Unexplained swelling symptoms (USS) are common in women and many patients give a strong personal and family history of depression. Co-morbid depression and swelling symptoms define a useful sub-phenotype for investigating genetic factors in depression. We have completed a genome-wide linkage analysis using 371 microsatellite markers in four families where MDD is co-morbid with USS. Of 47 affected individuals, 28 had both MDD and unexplained swelling, 11 had symptoms of swelling alone, and 8 had MDD alone. Parametric marker-specific analysis identified one suggestive locus, D8S260 (LOD = 2.02) and non-parametric multipoint variance component analysis identified a region on 7p (LOD = 2.10). A 47 cM suggestive linkage region on chromosome 14q (identified by both parametric and non-parametric methods) was identified and investigated further with fine-mapping markers but the evidence for linkage to this region decreased with increased marker information content.

Do the same genetic and environmental effects underlie the covariation of alcohol dependence, smoking, and aggressive behaviour?

We investigated genetic and environmental effects on alcohol dependence, smoking, and aggressive behaviour, as well as sex differences in these effects and whether these effects were the same for the three phenotypes. There were 3141 male (mean age 26.2 [S.E.=0.1]) and 6026 female (mean age 26.1 [S.E.=0.1]) participants in this extended twin study. Alcohol Use Disorder Identification Test (AUDIT), number of cigarettes smoked, and two subscales of Aggression Questionnaire (AQ) were used to assess the phenotypes. All three were positively interrelated with males exhibiting higher levels on each phenotype. Multivariate model fitting identified genetic effects on all phenotypes for both men and women ranging between 69% (aggressive behaviour in women) and 78% (alcohol dependence in men). The effects were stronger in men for alcohol dependence and smoking. There were no qualitative genetic sex differences. Genetic correlations between all phenotypes were substantial.

Genetics of the Framingham Heart Study population

This chapter provides an introduction to the Framingham Heart Study and the genetic research related to cardiovascular diseases conducted in this unique population. It briefly describes the origins of the study, the risk factors that contribute to heart disease, and the approaches taken to discover the genetic basis of some of these risk factors. The genetic architecture of several biological risk factors has been explained using family studies, segregation analysis, heritability, and phenotypic and genetic correlations. Many quantitative trait loci underlying cardiovascular diseases have been discovered using different molecular markers. Additionally, initial results from genome-wide association studies using 116,000 markers and the prospects of using 550,000 markers for association studies are presented. Finally, the use of this unique sample to study genotype and environment interactions is described.

Definition of phenotype

Definition of the phenotype is crucial in designing any genetic study, especially an association study, intended to detect the disease predisposing genes. In this chapter, we review the different types of phenotypes such as discrete or continuous and discuss the issues impacting on the phenotype definition related to study design, specifically, the impact of diagnostic error (misclassification) in case-control studies and measurement error in continuous traits. We show that the power of a study depends heavily on the phenotype measured and that misclassification or measurement error can dramatically reduce the power. We also suggest some possible responses to these challenges.

The heritability of abstract reasoning in Caribbean Latinos with familial Alzheimer disease

BACKGROUND

Alzheimer disease (AD) is under substantial genetic influence. To better understand the genetic influence on component phenotypes of AD, we estimated the heritability (h(2)) of abstract reasoning and examined its relation with apolipoprotein epsilon 4 (APOE-epsilon 4).

METHODS

We studied abstract reasoning in 1,116 individuals from 210 Caribbean Hispanic families with late onset AD, using the similarities subtest scores from the Wechsler Adult Intelligence Scale. We computed h(2), then performed analysis of variance to examine the effect of APOE-epsilon 4.

RESULTS

Abstract reasoning was highly heritable (h(2)(unadjusted) = 79.9%). After adjusting for covariates, the h(2) was reduced to 32.6%, with education accounting for 40.8% of the variance. The APOE-epsilon 4 allele had no effect.

CONCLUSION

Abstract reasoning was strongly influenced by genetic factors and education. Genes other than APOE contribute to the inheritance of abstract reasoning ability.

Specific personality traits and dopamine, serotonin genes in anxiety-depressive alcoholism among Han Chinese in Taiwan

BACKGROUND

Cloninger [Cloninger CR. 1987. Neurogenetic adaptive mechanisms in alcoholism. Science 236: 410-416.] had proposed a psychobiological model suggesting that three main personality dimensions distinguish the alcoholism into two subtypes (type I and type II). However, the classification was equivocal for clinical diagnosis. Recently, anxiety-depressive alcohol dependence (ANX/DEP ALC) has been posited as a genetically specific subtype of alcoholism. Its clinical characteristics were similar to individuals with type I alcoholism [Cloninger, C.R. 1987. Neurogenetic adaptive mechanisms in alcoholism. Science 236: 410-6.] such as having a high comorbidity with mood disorder, late-onset and more anxious/depressed traits. We attempted to investigate whether the dopamine D2 receptor (DRD2) and the serotonin transporter promoter region (5-HTTLPR) genes were involved in Novelty Seeking (NS) and Harm Avoidance (HA) of ANX/DEP ALC.

METHODS

We recruited 46 pure alcohol dependents (Pure ALC) and 87 anxiety-depression alcohol dependents (ANX/DEP ALC). All participants were diagnosed by DSM-IV criteria, genotyped by the PCR method and assessed with Tridimensional Personality Questionnaire (TPQ).

RESULTS

Both NS and HA were high in ANX/DEP ALC (p = 0.021; p = 0.001, respectively). The association between NS and ANX/DEP ALC only existed in subjects with DRD2 TaqI A1(+) allele (A1/A1 or A1/A2 genotypes) (p = 0.004) and in those with S/S genotype of 5-HTTLPR (p = 0.005). With the stratification of DRD2 TaqI A1(+) allele, high NS of ANX/DEP ALC existed only in carriers of 5-HTTLPR S/S genotype (p=0.001). Moreover, ANX/DEP ALC was related to high HA only in samples carrying 5-HTTLPR S/L or L/L genotype (p = 0.02).

CONCLUSIONS

These findings provided the empirical genetic characterization of the specific personality traits in ANX/DEP ALC among Han Chinese population in Taiwan.

Genetics of nicotine dependence and pharmacotherapy

Nicotine dependence is substantially heritable. Several regions across the genome have been implicated in containing genes that confer liability to nicotine dependence and variation in individual genes has been associated with nicotine dependence. Smoking cessation measures are also heritable, and measured genetic variation is associated with nicotine dependence treatment efficacy. Despite significant strides in the understanding of the relative contribution of genetic and environmental factors to nicotine dependence and treatment, emergent challenges necessitate interdisciplinary coordinated effort for effective problem solving. These challenges include refinement of the nicotine dependence phenotype, better understanding of the dynamic interplay between genes and environment in nicotine dependence etiology, application and development of molecular and statistical methodology that can adequately address vast amounts of data, and continuous translational cross-talk.

Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis

For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.

Informative phenotypes for genetic studies of psychiatric disorders

Despite its initial promise, there has been both progress and some set backs in genetic studies of the major psychiatric disorders of childhood and adulthood. Finding true susceptibility genes may be delayed because the most genetically informative phenotypes are not being used on a regular basis in linkage analysis and association studies. It is highly likely that using alternative phenotypes instead of DSM diagnostic categories will lead more rapid success in the search for these susceptibility genes. The objective of this paper is to describe the different types of informative phenotypes that can be employed in psychiatric genetic studies, to clarify their uses, to identify several methodologic issues the design and conduct of linkage and association studies that use alternative phenotypes and finally to suggest possible solutions to those difficulties. This is a conceptual review with a focus on methodological issues that may arise in psychiatric genetics and examples are taken from the literature on autism, schizophrenia, bipolar disorder, and alcoholism.

Linkage and association analysis of candidate genes for TB and TNFalpha cytokine expression: evidence for association with IFNGR1, IL-10, and TNF receptor 1 genes

Tuberculosis (TB) is a growing public health threat globally and several studies suggest a role of host genetic susceptibility in increased TB risk. As part of a household contact study in Kampala, Uganda, we have taken a unique approach to the study of genetic susceptibility to TB by developing an intermediate phenotype model for TB susceptibility, analyzing levels of tumor necrosis factor-alpha (TNFalpha) in response to culture filtrate as the phenotype. In the present study, we analyzed candidate genes related to TNFalpha regulation and found that interleukin (IL)-10, interferon-gamma receptor 1 (IFNGR1), and TNFalpha receptor 1 (TNFR1) genes were linked and associated to both TB and TNFalpha. We also show that these associations are with progression to active disease and not susceptibility to latent infection. This is the first report of an association between TB and TNFR1 in a human population and our findings for IL-10 and IFNGR1 replicate previous findings. By observing pleiotropic effects on both phenotypes, we show construct validity of our intermediate phenotype model, which enables the characterization of the role of these genetic polymorphisms on TB pathogenesis. This study further illustrates the utility of such a model for disentangling complex traits.

Neurocognitive deficits in first-episode schizophrenic patients and their first-degree relatives

Some neuropsychological abilities, particularly those affecting memory, attention and executive function, are impaired amongst both schizophrenic patients and their unaffected relatives, implying that these deficits are at least partly genetic in origin. However neuropsychological performance can be altered by medication, and has rarely been examined in first onset, drug naive patients. The objective of this study was to determine whether selected neurocognitive abilities are impaired in first-onset schizophrenic patients and their relatives compared to controls. We examined attention and speed of information processing, memory and learning, verbal function, visuoconstructive abilities and executive function in 207 first-episode schizophrenic patients (163 of whom were drug naïve), 322 of their first-degree relatives and 133 unrelated normal controls. The data were subjected to multilevel modeling to compare neurocognitive performance between schizophrenic probands, relatives and controls while taking into account potential correlations among members of the same family; age, gender, and years of education were included as covariates. Of the three groups, schizophrenic patients performed poorest at all neuropsychological tests, suggestive of a broad range of neurocognitive deficits. Their first-degree relatives showed a narrower pattern of poor performance at Digit Symbol, Digit Span, Trail Making, Verbal Fluency test, Tower of Hanoi, and WCST-M tests. Our findings show that selected neurocognitive deficits especially attention and executive function are impaired in the families of schizophrenic patients. These patterns of neurocognitive deficits may represent "endophenotypes" denoting varying degrees of vulnerability to schizophrenia and may be of value in future molecular genetic studies.

Relevance of Genetics and Genomics for Prevention and Treatment of Cardiovascular Disease

Atherosclerotic cardiovascular disease (CVD) is a major health problem in the United States and around the world. Evidence accumulated over decades convincingly demonstrates that family history in a parent or a sibling is associated with atherosclerotic CVD, manifested as coronary heart disease, stroke, and/or peripheral arterial disease. Although there are several mendelian disorders that contribute to CVD, most common forms of CVD are believed to be multifactorial and to result from many genes, each with a relatively small effect working alone or in combination with modifier genes and/or environmental factors. The identification and the characterization of these genes and their modifiers would enhance prediction of CVD risk and improve prevention, treatment, and quality of care. This scientific statement describes the approaches researchers are using to advance understanding of the genetic basis of CVD and details the current state of knowledge regarding the genetics of myocardial infarction, atherosclerotic CVD, hypercholesterolemia, and hypertension. Current areas of interest and investigation--including gene-environment interaction, pharmacogenetics, and genetic counseling--are also discussed. The statement concludes with a list of specific recommendations intended to help incorporate usable knowledge into current clinical and public health practice, foster and guide future research, and prepare both researchers and practitioners for the changes likely to occur as molecular genetics moves from the laboratory to clinic.

Heritability of indices for cardiac contractility in ambulatory recordings

INTRODUCTION

Overactivity of the sympathetic nervous system (SNS) plays a pivotal role in the development of cardiovascular disease. This involvement suggests that the genetic susceptibility to adverse cardiovascular events may derive in part from individual differences in SNS activity.

METHODS AND RESULTS

To establish a genetic contribution to SNS activity, we measured sympathetic effects on cardiac contractility in 755 healthy adult twins and their singleton siblings. The preejection period (PEP) and the ratio of PEP to the left ventricular ejection time (PEP/LVET ratio) were derived from ambulatory recordings of the ECG and thorax impedance. During this type of prolonged recordings in a real life setting, the extent of cardiac sympathetic activity will vary with the demands of daily activities. Therefore, the genetic architecture of both indices was examined separately across three daytime periods (morning, afternoon, evening), and during nighttime sleep. Results showed significant genetic contribution to PEP (48-62%) over all daily periods. Heritability estimates for PEP/LVET ratio ranged between 35% and 58%. Cardiac sympathetic activity during the waking and sleep periods was largely influenced by genetic factors that were common to the entire 24-hour period. During sleep, additional genetic influences emerged that accounted for 8% of the variance in PEP.

CONCLUSION

Impedance-derived measures of sympathetic effects on cardiac contractility show substantial heritability across all periods of the day and during sleep.

Evidence for a Major Gene Influence on Tumor Necrosis Factor-α Expression in Tuberculosis: Path and Segregation Analysis

OBJECTIVE

Tuberculosis (TB) is a growing global public health problem. Several studies suggest a role for host genetics in disease susceptibility, but studies to date have been inconsistent and a comprehensive genetic model has not emerged. A limitation of previous genetic studies is that they only analyzed the binary trait TB, which does not reflect disease heterogeneity. Furthermore, these studies have not accounted for the influence of shared environment within households on TB risk, which may spuriously inflate estimates of heritability.

METHODS

We conducted a household contact study in a TB-endemic community in Uganda. Antigen-induced tumor necrosis factor-alpha (TNFalpha) expression, a key component of the underlying immune response to TB, was used as an endophenotype for TB.

RESULTS

Path analysis, conducted to assess the effect of shared environment, suggested that TNFalpha is heritable (narrow sense heritability = 34-66%); the effect of shared environment is minimal (1-14%), but gene-environment interaction may be involved. Segregation analysis of TNFalpha suggested a major gene model that explained one-third of the phenotypic variance, and provided putative evidence of natural selection acting on this phenotype.

CONCLUSION

Our data further support TNFalpha as an endophenotype for TB, as it may increase power to detect disease-predisposing loci.

Replication in genetic studies of complex traits

Disappointments in replicating initial findings in gene mapping for complex traits are often attributed to small sample sizes and inadequate techniques to determine the threshold value. This is clearly not the whole truth. More fundamental reasons lie in the inherent heterogeneity related to disease, including genetic heterogeneity, differences in allele frequencies, and context-dependency in genetic architecture. There are also other reasons related to the data collection and analysis. Replication may remain a source of frustration unless more emphasis is put on controlling these sources of heterogeneity between studies.

Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies

BACKGROUND

Phenotype error causes reduction in power to detect genetic association. We present a quantification of phenotype error, also known as diagnostic error, on power and sample size calculations for case-control genetic association studies between a marker locus and a disease phenotype. We consider the classic Pearson chi-square test for independence as our test of genetic association. To determine asymptotic power analytically, we compute the distribution's non-centrality parameter, which is a function of the case and control sample sizes, genotype frequencies, disease prevalence, and phenotype misclassification probabilities. We derive the non-centrality parameter in the presence of phenotype errors and equivalent formulas for misclassification cost (the percentage increase in minimum sample size needed to maintain constant asymptotic power at a fixed significance level for each percentage increase in a given misclassification parameter). We use a linear Taylor Series approximation for the cost of phenotype misclassification to determine lower bounds for the relative costs of misclassifying a true affected (respectively, unaffected) as a control (respectively, case). Power is verified by computer simulation.

RESULTS

Our major findings are that: (i) the median absolute difference between analytic power with our method and simulation power was 0.001 and the absolute difference was no larger than 0.011; (ii) as the disease prevalence approaches 0, the cost of misclassifying a unaffected as a case becomes infinitely large while the cost of misclassifying an affected as a control approaches 0.

CONCLUSION

Our work enables researchers to specifically quantify power loss and minimum sample size requirements in the presence of phenotype errors, thereby allowing for more realistic study design. For most diseases of current interest, verifying that cases are correctly classified is of paramount importance.

Defining the Phenotype in Human Genetic Studies: Forward Genetics and Reverse Phenotyping

The definition of phenotypes for genetic study is a challenging endeavor. Just as we apply strict quality standards to genotype data, we should expect that phenotypes meet consistently high standards of reproducibility and validity. The methods for achieving accurate phenotype assignment in the research setting--the 'research diagnosis'--are different from the methods used in clinical diagnosis in the patient care setting. We evaluate some of the main challenges of phenotype definition in human genetics, and begin to outline a set of standards to which phenotypes used in genetics studies may aspire with the goal of increasing the quality and reproducibility of linkage and association studies. Revisiting the traditional phenotype definitions through a focus on familial components and heritable endophenotypes is a time-honored approach. Reverse phenotyping, where phenotypes are refined based on genetic marker data, may be a promising new approach. The stakes are high, since the success of gene mapping in genetically complex disorders hinges on the ability to delineate the target phenotype with accuracy and precision.

Distinct Heritable Patterns of Angiographic Coronary Artery Disease in Families With Myocardial Infarction

BACKGROUND

Coronary artery disease (CAD) and myocardial infarction (MI) are significantly determined by genetic background. Whether distinct angiographic features of CAD are affected by inherited factors has never been investigated. Thus, we analyzed comprehensively the extent to which various aspects of CAD, including disease severity, distribution of lesions, presence of coronary calcification, morphology of stenoses, and anatomic characteristics, are under genetic control.

METHODS AND RESULTS

We retrospectively studied the coronary angiograms of 882 siblings with CAD from 401 families. These families were ascertained through index patients defined by MI before the age of 60 years and at least 1 sibling with MI or coronary revascularization procedures. Heritability calculations were performed with variance-component analysis. Additionally, recurrence risks to siblings were analyzed. Traditional cardiovascular risk factors and age at the first coronary event displayed significant heritable components. After adjustment for age and sex, significant heritabilities were identified for proximal stenoses, in particular, left main CAD (h2=0.49+/-0.12; P=0.01), coronary calcification (h2=0.51+/-0.17; P=0.001), and ectatic coronary lesions (h2=0.52+/-0.07; P=0.001). In contrast, no heritability was found for distal disease (h2=0.05+/-0.19; NS), the pattern of coronary arterial blood supply, or the number of diseased vessels. Calculation of recurrence risks in siblings largely confirmed the heritability estimates.

CONCLUSIONS

Distinct morphological characteristics associated with CAD show different degrees of heritability. Notably, the most hazardous localizations, like left main or proximal disease, display a high heritability. In contrast, some features of coronary morphology, such as distal disease, do not appear to be markedly influenced by heritable factors.

Pleiotropic effects of a chromosome 3 locus on speech-sound disorder and reading

Speech-sound disorder (SSD) is a complex behavioral disorder characterized by speech-sound production errors associated with deficits in articulation, phonological processes, and cognitive linguistic processes. SSD is prevalent in childhood and is comorbid with disorders of language, spelling, and reading disability, or dyslexia. Previous research suggests that developmental problems in domains associated with speech and language acquisition place a child at risk for dyslexia. Recent genetic studies have identified several candidate regions for dyslexia, including one on chromosome 3 segregating in a large Finnish pedigree. To explore common genetic influences on SSD and reading, we examined linkage for several quantitative traits to markers in the pericentrometric region of chromosome 3 in 77 families ascertained through a child with SSD. The quantitative scores measured several processes underlying speech-sound production, including phonological memory, phonological representation, articulation, receptive and expressive vocabulary, and reading decoding and comprehension skills. Model-free linkage analysis was followed by identification of sib pairs with linkage and construction of core shared haplotypes. In our multipoint analyses, measures of phonological memory demonstrated the strongest linkage (marker D3S2465, P=5.6 x 10(-5), and marker D3S3716, P=6.8 x 10(-4)). Tests for single-word decoding also demonstrated linkage (real word reading: marker D3S2465, P=.004; nonsense word reading: marker D3S1595, P=.005). The minimum shared haplotype in sib pairs with similar trait values spans 4.9 cM and is bounded by markers D3S3049 and D3S3045. Our results suggest that domains common to SSD and dyslexia are pleiotropically influenced by a putative quantitative trait locus on chromosome 3.

Genetics and drug use as a complex phenotype

Drug use is a complex behavior influenced by multiple biological, family, and sociocultural factors. The concurrent use/misuse of multiple drugs is often seen and drug use also co-occurs with other psychiatric conditions. Behavior and molecular genetic studies support an important posited role of genes in drug use. This posited genetic risk does not appear to be conferred by one or two major genes manifesting large effects, but rather by a number of genes manifesting smaller effects. Genetic factors explain, on average, only about half of the total variability in drug use, with the remaining variability influenced by environmental factors. Also, genetic risk may be differentially expressed in the presence vs. absence of particular environmental conditions. Thus, investigation of environmental factors and their interaction with genetic risk is a necessary component of genetic research. While the full potential of genetic investigations for the prevention of drug misuse has yet to be realized, an example of the impact of risk factor modification under various conditions of gene-environment interaction is provided, and the implications for use of genetic information in drug-misuse prevention are discussed. The multifactorial nature of drug use necessitates coordinated investigation from multiple disciplines and timely dissemination of scientific findings. In addition, this work demands adherence to the highest standards of confidentiality and ethical use of genetic information to best inform future prevention efforts.

Power of maximum HLOD tests to detect linkage to obesity genes

Background

We investigate the power of heterogeneity LOD test to detect linkage when a trait is determined by several major genes using Genetic Analysis Workshop 13 simulated data. We consider three traits, two of which are disease-causing traits: 1) the rate of change in body mass index (BMI); and 2) the maximum BMI; and 3) the disease itself (hypertension). Of interest is the power of "HLOD2", the maximum heterogeneity LOD obtained upon maximizing over the two genetic models.

Results

Using a trait phenotype Obesity Slope, we observe that the power to detect the two markers closest to the two genes (S1, S2) at the 0.05 level using HLOD2 is 13% and 10%. The power of HLOD2 for Max BMI phenotype is 12% and 9%. The corresponding values for the Hypertension phenotype are 8% and 6%.

Conclusion

The power to detect linkage to the slope genes is quite low. But the power using disease-related traits as a phenotype is greater than the power using the disease (hypertension) phenotype.

Heritability analysis of cytokines as intermediate phenotypes of tuberculosis

Numerous studies have provided support for genetic susceptibility to tuberculosis (TB); however, heterogeneity in disease expression has hampered previous genetic studies. The purpose of this work was to investigate possible intermediate phenotypes for TB. A set of cytokine profiles, including antigen-stimulated whole-blood assays for interferon (IFN)-gamma, tumor necrosis factor (TNF)-alpha, transforming growth factor (TGF)-beta, and the ratio of IFN to TNF, were analyzed in 177 pedigrees from a community in Uganda with a high prevalence of TB. The heritability of these variables was estimated after adjustment for covariates, and TNF-alpha, in particular, had an estimated heritability of 68%. A principal component analysis of IFN-gamma, TNF-alpha, and TGF-beta reflected the immunologic model of TB. In this analysis, the first component explained >38% of the variation in the data. This analysis illustrates the value of such intermediate phenotypes in mapping susceptibility loci for TB and demonstrates that this area deserves further research.

Genetics of obesity and type 2 diabetes: tracking pathogenic traits during the predisease period

The modern generalization of sedentary life and caloric abundance has created new physiological conditions capable of changing the level of expression of a number of genes involved in fuel metabolism and body weight regulation. It is likely that the genetic variants or alleles of these genes have in the past participated in the adaptation of human physiology to its evolutionary constraints. The nature and prevalence of polymorphisms responsible for the quantitative variation of complex metabolic traits may have been different among human populations, depending on their environment and ancestral genetic background. These polymorphisms could likely explain differences in disease susceptibility and prevalence among groups of humans. From complex traits to potentially complex alleles, understanding the molecular genetic basis underlying quantitative variation will continue to be a growing concern among geneticists dealing with obesity and type 2 diabetes, the main fuel disorders of the modern era. Genomics and genetic epidemiology now allow high-level linkage and association studies to be designed. But the pooling of large trans-geographic cohorts may in fact increase the genetic heterogeneity of studied traits and dilute genotype-phenotype associations. In this article, we underscore the importance of selecting the traits to be subjected to quantitative genetic analysis. Although this is not possible for most other multifactorial diseases, obesity and type 2 diabetes can be subjected to a pregenetic dissection of complexity into simpler quantitative traits (QTs). This dissection is based on the pathogenic mechanisms, and the time course of the traits, and the individuals' age, within the predisease period rather than on descriptive parameters after disease diagnosis. We defend that this approach of phenotypes may ease future associations to be established between QTs of intermediate complexity and genetic polymorphisms.

O futuro da epidemiologia genética de características complexas

A epidemiologia genética evoluiu de um enfoque em estudos sobre doenças mendelianas raras para a análise genética de características complexas. Com o advento de informações sobre a completa seqüência de genes ao longo do genoma humano e de outros organismos, o interesse da epidemiologia genética em desvendar a natureza dos fatores que influenciam essas características se tornou primordial. São apresentados os principais métodos empregados no estudo de doenças complexas bem como suas principais vantagens e desvantagens. Discute-se a importância na determinação da amostra e o uso de fenótipos e marcadores genéticos apropriados. Como exemplo das estratégias citadas tomamos o estudo de índice de massa corporal (BMI) para ilustrar um fator genético principal localizado no cromossomo 7. Em uma discussão sobre tendências no estudo de ligação, embora reconhecendo que famílias e genealogias continuarão sendo o foco principal das amostras, discute-se alguns novos e eficientes tipos de amostragem (como por exemplo, controles não-relacionados) em que amostras de conjunto de DNA serão universalmente empregadas. O reconhecimento da heterogeneidade genética entre estudos e sua interpretação será uma das mais importantes características no futuro das análises de características complexas.