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1.  Genetic markers of osteoarticular disorders: facts and hopes 
Arthritis Research  2001;3(5):270-280.
Osteoarthritis and osteoporosis are the two most common age-related chronic disorders of articular joints and skeleton, representing a major public health problem in most developed countries. Apart from being influenced by environmental factors, both disorders have a strong genetic component, and there is now considerable evidence from large population studies that these two disorders are inversely related. Thus, an accurate analysis of the genetic component of one of these two multifactorial diseases may provide data of interest for the other. However, the existence of confounding factors must always be borne in mind in interpreting the genetic analysis. In addition, each patient must be given an accurate clinical evaluation, including family history, history of drug treatments, lifestyle, and environment, in order to reduce the background bias. Here, we review the impact of recent work in molecular genetics suggesting that powerful molecular biology techniques will soon make possible both a rapid accumulation of data on the genetics of both disorders and the development of novel diagnostic, prognostic, and therapeutic approaches.
PMCID: PMC128904  PMID: 11549368
candidate genes; genetics; multifactorial diseases; osteoporosis; osteoarthritis
2.  Juvenile Myelomonocytic Leukemia: A Report from the 2nd International JMML Symposium 
Leukemia research  2008;33(3):355-362.
Juvenile myelomonocytic leukemia (JMML) is an aggressive childhood myeloproliferative disorder characterized by the overproduction of myelomonocytic cells. JMML incidence approaches 1.2/million persons in the United States (Cancer Incidence and Survival Among Children and Adolescents: United States SEER Program 1975–1995). Although rare, JMML is innately informative as the molecular genetics of this disease implicates hyperactive Ras as an essential initiating event. Given that Ras is one of the most frequently mutated oncogenes in human cancer, findings from this disease are applicable to more genetically diverse and complex adult leukemias. The JMML Foundation ( was founded by parent advocates dedicated to finding a cure for this disease. They work to bring investigators together in a collaborative manner. This article summarizes key presentations from the 2nd International JMML Symposium, on December 7–8, 2007 in Atlanta, GA. A list of all participants is in Supplementary Table.
PMCID: PMC2692866  PMID: 18954903
Juvenile myelomonocytic leukemia (JMML); RAS; PTPN11; NF1; Therapy; Diagnosis
3.  Pharmacogenetics: implementing personalized medicine 
Pharmacogenetics and pharmacogenomics have been widely recognized as fundamental steps toward personalized medicine. They deal with genetically determined variants in how individuals respond to drugs, and hold the promise to revolutionize drug therapy by tailoring it according to individual genotypes.
The clinical need for novel approaches to improve drug therapy derives from the high rate of adverse reactions to drugs and their lack of efficacy in many individuals that may be predicted by pharmacogenetic testing.
Significant advances in pharmacogenetic research have been made since inherited differences in response to drugs such as isoniazid and succinylcholine were explored in the 1950s. The clinical utility and applications of pharmacogenetics and pharmacogenomics are at present particularly evident in some therapeutic areas (anticancer, psycotrophic, and anticoagulant drugs).
Recent evidence derived from several studies includes screening for thiopurine methyl transferase or uridine 5'-diphosphoglucuronosyl-transferase 1A1 gene polymorphisms to prevent mercaptopurine and azathioprine or irinotecan induced myelosuppression, respectively. Also there is a large body of information concerning cytochrome P450 gene polymorphisms and their relationship to drug toxicity and response. Further examples include screening the presence of the HLA-B*5701 allele to prevent the hypersensitivity reactions to abacavir and the assessment of the human epidermal growth factor receptor (HER-2) expression for trastuzumab therapy of breast cancer or that of KRAS mutation status for cetuximab or panitumumab therapy in colorectal cancer.
Moreover, the application of pharmacogenetics and pharmacogenomics to therapies used in the treatment of osteoarticular diseases (e.g. rheumatoid arthritis, osteoporosis) holds great promise for tailoring therapy with clinically relevant drugs (e.g. disease-modifying antirheumatic drugs, vitamin D, and estrogens).
Although the classical candidate gene approach has helped unravel genetic variants that influence clinical drug responsiveness, gene-wide association studies have recently gained attention as they enable to associate specific genetic variants or quantitative differences in gene expression with drug response.
Although research findings are accumulating, most of the potential of pharmacogenetics and pharmacogenomics remains to be explored and must be validated in prospective randomized clinical trials.
The genetic and molecular foundations of personalized medicine appear solid and evidence indicates its growing importance in healthcare.
PMCID: PMC2781211  PMID: 22461093
pharmacogenetics, drug effects, drug metabolism, drug therapy, antineoplastic agents.
4.  P25 - Growing Strong and Healthy with Mister Bone: An Educational Programme to Ensure Strong Bones Later in Life 
Bone mass increases steadily until the age of 20–30 years and most bone mass is acquired during the first two decades of life. Nutrition plays a critical role in the achievement of one’s optimal genetically programmed peak bone mass (PBM), reducing the risk of osteoporosis later in life. PBM is the amount of bony tissue present in the skeleton at the end of skeletal maturation. Even though 90% of PBM is acquired by the end of second decade of life, skeletal mass continues to increase for up to 10–15 years after that, through the process of bone consolidation, with maximal PBM occurring at around 30 years of age. As a 10% increase in PBM corresponds to a gain of one standard deviation in bone mineral density in adulthood, osteoporotic fracture risk may be reduced by up to 50% by interventions aimed at maximising PBM in a sustainable manner in childhood and adolescence. Although genetic factors are the strongest predictors of bone mass, accounting for 50–80% of its variance, nutritional and lifestyle factors can explain an additional 20–30% of bone mass variance.
Bone is living tissue like any other, and its cells have the same kinds of nutrient needs as those of the rest of the body; it does not require only an energy supply, but also protein and micronutrients, calcium and vitamin D in primis. In a balanced western-style diet, about 60% of dietary calcium should come from milk and dairy products, 20% from fresh vegetables and dried fruits, and the rest from drinking water or other discrete sources.
Current research indicates that calcium intake in school-age children is below the recommended adequate level.The recommended adequate intake of calcium for children between the ages of 9 and 11 years is about 1100–1200 mg.
In response to this critical health issue it is essential to monitor children’s intake of dairy products and nutrients important for bone health, such as calcium and vitamin D, in order to ensure that their nutritional needs are met and that they are receiving the nutritional intakes needed to safeguard their health later in life. The aim of our study was to monitor and promote the intake of dairy products, calcium and vitamin D in children, in order to help them achieve their optimal PBM and to safeguard their bone health later in life. Modifications in schoolchildren’s nutritional behaviour were evaluated through a nutritional programme designed to increase calcium intake. The project was conducted with the support of novel instruments specifically created for this educational programme.
Our study sample comprised 180 children (48% males and 52% females) aged 9–11 years from a primary school in Florence. We evaluated the children’s eating habits through a questionnaire designed to assess intake of calcium, dairy products, and total caloric energy intake at baseline and at follow up. Data were processed using nutrition software (Win-Food 2.7-MediMatica) and analysed using Student’s paired T-test to determine pre- versus post-intervention differences. The results showed that total caloric intakes rose from 1690±290 before the educational intervention to 1700±330 kcal/day after the educational intervention in boys and from 1620±256 to 1640±260 kcal/day in girls. Statistical analysis of the data did not show any significant variation in pre- versus post-educational assessments (p<0.05), although the protein percentage increased by two points, from 14.5 to 16.5%, while both carbohydrate and lipid intake decreased by one percentage point. Student’s T-test analysis of dietary intakes evaluated, through the questionnaire, before and after the educational intervention revealed a significant increase (p<0.05) in calcium intake, which rose from 860±190 to 1060±200 mg/day in the girls and from 890±200 to 1100±210 mg/day in the boys, and in vitamin D intake, which rose from 3.6±1.53 μg/day to 4.1±2 μg/day, without significant differences emerging between the boys and girls. Although sub-optimal, the calcium intake obtained after the educational programme was sufficient to attain the target RDI of 1100–1200 mg/day. During the educational programme the percentage of children who drank milk rose from 92 to 96%. A change in the quantity of milk intake was also detected: the results showed a significant increase from 200±35 to about 270±65 ml/day in boys and girls (p<0.05). The observations on hard cheese intake revealed an increase in cheese consumers, from 84% to 91% at the end of the educational period. Similarly, a positive change was recorded in the percentage of children eating fresh vegetables: an increase from 89% to 96%.
Our educational programme appears to be significantly effective in modifying calcium intake in children. Analysis of the questionnaire data, which showed significantly increased consumption of dairy products and vegetables, without significant changes in total caloric intakes, revealed an important change in these children’s dietary habits. These behavioural modifications are the result of progressive nutritional education imparted through lessons, brochures, calendars, games, and crosswords. These findings may prompt school policy-makers to introduce educational strategies to promote students’ skeletal health.
PMCID: PMC3213834
5.  Next generation sequencing for molecular diagnosis of neurological disorders using ataxias as a model 
Brain  2013;136(10):3106-3118.
Many neurological conditions are caused by immensely heterogeneous gene mutations. The diagnostic process is often long and complex with most patients undergoing multiple invasive and costly investigations without ever reaching a conclusive molecular diagnosis. The advent of massively parallel, next-generation sequencing promises to revolutionize genetic testing and shorten the ‘diagnostic odyssey’ for many of these patients. We performed a pilot study using heterogeneous ataxias as a model neurogenetic disorder to assess the introduction of next-generation sequencing into clinical practice. We captured 58 known human ataxia genes followed by Illumina Next-Generation Sequencing in 50 highly heterogeneous patients with ataxia who had been extensively investigated and were refractory to diagnosis. All cases had been tested for spinocerebellar ataxia 1–3, 6, 7 and Friedrich’s ataxia and had multiple other biochemical, genetic and invasive tests. In those cases where we identified the genetic mutation, we determined the time to diagnosis. Pathogenicity was assessed using a bioinformatics pipeline and novel variants were validated using functional experiments. The overall detection rate in our heterogeneous cohort was 18% and varied from 8.3% in those with an adult onset progressive disorder to 40% in those with a childhood or adolescent onset progressive disorder. The highest detection rate was in those with an adolescent onset and a family history (75%). The majority of cases with detectable mutations had a childhood onset but most are now adults, reflecting the long delay in diagnosis. The delays were primarily related to lack of easily available clinical testing, but other factors included the presence of atypical phenotypes and the use of indirect testing. In the cases where we made an eventual diagnosis, the delay was 3–35 years (mean 18.1 years). Alignment and coverage metrics indicated that the capture and sequencing was highly efficient and the consumable cost was ∼£400 (€460 or US$620). Our pathogenicity interpretation pathway predicted 13 different mutations in eight different genes: PRKCG, TTBK2, SETX, SPTBN2, SACS, MRE11, KCNC3 and DARS2 of which nine were novel including one causing a newly described recessive ataxia syndrome. Genetic testing using targeted capture followed by next-generation sequencing was efficient, cost-effective, and enabled a molecular diagnosis in many refractory cases. A specific challenge of next-generation sequencing data is pathogenicity interpretation, but functional analysis confirmed the pathogenicity of novel variants showing that the pipeline was robust. Our results have broad implications for clinical neurology practice and the approach to diagnostic testing.
PMCID: PMC3784284  PMID: 24030952
ataxia; genetics; autosomal dominant cerebellar ataxia; autosomal recessive cerebellar ataxia; diagnosis
6.  Symposium on ‘Nutrition and health in children and adolescents’ Session 1: Nutrition in growth and development 
The growth and development of the human skeleton requires an adequate supply of many different nutritional factors. Classical nutrient deficiencies are associated with stunting (e.g. energy, protein, Zn), rickets (e.g. vitamin D) and other bone abnormalities (e.g. Cu, Zn, vitamin C). In recent years there has been interest in the role nutrition may play in bone growth at intakes above those required to prevent classical deficiencies, particularly in relation to optimising peak bone mass and minimising osteoporosis risk. There is evidence to suggest that peak bone mass and later fracture risk are influenced by the pattern of growth in childhood and by nutritional exposures in utero, in infancy and during childhood and adolescence. Of the individual nutrients, particular attention has been paid to Ca, vitamin D, protein and P. There has also been interest in several food groups, particularly dairy products, fruit and vegetables and foods contributing to acid–base balance. However, it is not possible at the present time to define dietary reference values using bone health as a criterion, and the question of what type of diet constitutes the best support for optimal bone growth and development remains open. Prudent recommendations (Department of Health, 1998; World Health Organization/Food and Agriculture Organization, 2003) are the same as those for adults, i.e. to consume a Ca intake close to the reference nutrient intake, optimise vitamin D status through adequate summer sunshine exposure (and diet supplementation where appropriate), be physically active, have a body weight in the healthy range, restrict salt intake and consume plenty of fruit and vegetables.
PMCID: PMC2039894  PMID: 17181901
Bone growth and development; Bone health; Nutritional factors; Dietary and lifestyle recommendations; Bone measurements in children
7.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
PMCID: PMC4028183  PMID: 24845081
8.  Genetics of bipolar disorder 
Journal of Medical Genetics  1999;36(8):585-594.
Bipolar disorder (also known as manic depressive illness) is a complex genetic disorder in which the core feature is pathological disturbance in mood (affect) ranging from extreme elation, or mania, to severe depression usually accompanied by disturbances in thinking and behaviour. The lifetime prevalence of 1% is similar in males and females and family, twin, and adoption studies provide robust evidence for a major genetic contribution to risk. There are methodological impediments to precise quantification, but the approximate lifetime risk of bipolar disorder in relatives of a bipolar proband are: monozygotic co-twin 40-70%; first degree relative 5-10%; unrelated person 0.5-1.5%. Occasional families may exist in which a single gene plays the major role in determining susceptibility, but the majority of bipolar disorder involves the interaction of multiple genes (epistasis) or more complex genetic mechanisms (such as dynamic mutation or imprinting). Molecular genetic positional and candidate gene approaches are being used for the genetic dissection of bipolar disorder. No gene has yet been identified but promising findings are emerging. Regions of interest identified in linkage studies include 4p16, 12q23-q24, 16p13, 21q22, and Xq24-q26. Chromosome 18 is also of interest but the findings are confusing with up to three possible regions implicated. To date most candidate gene studies have focused on neurotransmitter systems influenced by medication used in clinical management of the disorder but no robust positive findings have yet emerged. It is, however, almost certain that over the next few years bipolar susceptibility genes will be identified. This will have a major impact on our understanding of disease pathophysiology and will provide important opportunities to investigate the interaction between genetic and environmental factors involved in pathogenesis. This is likely to lead to major improvements in treatment and patient care but will also raise important ethical issues that will need to be addressed.

Keywords: bipolar disorder; manic depressive illness
PMCID: PMC1762980  PMID: 10465107
9.  Commentary on “A roadmap for the prevention of dementia II. Leon Thal Symposium 2008.” Prevention Trials in Persons At-Risk for Dominantly-Inherited Alzheimer's Disease: Opportunities and Challenges 
Autosomal dominant familial Alzheimer's disease (FAD) of young onset due to alterations in the PSEN1, APP, and PSEN2 genes is a fully-penetrant and devastating condition. As the subsequent development of AD in persons inheriting such genes is essentially certain, the condition provides a unique opportunity to perform informative studies of interventions with potential for preventing the disease. Though feasible, there are many challenges to such an endeavor including the fact that most persons at-risk for FAD do not desire to know their genetic status. Other challenges include the time course over which a preventative treatment would need to be administered and potential limitations to the degree to which the knowledge gained might be validly generalized to the more common late-onset AD. In this paper we discuss issues of study design including power estimates, protocols in which subjects' genetic status is not revealed to them, and the advantage of one-time interventions such as vaccinations. Though addressed in the context of FAD, many of the issues discussed are relevant to other fully-penetrant autosomal dominant degenerative illnesses such as Huntington's disease. We also discuss important next steps including the performance of pre-clinical studies in model systems appropriate for FAD and the recently funded international Dominantly Inherited Alzheimer Network (DIAN). The goals of the DIAN are to characterize the natural history of FAD and to establish the infrastructure that would be required to perform meaningful studies in this rare, widely dispersed, but informative population.
PMCID: PMC2746429  PMID: 19328453
10.  Exploration of transitional life events in individuals with Friedreich ataxia: Implications for genetic counseling 
Human development is a process of change, adaptation and growth. Throughout this process, transitional events mark important points in time when one's life course is significantly altered. This study captures transitional life events brought about or altered by Friedreich ataxia, a progressive chronic illness leading to disability, and the impact of these events on an affected individual's life course.
Forty-two adults with Friedreich ataxia (18-65y) were interviewed regarding their perceptions of transitional life events. Data from the interviews were coded and analyzed thematically using an iterative process.
Identified transitions were either a direct outcome of Friedreich ataxia, or a developmental event altered by having the condition. Specifically, an awareness of symptoms, fear of falling and changes in mobility status were the most salient themes from the experience of living with Friedreich ataxia. Developmental events primarily influenced by the condition were one's relationships and life's work.
Friedreich ataxia increased the complexity and magnitude of transitional events for study participants. Transitional events commonly represented significant loss and presented challenges to self-esteem and identity. Findings from this study help alert professionals of potentially challenging times in patients' lives, which are influenced by chronic illness or disability. Implications for developmental counseling approaches are suggested for genetic counseling.
Human development can be described in terms of key transitional events, or significant times of change. Transitional events initiate shifts in the meaning or direction of life and require the individual to develop skills or utilize coping strategies to adapt to a novel situation [1,2]. A successful transition has been defined as the development of a sense of mastery over the changed event [3].
Transitions can be influenced by a variety of factors including one's stage of development, such as graduation from high school, historical events, including war, and idiosyncratic factors, such as health status [4,5]. Of particular interest in the present study are transitional life events, brought about or altered by progressive chronic illness and disability, and the impact of these events on the lives of affected individuals.
It has been recognized that the clinical characteristics of a chronic illness or disability may alter the course and timing of many developmentally-related transitional events [6]. For example, conditions associated with a shortened lifespan may cause an individual to pursue a career with a shorter course of training [6]. Specific medical manifestations may also promote a lifestyle incongruent with developmental needs [6,7]. For example, an adolescent with a disability may have difficulty achieving autonomy because of his/her physical dependence on others.
In addition to the aforementioned effects of chronic illness and disability on developmentally-related transitional events, a growing body of literature has described disease-related transitional events: those changes that are a direct result of chronic illness and disability. Diagnosis has received attention as being a key disease-related transitional event [8,9]. Studies have also noted other disease transitions related to illness trajectory [10], as the clinical features of the disease may require the individual to make specific adaptations. Disease-related events have also been described in terms of accompanying psychological processes, such as one's awareness of differences brought about by illness [11].
While disease-related events are seemingly significant, the patient's perception of the events is varied. Some events may be perceived as positive experiences for the individual. For example, a diagnosis may end years of uncertainty. Some individuals may perceive these transitional events as insignificant, as they have accommodated to the continual change brought about by a chronic disease [12,13].
The aforementioned impact of disability and chronic illness on transitional events may create psychological stress. Developed by Lazarus and Folkman, the Transitional Model of Stress and Coping describes the process of adaptation to a health condition [14]. This model purports that individuals first appraise a stressor and then utilize a variety of coping strategies in order to meet the stressor's demands [14]. Thus, in the context of chronic illness, the ability of the individual to cope successfully with the stress of a health threat contributes to the process of overall adaptation to the condition.
The process of adaptation can be more complex when the chronic illness or disability is progressive. Each transition brought about or altered by the disability may also represent additional loss, including the loss of future plans, freedom in social life and the ability to participate in hobbies [15]. These losses may be accompanied by grief, uncertainty, and a continual need for adaptation [16,17].
Friedreich ataxia (FRDA) is one example of a progressive disorder, leading to adolescent and adult onset disability. To better understand patients' perceptions of key transitional events and the factors perceived to facilitate progression through these events, individuals with FRDA were interviewed.
FRDA is a rare, progressive, neurodegenerative disorder affecting approximately one in 30,000 people in the United States [18]. It equally affects both men and women. Individuals with FRDA experience progressive muscle weakness and loss of coordination in the arms and legs. For most patients, ataxia leads to motor incapacitation and full-time use of a wheelchair, commonly by the late teens or early twenties. Other complications such as vision and hearing impairment, dysarthria, scoliosis, diabetes mellitus and hypertrophic cardiomyopathy may occur [19,20]. Cardiomyopathy and respiratory difficulties often lead to premature death at an average age of 37 years [21]. Currently, there are no treatments or cures for FRDA. Little is known about the specific psychological or psychosocial effects of the condition.
FRDA is an autosomal recessive condition. The typical molecular basis of Friedreich ataxia is the expansion of a GAA trinucleotide repeat in both copies of the FXN gene [22]. Age of onset usually occurs in late childhood or early adolescence. However, the availability of genetic testing has identified affected individuals with an adult form of the condition. This late-onset form is thought to represent approximately 10-15% of the total FRDA population [23].
Health care providers of individuals with progressive, neurodegenerative disorders can help facilitate their patients' progression through transitional events. Data suggest that improvements should be made in the care of these individuals. Shaw et al. [24] found that individualized care that helps to prepare patients for transition is beneficial. Beisecker et al. [25] found that patients desire not only physical care from their providers, but also emotional and psychosocial support.
Genetic counselors have an important opportunity to help patients with neuromuscular disorders progress through transitional events, as several of these conditions have a genetic etiology. Genetic counselors in pediatric and adult settings often develop long-term relationships with patients, due to follow-up care. This extended relationship is becoming increasingly common as genetic counselors move into various medical sub-specialties, such as neurology, ophthalmology, oncology and cardiology.
The role of the genetic counselor in addressing the psychosocial needs of patients has been advocated, but rarely framed in the context of developmental events [26]. Data suggest that patients may not expect a genetic counselor to address psychosocial needs [27]. In a survey of genetic counseling patients, Wertz [28] found a majority of respondents understood genetic conditions to have a moderate to serious effect on family life and finances, while almost half perceived there to be an effect on the spouse, quality of life, and the relationship between home and work. However, these topics were reportedly not discussed within genetic counseling sessions [27,28]. Overall, there is limited information about the experiences of transitional life events in FRDA, as well as a lack of recommendations for genetic counselors and other health care providers to assist patients through these events.
Our study investigated perceptions of patients with Friedreich ataxia to 1) identify key transitional events and specific needs associated with events; 2) describe perception of factors to facilitate progression through the identified events; and 3) explore the actual or potential role of the health care provider in facilitating adaptation to the identified events. Data were used to make suggestions for developmental genetic counseling approaches in the context of ongoing care of clients with hereditary, progressive, neurodegenerative conditions.
PMCID: PMC2987979  PMID: 20979606
11.  Symposium on the Chemical Senses and Longevity: Introduction and Overview 
Dietary restriction is one manipulation that delays aging in every species in which it has been applied, including man. The mechanisms are not yet known. This symposium was designed to bring to ISOT 2008 leading scientists in the field of molecular mechanisms of aging/longevity to stimulate interest in novel research that may bridge the burgeoning interface between chemical senses and longevity. Speakers presented provocative research into the mechanisms underlying longevity that suggest that life span is regulated by both gene modification and environmental cues and that chemosensory perception of food-related environmental cues can modulate lifespan.
PMCID: PMC4144200  PMID: 19686211
chemosensory aging; longevity; olfactory impairment; gustatory; taste; smell; olfactory; odor; aging; c. elegans
12.  Genetic studies in pediatric ITP: outlook, feasibility and requirements 
Annals of hematology  2010;89(Suppl 1):S95-103.
The genomic revolution in medicine has not escaped attention of clinicians and scientists involved in medical management and research studies of immune thrombocytopenic purpura (ITP). In principle, ITP biology and care will benefit greatly from modern methods to understand the patterns of gene expression and genetic markers associated with fundamental parameters of the disease including predictors of remission; risk factors for severity; determinants of response to various therapies; and possibly biological sub-types. However, applying modern genetics to ITP carries severe challenges: (i) achieving adequate sample sizes is a fundamental problem because ITP is rare (and in pediatric ITP, chronic cases constitute only about 1/4 of the total); (ii) familial transmission of childhood ITP is so rare that a convincing pedigree requires consideration of other immunologic or hematologic disorders; (iii) ITP is probably biologically heterogeneous, based on clinical observations, immunological studies and animal models. Here we review the advantages and disadvantages of potential genetic approaches. Sufficient information is available to set reasonable bounds on which genetic analyses of ITP are feasible, and how they are most likely to be accomplished. The highest priority is for accurate phenotypes to compare to genetic analyses. Several registries worldwide hold promise for accomplishing this goal.
PMCID: PMC2910829  PMID: 20309691
GWAS; immune thrombocytopenic purpura; registries; cohort studies
13.  Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis 
PLoS Medicine  2012;9(10):e1001326.
Testing whether genetic information could inform the selection of the best drug for patients with depression, Rudolf Uher and colleagues searched for genetic variants that could predict clinically meaningful responses to two major groups of antidepressants.
It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.
Methods and Findings
The NEWMEDS consortium, an academia–industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10−8). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10−8) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.
No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study.
Please see later in the article for the Editors' Summary
Editors' Summary
Genetic and environmental factors can influence a person's response to medications. Taking advantage of the recent advancements in genetics, scientists are working to match specific gene variations with responses to particular medications. Knowing whether a patient is likely to respond to a drug or have serious side effects would allow doctors to select the best treatment up front. Right now, there are only a handful of examples where a patient's version of a particular gene predicts their response to a particular drug. Some scientists believe that there will be many more such matches between genetic variants and treatment responses. Others think that because the action of most drugs is influenced by many different genes, a variant in one of those genes is unlikely to have measurable effect in most cases.
Why Was This Study Done?
One of the areas where patients' responses to available drugs vary widely is severe depression (or major depressive disorder). Prescription of an antidepressant is often the first step in treating the disease. However, less than half of patients get well taking the first antidepressant prescribed. Those who don't respond to the first drug need to, together with their doctors, try multiple courses of treatment to find the right drug and the right dose for them. For some patients none of the existing drugs work well.
To see whether genetic information could help improve the choice of antidepressant, researchers from universities and the pharmaceutical industry joined forces in this large study. They examined two ways to use genetic information to improve the treatment of depression. First, they searched all genes for common genetic variants that could predict which patients would not respond to the two major groups of antidepressants (serotonin reuptake inhibitors, or SRIs, and noradrenaline reuptake inhibitors, or NRIs). They hoped that this would help with the development of new drugs that could help these patients. Second, they looked for common genetic variants in all genes that could identify patients who responded to one of the two major groups of antidepressants. Such predictors would make it possible to know which drug to prescribe for which patient.
What Did the Researchers Do and Find?
The researchers selected 1,790 patients with severe depression who had participated in one of several research studies; 1,222 of the patients had been treated with an SRI, the remaining 568 with an NRI, and it was recorded how well the drugs worked for each patient. The researchers also had a detailed picture of the genetic make-up of each patient, with information for over half a million genetic variants. They then looked for an association between genetic variants and responses to drugs.
They found not a single genetic variant that could predict clearly whether a person would respond to antidepressants in general, to one of the two main groups (SRIs and NRIs), or much better to one than the other. They also didn't find any combination of variants in groups of genes that work together that could predict responses. Combining their data with those from another large study did not yield any robust predictors either.
What Do These Findings Mean?
This study was large enough that it should have been possible to find common genetic variants that by themselves could predict a clinically meaningful response to SRIs and/or NRIs, had such variants existed. The fact that the study failed to find such variants suggests that such variants do not exist. It is still possible, however, that variants that are less common could predict response, or that combinations of variants could. To find those, if they do exist, even larger studies will need to be done.
Additional Information
Please access these websites via the online version of this summary at
The National Institute of General Medical Sciences at the US National Institutes of Health has a fact sheet on personalized medicine
PubMed Health at the US National Library of Medicine has a page on major depressive disorder
Wikipedia has pages on major depressive disorder and pharmacogenetics, the study of how genetic variation affects response to certain drugs (note that Wikipedia is a free online encyclopedia that anyone can edit)
The UK National Health Service has comprehensive information pages on depression
PMCID: PMC3472989  PMID: 23091423
14.  Physical Activity Attenuates the Influence of FTO Variants on Obesity Risk: A Meta-Analysis of 218,166 Adults and 19,268 Children 
Kilpeläinen, Tuomas O. | Qi, Lu | Brage, Soren | Sharp, Stephen J. | Sonestedt, Emily | Demerath, Ellen | Ahmad, Tariq | Mora, Samia | Kaakinen, Marika | Sandholt, Camilla Helene | Holzapfel, Christina | Autenrieth, Christine S. | Hyppönen, Elina | Cauchi, Stéphane | He, Meian | Kutalik, Zoltan | Kumari, Meena | Stančáková, Alena | Meidtner, Karina | Balkau, Beverley | Tan, Jonathan T. | Mangino, Massimo | Timpson, Nicholas J. | Song, Yiqing | Zillikens, M. Carola | Jablonski, Kathleen A. | Garcia, Melissa E. | Johansson, Stefan | Bragg-Gresham, Jennifer L. | Wu, Ying | van Vliet-Ostaptchouk, Jana V. | Onland-Moret, N. Charlotte | Zimmermann, Esther | Rivera, Natalia V. | Tanaka, Toshiko | Stringham, Heather M. | Silbernagel, Günther | Kanoni, Stavroula | Feitosa, Mary F. | Snitker, Soren | Ruiz, Jonatan R. | Metter, Jeffery | Larrad, Maria Teresa Martinez | Atalay, Mustafa | Hakanen, Maarit | Amin, Najaf | Cavalcanti-Proença, Christine | Grøntved, Anders | Hallmans, Göran | Jansson, John-Olov | Kuusisto, Johanna | Kähönen, Mika | Lutsey, Pamela L. | Nolan, John J. | Palla, Luigi | Pedersen, Oluf | Pérusse, Louis | Renström, Frida | Scott, Robert A. | Shungin, Dmitry | Sovio, Ulla | Tammelin, Tuija H. | Rönnemaa, Tapani | Lakka, Timo A. | Uusitupa, Matti | Rios, Manuel Serrano | Ferrucci, Luigi | Bouchard, Claude | Meirhaeghe, Aline | Fu, Mao | Walker, Mark | Borecki, Ingrid B. | Dedoussis, George V. | Fritsche, Andreas | Ohlsson, Claes | Boehnke, Michael | Bandinelli, Stefania | van Duijn, Cornelia M. | Ebrahim, Shah | Lawlor, Debbie A. | Gudnason, Vilmundur | Harris, Tamara B. | Sørensen, Thorkild I. A. | Mohlke, Karen L. | Hofman, Albert | Uitterlinden, André G. | Tuomilehto, Jaakko | Lehtimäki, Terho | Raitakari, Olli | Isomaa, Bo | Njølstad, Pål R. | Florez, Jose C. | Liu, Simin | Ness, Andy | Spector, Timothy D. | Tai, E. Shyong | Froguel, Philippe | Boeing, Heiner | Laakso, Markku | Marmot, Michael | Bergmann, Sven | Power, Chris | Khaw, Kay-Tee | Chasman, Daniel | Ridker, Paul | Hansen, Torben | Monda, Keri L. | Illig, Thomas | Järvelin, Marjo-Riitta | Wareham, Nicholas J. | Hu, Frank B. | Groop, Leif C. | Orho-Melander, Marju | Ekelund, Ulf | Franks, Paul W. | Loos, Ruth J. F.
PLoS Medicine  2011;8(11):e1001116.
Ruth Loos and colleagues report findings from a meta-analysis of multiple studies examining the extent to which physical activity attenuates effects of a specific gene variant, FTO, on obesity in adults and children. They report a fairly substantial attenuation by physical activity on the effects of this genetic variant on the risk of obesity in adults.
The FTO gene harbors the strongest known susceptibility locus for obesity. While many individual studies have suggested that physical activity (PA) may attenuate the effect of FTO on obesity risk, other studies have not been able to confirm this interaction. To confirm or refute unambiguously whether PA attenuates the association of FTO with obesity risk, we meta-analyzed data from 45 studies of adults (n = 218,166) and nine studies of children and adolescents (n = 19,268).
Methods and Findings
All studies identified to have data on the FTO rs9939609 variant (or any proxy [r2>0.8]) and PA were invited to participate, regardless of ethnicity or age of the participants. PA was standardized by categorizing it into a dichotomous variable (physically inactive versus active) in each study. Overall, 25% of adults and 13% of children were categorized as inactive. Interaction analyses were performed within each study by including the FTO×PA interaction term in an additive model, adjusting for age and sex. Subsequently, random effects meta-analysis was used to pool the interaction terms. In adults, the minor (A−) allele of rs9939609 increased the odds of obesity by 1.23-fold/allele (95% CI 1.20–1.26), but PA attenuated this effect (pinteraction  = 0.001). More specifically, the minor allele of rs9939609 increased the odds of obesity less in the physically active group (odds ratio  = 1.22/allele, 95% CI 1.19–1.25) than in the inactive group (odds ratio  = 1.30/allele, 95% CI 1.24–1.36). No such interaction was found in children and adolescents.
The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of PA in particular in those genetically predisposed to obesity.
Please see later in the article for the Editors' Summary
Editors’ Summary
Two in three Americans are overweight, of whom half are obese, and the trend towards increasing obesity is now seen across developed and developing countries. There has long been interest in understanding the impact of genes and environment when it comes to apportioning responsibility for obesity. Carrying a change in the FTO gene is common (found in three-quarters of Europeans and North Americans) and is associated with a 20%–30% increased risk of obesity. Some overweight or obese individuals may feel that the dice are loaded and there is little point in fighting the fat; it has been reported that those made aware of their genetic susceptibility to obesity may still choose a poor diet. A similar fatalism may occur when overweight and obese people consider physical activity. But disentangling the influence of physical activity on those genetically susceptible to obesity from other factors that might impact weight is not straightforward, as it requires large sample sizes, could be subject to publication bias, and may rely on less than ideal self-reporting methods.
Why Was This Study Done?
The public health ramifications of understanding the interaction between genetic susceptibility to obesity and physical activity are considerable. Tackling the rising prevalence of obesity will inevitably include interventions principally aimed at changing dietary intake and/or increasing physical activity, but the evidence for these with regards to those genetically susceptible has been lacking to date. The authors of this paper set out to explore the interaction between the commonest genetic susceptibility trait and physical activity using a rigorous meta-analysis of a large number of studies.
What Did the Researchers Do and Find?
The authors were concerned that a meta-analysis of published studies would be limited both by the data available to them and by possible bias. Instead of this more widely used approach, they took the literature search as their starting point, identified other studies through their collaborators’ network, and then undertook a meta-analysis of all available studies using a new and standardized analysis plan. This entailed an extremely large number of authors mining their data afresh to extract the relevant data points to enable such a meta-analysis. Physical activity was identified in the original studies in many different ways, including by self-report or by using an external measure of activity or heart rate. In order to perform the meta-analysis, participants were labeled as physically active or inactive in each study. For studies that had used a continuous scale, the authors decided that the bottom 20% of the participants were inactive (10% for children and adolescents). Using data from over 218,000 adults, the authors found that carrying a copy of the susceptibility gene increased the odds of obesity by 1.23-fold. But the size of this influence was 27% less in the genetically susceptible adults who were physically active (1.22-fold) compared to those who were physically inactive (1.30-fold). In a smaller study of about 19,000 children, no such effect of physical activity was seen.
What Do these Findings Mean?
This study demonstrates that people who carry the susceptibility gene for obesity can benefit from physical activity. This should inform health care professionals and the wider public that the view of genetically determined obesity not being amenable to exercise is incorrect and should be challenged. Dissemination, implementation, and ensuring uptake of effective physical activity programs remains a challenge and deserves further consideration. That the researchers treated “physically active” as a yes/no category, and how they categorized individuals, could be criticized, but this was done for pragmatic reasons, as a variety of means of assessing physical activity were used across the studies. It is unlikely that the findings would have changed if the authors had used a different method of defining physically active. Most of the studies included in the meta-analysis looked at one time point only; information about the influence of physical activity on weight changes over time in genetically susceptible individuals is only beginning to emerge.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLoS Medicine Perspective by Lennert Veerman
The US Centers for Disease Control and Prevention provides obesity-related statistics, details of prevention programs, and an overview on public health strategy in the United States
A more worldwide view is given by the World Health Organization
The UK National Health Service website gives information on physical activity guidelines for different age groups, while similar information can also be found from US sources
PMCID: PMC3206047  PMID: 22069379
15.  Role of sclerostin in bone and cartilage and its potential as a therapeutic target in bone diseases 
Sclerostin is a small protein expressed by the SOST gene in osteocytes, bone cells that respond to mechanical stress applied to the skeleton and appear to play an important role in the regulation of bone remodeling. When sclerostin binds to its receptors on the cell surface of osteoblasts, a downstream cascade of intracellular signaling is initiated, with the ultimate effect of inhibiting osteoblastic bone formation. Recent studies have shown that the SOST gene is also expressed by articular chondrocytes and that modulation of its activity may have effects on articular cartilage and subchondral bone. The role of sclerostin in the pathogenesis of osteoarthritis in humans has not yet been defined, and the potential utility of treating osteoarthritis with interventions that alter sclerostin is not known. Rare genetic skeletal disorders in humans with low sclerostin levels, such as sclerosteosis and van Buchem disease, have been associated with a high bone mineral density (BMD) phenotype and low risk of fractures. This has led to the concept that antisclerostin interventions might be useful in the treatment of patients with osteoporosis and skeletal disorders associated with low bone mass. Compounds that inhibit sclerostin have been shown to stimulate bone formation and reduce bone resorption, with a robust increase in BMD. Investigational monoclonal antibodies to sclerostin, including romosozumab, blosozumab, and BPS804, have advanced to phase II clinical trials or beyond. If antisclerostin therapy is found to have beneficial effects on clinical endpoints, such as reduction of fracture risk or improvement in quality of life in patients with osteoarthritis, with a favorable balance of benefit and risk, then this class of compounds may become a prominent addition to the options for therapy of osteoporosis and other skeletal disorders.
PMCID: PMC3956136  PMID: 24688605
anabolic; blosozumab; BPS804; osteoporosis; romosozumab; sclerostin
16.  Nosology and Classification of Genetic Skeletal Disorders – 2010 Revision& 
Genetic disorders involving the skeletal system arise through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their variety. The Nosology and Classification of Genetic Skeletal Disorders provides an overview of recognized diagnostic entities and groups them by clinical and radiographic features and molecular pathogenesis. The aim is to provide the Genetics, Pediatrics and Radiology community with a list of recognized genetic skeletal disorders that can be of help in the diagnosis of individual cases, in the delineation of novel disorders, and in building bridges between clinicians and scientists interested in skeletal biology.
In the 2010 revision, 456 conditions were included and placed in 40 groups defined by molecular, biochemical and/or radiographic criteria. Of these conditions, 316 were associated with mutations in one or more of 226 different genes, ranging from common, recurrent mutations to “private” found in single families or individuals. Thus, the Nosology is a hybrid between a list of clinically defined disorders, waiting for molecular clarification, and an annotated database documenting the phenotypic spectrum produced by mutations in a given gene.
The Nosology should be useful for the diagnosis of patients with genetic skeletal diseases, particularly in view of the information flood expected with the novel sequencing technologies; in the delineation of clinical entities and novel disorders, by providing an overview of established nosologic entities; and for scientists looking for the clinical correlates of genes, proteins and pathways involved in skeletal biology.
PMCID: PMC3166781  PMID: 21438135
17.  Nosology and Classification of Genetic Skeletal Disorders: 2010 Revision 
Genetic disorders involving the skeletal system arise through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their variety. The Nosology and Classification of Genetic Skeletal Disorders provides an overview of recognized diagnostic entities and groups them by clinical and radiographic features and molecular pathogenesis. The aim is to provide the Genetics, Pediatrics and Radiology community with a list of recognized genetic skeletal disorders that can be of help in the diagnosis of individual cases, in the delineation of novel disorders, and in building bridges between clinicians and scientists interested in skeletal biology. In the 2010 revision, 456 conditions were included and placed in 40 groups defined by molecular, biochemical, and/or radiographic criteria. Of these conditions, 316 were associated with mutations in one or more of 226 different genes, ranging from common, recurrent mutations to “private” found in single families or individuals. Thus, the Nosology is a hybrid between a list of clinically defined disorders, waiting for molecular clarification, and an annotated database documenting the phenotypic spectrum produced by mutations in a given gene. The Nosology should be useful for the diagnosis of patients with genetic skeletal diseases, particularly in view of the information flood expected with the novel sequencing technologies; in the delineation of clinical entities and novel disorders, by providing an overview of established nosologic entities; and for scientists looking for the clinical correlates of genes, proteins and pathways involved in skeletal biology. © 2011 Wiley-Liss, Inc.
PMCID: PMC3166781  PMID: 21438135
skeletal genetics; osteochondrodysplasias; nosology; dysostoses; molecular basis of disease
18.  Detecting Key Structural Features within Highly Recombined Genes 
PLoS Computational Biology  2007;3(1):e14.
Many microorganisms exhibit high levels of intragenic recombination following horizontal gene transfer events. Furthermore, many microbial genes are subject to strong diversifying selection as part of the pathogenic process. A multiple sequence alignment is an essential starting point for many of the tools that provide fundamental insights on gene structure and evolution, such as phylogenetics; however, an accurate alignment is not always possible to attain. In this study, a new analytic approach was developed in order to better quantify the genetic organization of highly diversified genes whose alleles do not align. This BLAST-based method, denoted BLAST Miner, employs an iterative process that places short segments of highly similar sequence into discrete datasets that are designated “modules.” The relative positions of modules along the length of the genes, and their frequency of occurrence, are used to identify sequence duplications, insertions, and rearrangements. Partial alleles of sof from Streptococcus pyogenes, encoding a surface protein under host immune selection, were analyzed for module content. High-frequency Modules 6 and 13 were identified and examined in depth. Nucleotide sequences corresponding to both modules contain numerous duplications and inverted repeats, whereby many codons form palindromic pairs. Combined with evidence for a strong codon usage bias, data suggest that Module 6 and 13 sequences are under selection to preserve their nucleic acid secondary structure. The concentration of overlapping tandem and inverted repeats within a small region of DNA is highly suggestive of a mechanistic role for Module 6 and 13 sequences in promoting aberrant recombination. Analysis of pbp2X alleles from Streptococcus pneumoniae, encoding cell wall enzymes that confer antibiotic resistance, supports the broad applicability of this tool in deciphering the genetic organization of highly recombined genes. BLAST Miner shares with phylogenetics the important predictive quality that leads to the generation of testable hypotheses based on sequence data.
Author Summary
Microbial genes that accumulate large amounts of nucleotide sequence diversity through lateral exchanges with other microorganisms are often central to understanding key interactions between the microbe and an ever-changing host or environment. Proper sequence alignment of multiple gene alleles is an essential starting point for many of the tools that provide fundamental insights on gene structure and evolution, and allow scientists to develop hypotheses on biological processes. However, for some of the most interesting genes, a good quality alignment can be impossible to attain. We introduce a new software program, BLAST Miner, for analyzing genes that cannot be well-aligned. It relies on identifying small gene segments having high levels of sequence homology, irrespective of their relative positions within the different genes. Genes encoding a drug-resistance determinant and a target of host immunity are used as examples to demonstrate the application of BLAST Miner, and a potentially novel mechanism for generating genetic change is uncovered. This new bioinformatics tool provides an avenue for studying genes that are intractable by most other analytic approaches.
PMCID: PMC1782043  PMID: 17257051
19.  In Silico Detection of Sequence Variations Modifying Transcriptional Regulation 
Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at for all researchers interested in the detection and characterization of regulatory sequence variation.
Author Summary
DNA sequence variations (polymorphisms) that affect the expression levels of genes play important roles in the pathogenesis of many complex diseases. Compared with genetic variations that alter the amino acid sequences of encoded proteins, which are relatively easy to identify, sequence variants that affect the regulation of genes are difficult to pinpoint among the large amount of nonfunctional polymorphisms located in the vicinity of genes. Computational methods to distinguish functional from neutral variations could therefore prove useful to direct limited laboratory resources to sites most likely to exhibit a phenotypic effect. In this paper we present a Web-based tool for the identification of genetic variation in potential transcription factor binding sites. This tool can be used by any scientist interested in the characterization of regulatory polymorphisms. Using experimentally verified regulatory polymorphisms and background data collected from the literature, we evaluate the method's capacity to identify regulatory genetic variation, and we discuss the limitations of its application.
PMCID: PMC2211530  PMID: 18208319
20.  Epistatic relationships reveal the functional organization of yeast transcription factors 
A comprehensive quantitative genetic interaction map, or E-MAP, has provided a global view of the functional interdependencies between the components of the transcriptional apparatus in budding yeast.Transcription factors that display aggravating/negative genetic interactions regulate gene expression in an independent rather than coordinated manner.Parallel/compensating relationships between regulators often characterize transcriptional circuits.
Genetic interactions identify the functional interdependencies between genes (Guarente, 1993). They can be either positive (i.e. alleviating) or negative (i.e. aggravating) in nature corresponding to cases where the double mutant grows better or worse, respectively, then expected from growth of the corresponding single mutants (Beyer et al, 2007). Negative genetic interactions between non-essential genes often identify factors involved in parallel pathways, whereas positive ones often correspond to cases where the corresponding proteins are working in the same pathway and/or are physically associated (Beltrao et al, 2010). The epistatic miniarray profile (E-MAP) approach (Schuldiner et al, 2005), which quantitatively and comprehensively identifies both positive and negative genetic interactions on a logically selected set of genes, was used in this study in S. cerevisiae to genetically interrogate the set of 151 sequence-specific transcription factors (STFs) as well as 172 components of the general transcriptional machinery (GTFs).
We found a higher propensity of the group of STFs to strongly genetically interact with GTFs than with themselves (Figure 1A and B). However, within the set of STF–STF genetic interactions, there was a significant enrichment of negative over positive genetic interactions (Figure 1A and C), suggesting that parallel/compensating relationships, rather than linear pathways, predominate within the set of STFs. These genetic trends are in stark contrast to what was previously observed with factors involved in regulating signaling (e.g. kinases and phosphatases), which were significantly enriched in positive over negative genetic interactions (Fiedler et al, 2009).
In addition to providing an overview of the global relationships among TFs, the fine structure of the E-MAP can be used to address the nature of the regulatory architecture controlling individual genes. A variety of regulatory patterns have been described that serve the differing functional requirements of various biological processes (Istrail and Davidson, 2005). Our E-MAP identified several examples of the regulatory relationships between transcription factors, including (1) one TF acting as a repressor of another TF (e.g. Gal80 acting as a repressor of Gal4, the activator of the GAL genes); (2) two TFs acting redundantly to regulate a set of genes (e.g. Gln3 and Gat1, two GATA family activators involved in regulating nitrogen catabolite repression (NCR)); and (3) two TFs regulating genes in a coordinated manner (e.g. Hac1 working with the HDAC complex Rpd3C(L) to regulate expression of early meiotic genes).
Given the complex structures of promoters (Zhu and Zhang, 1999; Chin et al, 2005) and the possible types of regulatory logic (Buchler et al, 2003), we wanted to identify the types of logic that are used in nature. We explored this by combining our genetic interaction data with the information about the network connections between STFs and their targets. By initially focusing on pairs of STFs that share a set of targets defined by the genome-wide binding studies (Harbison et al, 2004; MacIsaac et al, 2006), a total of 110 STF gene pairs were identified that have statistically significant target overlap with a P-value <0.005, whereas 49 pairs have significant overlap at a more stringent cutoff (P<10−7). Several examples were examined in more detail by quantitative growth assay in liquid culture and gene expression profiling of the TF-deletion mutants. In each case, the growth rate of one of the single-deletion mutants is significantly reduced (i.e. ‘the major regulator'), whereas the growth rate of the other single-deletion mutant is similar to that of the wild type (i.e. ‘the minor regulator'). In the absence of the major regulator, the deletion of the minor regulator leads to a more severe growth defect, resulting in a negative genetic interaction (Figure 5A). We examined the response of common target genes of two pairs of TFs (Swi4-Skn7 and Gcr2-Tye7) and found an enrichment of common target genes displaying ‘OR' but not ‘AND' behavior, in the simplified language of Boolean logic. Further examination of the targets revealed that many of them are induced/repressed more by the double deletion than each of the single deletions (Figure 5D). Collectively, these results suggest that frequently TF pairs with negative interactions regulate the transcription of their common target genes in a redundant manner.
The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.
PMCID: PMC2990640  PMID: 20959818
genetic interaction; regulatory network; transcription factor; transcription regulation
21.  Development of computations in bioscience and bioinformatics and its application: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB06) 
BMC Bioinformatics  2006;7(Suppl 4):S1.
The first symposium of computations in bioinformatics and bioscience (SCBB06) was held in Hangzhou, China on June 21–22, 2006. Twenty-six peer-reviewed papers were selected for publication in this special issue of BMC Bioinformatics. These papers cover a broad range of topics including bioinformatics theories, algorithms, applications and tool development. The main technical topics contain gene expression analysis, sequence analysis, genome analysis, phylogenetic analysis, gene function prediction, molecular interaction and system biology, genetics and population study, immune strategy, protein structure prediction and proteomics.
PMCID: PMC1780134  PMID: 17217501
22.  How genomics has informed our understanding of the pathogenesis of osteoporosis 
Genome Medicine  2009;1(9):84.
Osteoporosis is a skeletal disorder characterized by compromised bone strength that predisposes a person to an increased risk of fracture. Osteoporosis is a complex trait that involves multiple genes, environmental factors, and gene-gene and gene-environment interactions. Twin and family studies have indicated that between 25% and 85% of the variation in bone mass and other skeletal phenotypes is heritable, but our knowledge of the underlying genes is limited. Bone mineral density is the most common assessment for diagnosing osteoporosis and is the most often used quantitative value in the design of genetic studies. In recent years, our understanding of the pathophysiology of osteoporosis has been greatly facilitated by advances brought about by the Human Genome Project. Genetic approaches ranging from family studies of monogenic traits to association studies with candidate genes, to whole-genome scans in both humans and animals have identified a small number of genes that contribute to the heritability of bone mass. Studies with transgenic and knockout mouse models have revealed major new insights into the biology of many of these identified genes, but much more needs to be learned. Ultimately, we hope that by revealing the underlying genetics and biology driving the pathophysiology of osteoporosis, new and effective treatment can be developed to combat and possibly cure this devastating disease. Here we review the rapidly evolving field of the genomics of osteoporosis with a focus on important gene discoveries, new biological/physiological paradigms that are emerging, and many of the unanswered questions and hurdles yet to be overcome in the field.
PMCID: PMC2768991  PMID: 19735586
23.  Biological changes associated with healthy versus pathological aging: A symposium review 
Ageing research reviews  2009;8(2):140-146.
The Douglas Mental Health University Institute, in collaboration with the McGill Centre for Studies in Aging, organized a two day symposium entitled “Biological Changes Associated with Healthy Versus Pathological Aging” that was held in December 13 and 14, 2007 on the Douglas campus. The symposium involved presentations on current trends in aging and dementia research across several sub-disciplines: genetics, neurochemistry, structural and functional neuroimaging and clinical treatment and rehabilitation. The goal of this symposium was to provide a forum for knowledge-transfer between scientists and clinicians with different specializations in order to promote cross-fertilization of research ideas that would lead to future collaborative neuroscience research in aging and dementia. In this review article we summarize the presentations made by the thirteen international scientists at the symposium and highlight: (i) past research, and future research trends in neuroscience of aging and dementia and (ii) links across levels of analysis that can lead to fruitful transdisciplinary research programs that will advance knowledge about the neurobiological changes associated with healthy aging and dementia.
PMCID: PMC2671241  PMID: 19274854
healthy aging; dementia; hippocampus; prefrontal cortex; amyloid deposition; MRI; volumetry; dopamine
24.  Systems Biology, Bioinformatics, and Biomarkers in Neuropsychiatry 
Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a “top-down” method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders’ complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software “omics”-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.
PMCID: PMC3529307  PMID: 23269912
systems biology; biomarkers; bioinformatics; psychiatry; data mining; proteomics; autism; omics
25.  Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks 
PLoS ONE  2008;3(11):e3740.
Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development.
Methodology/Principal Findings
In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential “interactome” network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes.
Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets.
PMCID: PMC2582945  PMID: 19011694

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