Novel family-based approaches to genetic risk in thrombosis

Genetic Influences on Cognitive Dysfunction in Schizophrenia

Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case-control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.

Footprints in the Sand: Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity

With the modern quality, quantity, and availability of genomic sequencing across species, as well as across the expanse of human populations, we can screen for shared signatures underlying longevity and lifespan. Knowledge of these mechanisms would be medically invaluable in combating aging and age-related diseases. The diversity of longevities across vertebrates is an opportunity to look for patterns of genetic variation that may signal how this life history property is regulated, and ultimately how it can be modulated. Variation in human longevity provides a unique window to look for cases of extreme lifespan within a population, as well as associations across populations for factors that influence capacity to live longer. Current large cohort studies support the use of population level analyses to identify key factors associating with human lifespan. These studies are powerful in concept, but have demonstrated limited ability to resolve signals from background variation. In parallel, the expanding catalog of sequencing and annotation from diverse species, some of which have evolved longevities well past a human lifespan, provides independent cases to look at the genomic signatures of longevity. Recent comparative genomic work has shown promise in finding shared mechanisms associating with longevity among distantly related vertebrate groups. Given the genetic constraints between vertebrates, we posit that a combination of approaches, of parallel meta-analysis of human longevity along with refined analysis of other vertebrate clades having exceptional longevity, will aid in resolving key regulators of enhanced lifespan that have proven to be elusive when analyzed in isolation.

Creativity and Bipolar Disorder: A Shared Genetic Vulnerability

Bipolar disorder is a lifelong mood disorder characterized by extreme mood swings between mania and depression. Despite fitness costs associated with increased mortality and significant impairment, bipolar disorder has persisted in the population with a high heritability and a stable prevalence. Creativity and other positive traits have repeatedly been associated with the bipolar spectrum, particularly among unaffected first-degree relatives and those with milder expressions of bipolar traits. This suggests a model in which large doses of risk variants cause illness, but mild to moderate doses confer advantages, which serve to maintain bipolar disorder in the population. Bipolar disorder may thus be better conceptualized as a dimensional trait existing at the extreme of normal population variation in positive temperament, personality, and cognitive traits, aspects of which may reflect a shared vulnerability with creativity. Investigations of this shared vulnerability may provide insight into the genetic mechanisms underlying illness and suggest novel treatments.

Genome-wide Association of Endophenotypes for Schizophrenia From the Consortium on the Genetics of Schizophrenia (COGS) Study

IMPORTANCE

The Consortium on the Genetics of Schizophrenia (COGS) uses quantitative neurophysiological and neurocognitive endophenotypes with demonstrated deficits in schizophrenia as a platform from which to explore the underlying neural circuitry and genetic architecture. Many of these endophenotypes are associated with poor functional outcome in schizophrenia. Some are also endorsed as potential treatment targets by the US Food and Drug Administration.

OBJECTIVE

To build on prior assessments of heritability, association, and linkage in the COGS phase 1 (COGS-1) families by reporting a genome-wide association study (GWAS) of 11 schizophrenia-related endophenotypes in the independent phase 2 (COGS-2) cohort of patients with schizophrenia and healthy comparison participants (HCPs).

DESIGN, SETTING, AND PARTICIPANTS

A total of 1789 patients with schizophrenia and HCPs of self-reported European or Latino ancestry were recruited through a collaborative effort across the COGS sites and genotyped using the PsychChip. Standard quality control filters were applied, and more than 6.2 million variants with a genotyping call rate of greater than 0.99 were available after imputation. Association was performed for data sets stratified by diagnosis and ancestry using linear regression and adjusting for age, sex, and 5 principal components, with results combined through weighted meta-analysis. Data for COGS-1 were collected from January 6, 2003, to August 6, 2008; data for COGS-2, from June 30, 2010, to February 14, 2014. Data were analyzed from October 28, 2016, to May 4, 2018.

MAIN OUTCOMES AND MEASURES

A genome-wide association study was performed to evaluate association for 11 neurophysiological and neurocognitive endophenotypes targeting key domains of schizophrenia related to inhibition, attention, vigilance, learning, working memory, executive function, episodic memory, and social cognition.

RESULTS

The final sample of 1533 participants included 861 male participants (56.2%), and the mean (SD) age was 41.8 (13.6) years. In total, 7 genome-wide significant regions (P < 5 × 10-8) and 2 nearly significant regions (P < 9 × 10-8) containing several genes of interest, including NRG3 and HCN1, were identified for 7 endophenotypes. For each of the 11 endophenotypes, enrichment analyses performed at the level of P < 10-4 compared favorably with previous association results in the COGS-1 families and showed extensive overlap with regions identified for schizophrenia diagnosis.

CONCLUSIONS AND RELEVANCE

These analyses identified several genomic regions of interest that require further exploration and validation. These data seem to demonstrate the utility of endophenotypes for resolving the genetic architecture of schizophrenia and characterizing the underlying biological dysfunctions. Understanding the molecular basis of these endophenotypes may help to identify novel treatment targets and pave the way for precision-based medicine in schizophrenia and related psychotic disorders.

Endophenotypes in Schizophrenia: Digging Deeper to Identify Genetic Mechanisms

Schizophrenia (SZ) is a severe psychotic disorder that is highly heritable and common in the general population. The genetic heterogeneity of SZ is substantial, with contributions from common, rare, and de novo variants, in addition to environmental factors. Large genome-wide association studies have detected many variants that are associated with SZ, yet the pathways by which these variants influence risk remain largely unknown. SZ is also clinically heterogeneous, with patients exhibiting a broad range of deficits and symptom severity that vary over the course of illness and treatment, which has complicated efforts to identify risk variants. However, the underlying brain dysfunction forms a more stable trait marker that quantitative neurocognitive and neurophysiological endophenotypes may be able to objectively measure. These endophenotypes are less likely to be heterogeneous than the disorder and provide a neurobiological context to detect risk variants and underlying pathways among genes associated with SZ diagnosis. Furthermore, many endophenotypes are translational into animal model systems, allowing for direct evaluation of the neural circuit dysfunctions and neurobiological substrates. We review a selection of the most promising SZ endophenotypes, including prepulse inhibition, mismatch negativity, oculomotor antisaccade, letter-number sequencing, and continuous performance tests. We also highlight recent findings from large consortia that suggest the potential role of genes, particularly in the neuregulin and glutamate pathways, in several of these endophenotypes. Although endophenotypes require additional time and effort to assess, the insight into the underlying neurobiology that they provide may ultimately reveal the underlying genetic architecture for SZ and suggest novel treatment targets.

Glycated Serum Protein Genetics and Pleiotropy with Cardiometabolic Risk Factors

Measurements of fasting glucose (FG) or glycated hemoglobin A1c (HbA1c) are two clinically approved approaches commonly used to determine glycemia, both of which are influenced by genetic factors. Obtaining accurate measurements of FG or HbA1c is not without its challenges, though. Measuring glycated serum protein (GSP) offers an alternative approach for assessing glycemia. The aim of this study was to estimate the heritability of GSP and GSP expressed as a percentage of total serum albumin (%GA) using a variance component approach and localize genomic regions (QTLs) that harbor genes likely to influence GSP and %GA trait variation in a large extended multigenerational pedigree from Jiri, Nepal (n = 1,800). We also performed quantitative bivariate analyses to assess the relationship between GSP or %GA and several cardiometabolic traits. Additive genetic effects significantly influence variation in GSP and %GA levels (p values: 1.15 × 10^-5 and 3.39 × 10^-5, respectively). We localized a significant (LOD score = 3.18) and novel GSP QTL on chromosome 11q, which has been previously linked to type 2 diabetes. Two common (MAF > 0.4) SNPs within the chromosome 11 QTL were associated with GSP (adjusted pvalue < 5.87 × 10^-5): an intronic variant (rs10790184) in the DSCAML1 gene and a 3'UTR variant (rs8258) in the CEP164 gene. Significant positive correlations were observed between GSP or %GA and blood pressure, and lipid traits (p values: 0.0062 to 1.78 × 10^-9). A significant negative correlation was observed between %GA and HDL cholesterol (p = 1.12 × 10^-5). GSP is influenced by genetic factors and can be used to assess glycemia and diabetes risk. Thus, GSP measurements can facilitate glycemic studies when accurate FG and/or HbA1c measurements are difficult to obtain. GSP can also be measured from frozen blood (serum) samples, which allows the prospect of retrospective glycemic studies using archived samples.

Deficient prepulse inhibition in schizophrenia in a multi-site cohort: Internal replication and extension

BACKGROUND

The Consortium on the Genetics of Schizophrenia (COGS) collected case-control endophenotype and genetic information from 2457 patients and healthy subjects (HS) across 5 test sites over 3.5 years. Analysis of the first "wave" (W1) of 1400 subjects identified prepulse inhibition (PPI) deficits in patients vs. HS. Data from the second COGS "wave" (W2), and the combined W(1+2), were used to assess: 1) the replicability of PPI deficits in this design; 2) the impact of response criteria on PPI deficits; and 3) PPI in a large cohort of antipsychotic-free patients.

METHODS

PPI in W2 HS (n=315) and schizophrenia patients (n=326) was compared to findings from W1; planned analyses assessed the impact of diagnosis, "wave" (1 vs. 2), and startle magnitude criteria. Combining waves allowed us to assess PPI in 120 antipsychotic-free patients, including many in the early course of illness.

RESULTS

ANOVA of all W(1+2) subjects revealed robust PPI deficits in patients across "waves" (p<0.0004). Strict response criteria excluded almost 39% of all subjects, disproportionately impacting specific subgroups; ANOVA in this smaller cohort confirmed no significant effect of "wave" or "wave x diagnosis" interaction, and a significant effect of diagnosis (p<0.002). Antipsychotic-free, early-illness patients had particularly robust PPI deficits.

DISCUSSION

Schizophrenia-linked PPI deficits were replicable across two multi-site "waves" of subjects collected over 3.5years. Strict response criteria disproportionately excluded older, male, non-Caucasian patients with low-normal hearing acuity. These findings set the stage for genetic analyses of PPI using the combined COGS wave 1 and 2 cohorts.

Assessment of Cognition and Personality as Potential Endophenotypes in the Western Australian Family Study of Schizophrenia

Phenotypic heterogeneity is a major barrier to understanding the genetic architecture underlying schizophrenia. Incorporating endophenotypes is one way to reduce heterogeneity and facilitate more powerful genetic analysis. Candidate endophenotypes require systematic assessment against endophenotype criteria, and a ranking of their potential utility for genetic analysis. In this study we assess 20 cognitive and personality measures in a sample of 127 families with at least 2 cases of schizophrenia per family (n = 535) plus a set of 30 control families (n = 121) against 4 endophenotype criteria: (a) be associated with the illness but not be a part of its diagnosis, (b) be heritable, (c) co-segregate with the illness in families, and (d) be found in unaffected relatives at a higher rate than in the general population. The endophenotype ranking score (endophenotype ranking variable [ERV]) was used to rank candidate endophenotypes based on their heritability and genetic correlation with schizophrenia. Finally, we used factor analysis to explore latent factors underlying the cognitive and personality measures. Evidence for personality measures as endophenotypes was at least equivalent to that of the cognitive measures. Factor analysis indicated that personality and cognitive traits contribute to independent latent dimensions. The results suggest for this first time that a number of cognitive and personality measures are independent and informative endophenotypes. Use of these endophenotypes in genetic studies will likely improve power and facilitate novel aetiological insights.

ADHD-related symptoms and attention profiles in the unaffected siblings of probands with autism spectrum disorder: focus on the subtypes of autism and Asperger's disorder

BACKGROUND

The presence of attention-deficit/hyperactive disorder (ADHD) symptoms and impaired attention performance are commonly noted in individuals with autism spectrum disorder (ASD). However, little is known about attention performance in their unaffected siblings. This study aimed to investigate the ADHD-related traits and attention performance in unaffected siblings of probands with autism and Asperger syndrome (AS), as well as the clinical correlates of ADHD-related traits.

METHODS

We assessed the intention, hyperactivity-impulsivity, and oppositional symptoms, and attention profiles of 199 probands with a diagnosis of ASD (122 autism, 77 AS), their unaffected siblings, and 196 typically developing controls (TD) by their parents' reports on the ADHD-related symptoms and the Connors' Continuous Performance Test (CCPT), respectively.

RESULTS

Compared to TD, unaffected siblings of ASD probands were more hyperactive/impulsive and oppositional, particularly unaffected siblings of AS probands. In CCPT, unaffected siblings of AS have intermediate levels of performance between probands with AS and TD on focused attention and sustained attention but were not statistically different from AS probands or TD in these attention profiles. In contrast, unaffected siblings of autism probands have significantly better CCPT performance when compared to autism probands but not to TD. In addition, stereotyped behaviors predicted ADHD-related traits in both sibling groups, but distinctive patterns of other correlates for ADHD-related traits were found between the two sibling groups.

CONCLUSIONS

This work suggested that unaffected siblings of AS, but not autism, have more hyperactive/impulsive traits and a trend of pervasive attention deficits assessed by CCPT which might serve as potential endophenotypes for genetic studies in AS.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01582256.

Genetic Contribution of Femoral Neck Bone Geometry to the Risk of Developing Osteoporosis: A Family-Based Study

Femoral neck geometry parameters are believed to be as good as bone mineral density as independent factors in predicting hip fracture risk. This study was conducted to analyze the roles of genetic and environmental factors in femoral properties measured in a sample of Spanish families with osteoporotic fractures and extended genealogy. The "Genetic Analysis of Osteoporosis (GAO) Project" involved 11 extended families with a total number of 376 individuals. We studied three categorical phenotypes of particular clinical interest and we used a Hip structural analysis based on DXA to analyze 17 strength and geometrical phenotypes of the hip. All the femoral properties had highly significant heritability, ranging from 0.252 to 0.586. The most significant correlations were observed at the genetic level (ρG). Osteoporotic fracture status (Affected 2) and, particularly, low bone mass and osteoporotic condition (Affected 3) had the highest number of significant genetic correlations with diverse femoral properties. In conclusion, our findings suggest that a relatively simple and easy to use method based on DXA studies can provide useful data on properties of the Hip in clinical practice. Furthermore, our results provide a strong motivation for further studies in order to improve the understanding of the pathophysiological mechanism underlying bone architecture and the genetics of osteoporosis.

Genetics of cognitive control: Implications for Nimh's research domain criteria initiative

Cognitive control refers to a set of mental processes that modulate other cognitive and emotional systems in service of goal-directed adaptive behavior. There is growing support for the notion that cognitive control abnormalities are a central component of many of the neuropsychological deficits observed in individuals with mental illnesses, particularly those with psychotic disorders. NIMH's research domain criteria (RDoC) initiative, which is designed to develop biologically informed constructs to better understand psychopathology, designated cognitive control a construct within the cognitive systems domain. Identification of genes that influence cognitive control or its supportive brain systems will improve our understating of the RDoC construct and provide candidate genes for psychotic disorders. We examine evidence for cognitive control deficits in psychosis, determine if these measures could be useful endophenotypes, and explore work linking genetic variation to cognitive control performance. While there is a wealth of evidence to support the notion the cognitive control is a valid endophenotype for psychosis, its genetic underpinning remains ill characterized. However, existing work provides a promising foundation on which future endeavors might build. Confirming existing individual gene associations will go some way to expanding our understanding of the genetics of cognitive control, and by extension, psychotic disorders. Yet, to truly understand the molecular underpinnings of such complex traits, it may be necessary to evaluate genes in tandem, focusing not on single genes but rather on empirically derived gene sets or on functionally defined networks of genes.

The Norwegian preeclampsia family cohort study: a new resource for investigating genetic aspects and heritability of preeclampsia and related phenotypes

Background

Preeclampsia is a major pregnancy complication without curative treatment available. A Norwegian Preeclampsia Family Cohort was established to provide a new resource for genetic and molecular studies aiming to improve the understanding of the complex pathophysiology of preeclampsia.

Methods

Participants were recruited from five Norwegian hospitals after diagnoses of preeclampsia registered in the Medical birth registry of Norway were verified according to the study’s inclusion criteria. Detailed obstetric information and information on personal and family disease history focusing on cardiovascular health was collected. At attendance anthropometric measurements were registered and blood samples were drawn. The software package SPSS 19.0 for Windows was used to compute descriptive statistics such as mean and SD. P-values were computed based on t-test statistics for normally distributed variables. Nonparametrical methods (chi square) were used for categorical variables.

Results

A cohort consisting of 496 participants (355 females and 141 males) representing 137 families with increased occurrence of preeclampsia has been established, and blood samples are available for 477 participants. Descriptive analyses showed that about 60 % of the index women’s pregnancies with birth data registered were preeclamptic according to modern diagnosis criteria. We also found that about 41 % of the index women experienced more than one preeclamptic pregnancy. In addition, the descriptive analyses confirmed that preeclamptic pregnancies are more often accompanied with delivery complications.

Conclusion

The data and biological samples collected in this Norwegian Preeclampsia Family Cohort will provide an important basis for future research. Identification of preeclampsia susceptibility genes and new biomarkers may contribute to more efficient strategies to identify mothers “at risk” and contribute to development of novel preventative therapies.

Recurrent major depression and right hippocampal volume: A bivariate linkage and association study

Previous work has shown that the hippocampus is smaller in the brains of individuals suffering from major depressive disorder (MDD) than those of healthy controls. Moreover, right hippocampal volume specifically has been found to predict the probability of subsequent depressive episodes. This study explored the utility of right hippocampal volume as an endophenotype of recurrent MDD (rMDD). We observed a significant genetic correlation between the two traits in a large sample of Mexican American individuals from extended pedigrees (ρg = -0.34, p = 0.013). A bivariate linkage scan revealed a significant pleiotropic quantitative trait locus on chromosome 18p11.31-32 (LOD = 3.61). Bivariate association analysis conducted under the linkage peak revealed a variant (rs574972) within an intron of the gene SMCHD1 meeting the corrected significance level (χ(2) = 19.0, p = 7.4 × 10(-5)). Univariate association analyses of each phenotype separately revealed that the same variant was significant for right hippocampal volume alone, and also revealed a suggestively significant variant (rs12455524) within the gene DLGAP1 for rMDD alone. The results implicate right-hemisphere hippocampal volume as a possible endophenotype of rMDD, and in so doing highlight a potential gene of interest for rMDD risk.

Comparison of Twin and Extended Pedigree Designs for Obtaining Heritability Estimates

This study explores power assumptions relating to extended pedigree designs (EPD) and classical twin designs (CTD). We conducted statistical analyses to compare the power of the two designs for examining neuroimaging phenotypes, varying heritability and varying whether shared environmental variance is fixed or free. Results indicated that CTDs have more power to estimate heritability, with the exception of one condition: in EPDs, the power increases relative to CTDs when shared environmental variance contributes to sibling similarity only. We additionally show that assuming a priori that shared environmental effects play no role in a phenotype-as is commonly done in pedigree designs-can lead to substantially biased heritability estimates. General results indicate that both CTDs and EPDs obtain quite precise heritability estimates. Finally, we discuss methodological considerations relating to assumptions about age effects and shared environment.

Discovering schizophrenia endophenotypes in randomly ascertained pedigrees

BACKGROUND

Although case-control approaches are beginning to disentangle schizophrenia's complex polygenic burden, other methods will likely be necessary to fully identify and characterize risk genes. Endophenotypes, traits genetically correlated with an illness, can help characterize the impact of risk genes by providing genetically relevant traits that are more tractable than the behavioral symptoms that classify mental illness. Here, we present an analytic approach for discovering and empirically validating endophenotypes in extended pedigrees with very few affected individuals. Our approach indexes each family member's risk as a function of shared genetic kinship with an affected individual, often referred to as the coefficient of relatedness. To demonstrate the utility of this approach, we search for neurocognitive and neuroanatomic endophenotypes for schizophrenia in large unselected multigenerational pedigrees.

METHODS

A fixed-effects test within the variance component framework was performed on neurocognitive and cortical surface area traits in 1606 Mexican-American individuals from large, randomly ascertained extended pedigrees who participated in the Genetics of Brain Structure and Function study. As affecteds were excluded from analyses, results were not influenced by disease state or medication usage.

RESULTS

Despite having sampled just 6 individuals with schizophrenia, our sample provided 233 individuals at various levels of genetic risk for the disorder. We identified three neurocognitive measures (digit-symbol substitution, facial memory, and emotion recognition) and six medial temporal and prefrontal cortical surfaces associated with liability for schizophrenia.

CONCLUSIONS

With our novel analytic approach, one can discover and rank endophenotypes for schizophrenia, or any heritable disease, in randomly ascertained pedigrees.

Influence of age, sex and genetic factors on the human brain

We report effects of age, age(2), sex and additive genetic factors on variability in gray matter thickness, surface area and white matter integrity in 1,010 subjects from the Genetics of Brain Structure and Function Study. Age was more strongly associated with gray matter thickness and fractional anisotropy of water diffusion in white matter tracts, while sex was more strongly associated with gray matter surface area. Widespread heritability of neuroanatomic traits was observed, suggesting that brain structure is under strong genetic control. Furthermore, our findings indicate that neuroimaging-based measurements of cerebral variability are sensitive to genetic mediation. Fundamental studies of genetic influence on the brain will help inform gene discovery initiatives in both clinical and normative samples.

Common variants in BDNF, FAIM2, FTO, MC4R, NEGR1, and SH2B1 show association with obesity-related variables in Spanish Roma population

OBJECTIVES

The objective of this study is to investigate the association between previously GWAS identified genetic variants predisposing to obesity in Europeans and obesity-related phenotypes in Roma population.

METHODS

A total of 24 representative single nucleotide polymorphisms (SNPs) were genotyped in 372 individuals belonging to 50 extended families of Roma population. SNPs were tested for association with seven quantitative obesity-related phenotypes in the PLINK program.

RESULTS

Risk variants in NEGR1, FAIM2, FTO, and SH2B1 genes were associated with increased adiposity accumulation in Roma population with effect sizes between 0.21 and 0.34 Z-scores for each copy of the BMI increasing allele. Additionally, variants in BDNF and MC4R were significantly associated with adiposity distribution but not with overall fatness. No significant association was detected between obesity-related phenotypes and variants in the first intron of the FTO gene (e.g., rs9939609).

CONCLUSIONS

The results of this study suggest that SNPs in or near six genes (BDNF, FAIM2, FTO, MC4R, NEGR1, and SH2B1) are significantly associated with body fat accumulation and distribution in Roma people. However, the association observed among variants in the first intron of FTO and obesity in European derived populations is not evident in the analyzed Roma sample.

Linkage and association analyses using families identified a locus affecting an osteoporosis-related trait

Osteoporosis is a common disorder characterized by low bone mass and microarchitectural deterioration of bone tissue, resulting in an increase in bone fragility and in susceptibility to fractures. The genetic basis of osteoporosis is complex and involves multiple genes and environmental factors. Here we introduce a family-based study of the genetics of osteoporosis - the Genetic Analysis of Osteoporosis (GAO) Project - to discover genetic variants affecting osteoporosis-related phenotypes. The GAO Project involved 11 extended families from Barcelona, Spain selected through a proband with osteoporosis (N=367). We performed spine, femur and whole body densitometry for all participants and also analyzed strength and geometrical properties of the hip. Our study focused on 23 densitometric phenotypes that we considered of high clinical relevance and four definitions of low bone mass and fracture status. Pedigree validation was carried out through microsatellite genotyping. The same microsatellites were used to interrogate our data (i) for the replication of previous linkage signals and (ii) for the potential discovery of new linkage signals. The linkage analysis identified one region marked by microsatellite D17S787 showing a strong and significant signal of linkage with femoral shaft cross-sectional moment of inertia (CSMI; LOD=3.18; p=6.5×10(-5)). The chromosomal location marked by microsatellite D17S787 includes several genes, among which two are of particular interest: COL1A1 and SOST, coding for collagen alpha-1 (I) chain and sclerostin, respectively. Follow-up association analysis resulted in only one significant result for rs4792909 from the SOST genomic region (p=0.00248). As a result, we provide strong and significant evidence from both linkage and association analyses that the SOST gene may affect the strength of the femoral shaft. Future investigations should study the relationship between bone mass formation and strength properties of the bones.

Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Genomics of pain in osteoarthritis

Osteoarthritis (OA) accounts for the majority of the disease burden for musculoskeletal disorders and is one of the leading causes of disability worldwide. This disability is the result not of the cartilage loss that defines OA radiographically, but of the chronic pain whose presence defines symptomatic OA. It is becoming clear that many genes, each with a small effect size, contribute to the risk of developing OA. However, the genetics of OA pain are only just starting to be explored. This review will describe the first genes to have been identified in genomic studies of OA pain, as well as the possible dual roles of genes previously identified in genomic studies of OA in the context of pain. Difficulties associated with attempting to characterise the genetics of OA pain will be discussed and promising future avenues of research into genetic and epigenetic factors affecting OA pain described.

Quantitative trait locus linkage analysis in a large Amish pedigree identifies novel candidate loci for erythrocyte traits

We characterized a large Amish pedigree and, in 384 pedigree members, analyzed the genetic variance components with covariate screen as well as genome-wide quantitative trait locus (QTL) linkage analysis of red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet count (PLT), and white blood cell count (WBC) using SOLAR. Age and gender were found to be significant covariates in many CBC traits. We obtained significant heritability estimates for RBC, MCV, MCH, MCHC, RDW, PLT, and WBC. We report four candidate loci with Logarithm of the odds (LOD) scores above 2.0: 6q25 (MCH), 9q33 (WBC), 10p12 (RDW), and 20q13 (MCV). We also report eleven candidate loci with LOD scores between 1.5 and <2.0. Bivariate linkage analysis of MCV and MCH on chromosome 20 resulted in a higher maximum LOD score of 3.14. Linkage signals on chromosomes 4q28, 6p22, 6q25, and 20q13 are concomitant with previously reported QTL. All other linkage signals reported herein represent novel evidence of candidate QTL. Interestingly rs1800562, the most common causal variant of hereditary hemochromatosis in HFE (6p22) was associated with MCH and MCHC in this family. Linkage studies like the one presented here will allow investigators to focus the search for rare variants amidst the noise encountered in the large amounts of data generated by whole-genome sequencing.

Transcriptomics of cortical gray matter thickness decline during normal aging

INTRODUCTION

We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging.

METHODS

Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 μm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability.

RESULTS

Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT.

CONCLUSION

Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation.

Genetic determinants of plasma β₂-glycoprotein I levels: a genome-wide association study in extended pedigrees from Spain

BACKGROUND

β2 -Glycoprotein I (β2 -GPI), also designated apolipoprotein H, is a 50-kDa protein that circulates in blood at high concentrations, playing important roles in autoimmune diseases, hemostasis, atherogenesis, and angiogenesis, as well as in host defense against bacteria and in protein/cellular waste removal. Plasma β2 -GPI levels have a significant genetic component (heritability of ~ 80%).

OBJECTIVES

To present the results of a genome-wide association study for plasma β2 -GPI levels in a set of extended pedigrees from the Genetic Analysis of Idiopathic Thrombophilia (GAIT) Project.

PATIENTS/METHODS

A total of 306 individuals for whom β2 -GPI plasma measurements were available were typed for 307,984 single-nucleotide polymorphisms (SNPs) with the Infinium 317k Beadchip (Illumina). Association with the β2 -GPI phenotype was investigated through variance component analysis, and the most significant results were followed up for association with coronary artery disease (CAD) in an independent in silico analysis involving 5765 CAD cases from the PROCARDIS Project and 7264 controls from the PROCARDIS Project and the Wellcome Trust Case Control Consortium (WTCCC) collection.

RESULTS

After correction for multiple testing, three SNPs located in/around two genes (ELF5 and SCUBE2) reached genome-wide significance. Moreover, an SNP in the APOH gene showed suggestive association with the β2 -GPI phenotype. Some of the identified genes are plausible biological candidates, as they are actually or potentially involved in inflammatory processes.

CONCLUSIONS

Our results represent a first step towards identifying common variants reflecting the genetic architecture influencing plasma β2 -GPI levels, and warrant further validation by functional experiments, as the functions of some of the discovered loci are still unknown.

Osteoarthritis year 2012 in review: genetics and genomics

The field of genetics and genomics is a highly technological driven field that is advancing fast. The purpose of this year in review of genetics and genomics was to highlight the publications that apply these new technologies tools to improve understanding of the pathophysiology of osteoarthritis (OA). In addition, most recent developments in genetics and genomics research and their relevance to OA are discussed in this review.

The role of phenotype in gene discovery in the whole genome sequencing era

As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.

P-selectin Expression Tracks Cerebral Atrophy in Mexican-Americans

Background and Purpose: We hypothesized that the P-selectin (SELP) gene, localized to a region on chromosome 1q24, pleiotropically contributes to increased blood pressure and cerebral atrophy. We tested this hypothesis by performing genetic correlation analyses for 13 mRNA gene expression measures from P-selectin and 11 other genes located in 1q24 region and three magnetic resonance imaging derived indices of cerebral integrity. Methods: The subject pool consisted of 369 (219F; aged 28–85, average = 47.1 ± 12.7 years) normally aging, community-dwelling members of large extended Mexican-American families. Genetic correlation analysis decomposed phenotypic correlation coefficients into genetic and environmental components among 13 leukocyte-based mRNA gene expressions and three whole-brain and regional measurements of cerebral integrity: cortical gray matter thickness, fractional anisotropy of cerebral white matter, and the volume of hyperintensive WM lesions. Results: From the 13 gene expressions, significant phenotypic correlations were only found for the P- and L-selectin expression levels. Increases in P-selectin expression levels tracked with decline in cerebral integrity while the opposite trend was observed for L-selectin expression. The correlations for the P-selectin expression were driven by shared genetic factors, while the correlations with L-selectin expression were due to shared environmental effects. Conclusion: This study demonstrated that P-selectin expression shared a significant variance with measurements of cerebral integrity and posits elevated P-selectin expression levels as a potential risk factor of hypertension-related cerebral atrophy.

High dimensional endophenotype ranking in the search for major depression risk genes

BACKGROUND

Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness.

METHODS

Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees.

RESULTS

Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk.

CONCLUSIONS

The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.

A genome-wide association study of the Protein C anticoagulant pathway

The Protein C anticoagulant pathway regulates blood coagulation by preventing the inadequate formation of thrombi. It has two main plasma components: protein C and protein S. Individuals with protein C or protein S deficiency present a dramatically increased incidence of thromboembolic disorders. Here, we present the results of a genome-wide association study (GWAS) for protein C and protein S plasma levels in a set of extended pedigrees from the Genetic Analysis of Idiopathic Thrombophilia (GAIT) Project. A total number of 397 individuals from 21 families were typed for 307,984 SNPs using the Infinium® 317 k Beadchip (Illumina). Protein C and protein S (free, functional and total) plasma levels were determined with biochemical assays for all participants. Association with phenotypes was investigated through variance component analysis. After correcting for multiple testing, two SNPs for protein C plasma levels (rs867186 and rs8119351) and another two for free protein S plasma levels (rs1413885 and rs1570868) remained significant on a genome-wide level, located in and around the PROCR and the DNAJC6 genomic regions respectively. No SNPs were significantly associated with functional or total protein S plasma levels, although rs1413885 from DNAJC6 showed suggestive association with the functional protein S phenotype, possibly indicating that this locus plays an important role in protein S metabolism. Our results provide evidence that PROCR and DNAJC6 might play a role in protein C and free protein S plasma levels in the population studied, warranting further investigation on the role of these loci in the etiology of venous thromboembolism and other thrombotic diseases.

Genetic regulation of inflammation-mediated activation of haemostasis: family-based approaches in population studies

Blood coagulation and inflammation play a key role in atherosclerosis and thrombosis. Candidate gene and genome wide association studies have identified potential specific genes that might have a causal role in these pathogenic processes. The analysis of quantitative traits is more powerful as they are closer to direct gene action than disease phenotypes. Thus linkage-based studies on extended families might be useful both to estimate the heritability and to map the genetic loci responsible for the regulation of the trait. Family-based studies may estimate high heritability for thrombosis and quantitative traits regarding both platelet aggregation and blood coagulation. Some specific loci relevant to thrombosis have been identified, with some of them showing a direct pleiotropic effect on the risk of thrombosis. Haemostasis factors can be activated by inflammatory stimuli. Fibrinogen level is genetically correlated with C-reactive protein levels with a link for both traits on chromosomes 12 and 21. Genes related to prostanoid biosynthesis, involved both in inflammation and thrombosis, show high heritability levels in both enzyme expression and prostanoid production. Considering that few large family-based linkage studies have as yet been performed on haemostasis and inflammation-related traits, additional studies are highly needed. We are performing a family-based linkage study on large pedigrees (750 subjects from 23 families with juvenile myocardial infarction and 31 control families), to identify genes responsible for quantitative traits involved in the pathway progressively going from inflammation to haemostasis, cell activation, thrombus formation and cardiovascular events.

Genetic analysis of cortical thickness and fractional anisotropy of water diffusion in the brain

OBJECTIVES

The thickness of the brain's cortical gray matter (GM) and the fractional anisotropy (FA) of the cerebral white matter (WM) each follow an inverted U-shape trajectory with age. The two measures are positively correlated and may be modulated by common biological mechanisms. We employed four types of genetic analyses to localize individual genes acting pleiotropically upon these phenotypes.

METHODS

Whole-brain and regional GM thickness and FA values were measured from high-resolution anatomical and diffusion tensor MR images collected from 712, Mexican American participants (438 females, age = 47.9 ± 13.2 years) recruited from 73 (9.7 ± 9.3 individuals/family) large families. The significance of the correlation between two traits was estimated using a bivariate genetic correlation analysis. Localization of chromosomal regions that jointly influenced both traits was performed using whole-genome quantitative trait loci (QTL) analysis. Gene localization was performed using SNP genotyping on Illumina 1M chip and correlation with leukocyte-based gene-expression analyses. The gene-expressions were measured using the Illumina BeadChip. These data were available for 371 subjects.

RESULTS

Significant genetic correlation was observed among GM thickness and FA values. Significant logarithm of odds (LOD ≥ 3.0) QTLs were localized within chromosome 15q22-23. More detailed localization reported no significant association (p < 5·10(-5)) for 1565 SNPs located within the QTLs. Post hoc analysis indicated that 40% of the potentially significant (p ≤ 10(-3)) SNPs were localized to the related orphan receptor alpha (RORA) and NARG2 genes. A potentially significant association was observed for the rs2456930 polymorphism reported as a significant GWAS finding in Alzheimer's disease neuroimaging initiative subjects. The expression levels for RORA and ADAM10 genes were significantly (p < 0.05) correlated with both FA and GM thickness. NARG2 expressions were significantly correlated with GM thickness (p < 0.05) but failed to show a significant correlation (p = 0.09) with FA.

DISCUSSION

This study identified a novel, significant QTL at 15q22-23. SNP correlation with gene-expression analyses indicated that RORA, NARG2, and ADAM10 jointly influence GM thickness and WM-FA values.

Common genetic influences on depression, alcohol, and substance use disorders in Mexican-American families

Multiple genetic and environmental factors influence the risk for both major depression and alcohol/substance use disorders. In addition, there is evidence that these illnesses share genetic factors. Although, the heritability of these illnesses is well established, relatively few studies have focused on ethnic minority populations. Here, we document the prevalence, heritability, and genetic correlations between major depression and alcohol and drug disorders in a large, community-ascertained sample of Mexican-American families. A total of 1,122 Mexican-American individuals from 71 extended pedigrees participated in the study. All subjects received in-person psychiatric interviews. Heritability, genetic, and environmental correlations were estimated using SOLAR. Thirty-five percent of the sample met criteria for DSM-IV lifetime major depression, 34% met lifetime criteria for alcohol use disorders, and 8% met criteria for lifetime drug use disorders. The heritability for major depression was estimated to be h(2) = 0.393 (P = 3.7 × 10(-6)). Heritability estimates were higher for recurrent depression (h(2) = 0.463, P = 4.0 × 10(-6)) and early onset depression (h(2) = 0.485, P = 8.5 × 10(-5)). While the genetic correlation between major depression and alcohol use disorders was significant (ρ(g) = 0.58, P = 7 × 10(-3)), the environmental correlation between these traits was not significant. Although, there is evidence for increased rates of depression and substance use in US-born individuals of Mexican ancestry, our findings indicate that genetic control over major depression and alcohol/substance use disorders in the Mexican-American population is similar to that reported in other populations.

Blood pressure and cerebral white matter share common genetic factors in Mexican Americans

Elevated arterial pulse pressure and blood pressure (BP) can lead to atrophy of cerebral white matter (WM), potentially attributable to shared genetic factors. We calculated the magnitude of shared genetic variance between BP and fractional anisotropy of water diffusion, a sensitive measurement of WM integrity in a well-characterized population of Mexican Americans. The patterns of whole-brain and regional genetic overlap between BP and fractional anisotropy were interpreted in the context the pulse-wave encephalopathy theory. We also tested whether regional pattern in genetic pleiotropy is modulated by the phylogeny of WM development. BP and high-resolution (1.7 × 1.7 × 3 mm; 55 directions) diffusion tensor imaging data were analyzed for 332 (202 females; mean age 47.9 ± 13.3 years) members of the San Antonio Family Heart Study. Bivariate genetic correlation analysis was used to calculate the genetic overlap between several BP measurements (pulse pressure, systolic BP, and diastolic BP) and fractional anisotropy (whole-brain and regional values). Intersubject variance in pulse pressure and systolic BP exhibited a significant genetic overlap with variance in whole-brain fractional anisotropy values, sharing 36% and 22% of genetic variance, respectively. Regionally, shared genetic variance was significantly influenced by rates of WM development (r=-0.75; P=0.01). The pattern of genetic overlap between BP and WM integrity was generally in agreement with the pulse-wave encephalopathy theory. Our study provides evidence that a set of pleiotropically acting genetic factors jointly influence phenotypic variation in BP and WM integrity. The magnitude of this overlap appears to be influenced by phylogeny of WM development, suggesting a possible role for genotype-by-age interactions.

Genome-wide linkage in Utah autism pedigrees

Genetic studies of autism over the past decade suggest a complex landscape of multiple genes. In the face of this heterogeneity, studies that include large extended pedigrees may offer valuable insights, as the relatively few susceptibility genes within single large families may be more easily discerned. This genome-wide screen of 70 families includes 20 large extended pedigrees of 6-9 generations, 6 moderate-sized families of 4-5 generations and 44 smaller families of 2-3 generations. The Center for Inherited Disease Research (CIDR) provided genotyping using the Illumina Linkage Panel 12, a 6K single-nucleotide polymorphism (SNP) platform. Results from 192 subjects with an autism spectrum disorder (ASD) and 461 of their relatives revealed genome-wide significance on chromosome 15q, with three possibly distinct peaks: 15q13.1-q14 (heterogeneity LOD (HLOD)=4.09 at 29 459 872 bp); 15q14-q21.1 (HLOD=3.59 at 36 837 208 bp); and 15q21.1-q22.2 (HLOD=5.31 at 55 629 733 bp). Two of these peaks replicate earlier findings. There were additional suggestive results on chromosomes 2p25.3-p24.1 (HLOD=1.87), 7q31.31-q32.3 (HLOD=1.97) and 13q12.11-q12.3 (HLOD=1.93). Affected subjects in families supporting the linkage peaks found in this study did not reveal strong evidence for distinct phenotypic subgroups.

Whole brain and regional hyperintense white matter volume and blood pressure: overlap of genetic loci produced by bivariate, whole-genome linkage analyses

BACKGROUND AND PURPOSE

The volume of T2-hyperintense white matter (HWM) is an important neuroimaging marker of cerebral integrity with a demonstrated high heritability. Pathophysiology studies have shown that the regional, ependymal, and subcortical HWM lesions are associated with elevated arterial pulse pressure and arterial blood pressure (BP), respectively. We performed bivariate, whole-genome linkage analyses for HWM volumes and BP measurements to identify chromosomal regions that contribute jointly to both traits in a population of healthy Mexican Americans. Our aims were to localize novel quantitative trait loci acting pleiotropically on these phenotypes and to replicate previous genetic findings on whole brain HWM volume and BP measurements.

METHODS

BP measurements and volumes of whole-brain (WB), subcortical, and ependymal HWM lesions, measured from high-resolution (1 mm(3)) 3-dimensional fluid-attenuated inversion recovery images, served as focal quantitative phenotypes. Data were collected from 357 (218 females; mean age=47.9±13.2 years) members of large extended families who participated in the San Antonio Family Heart Study.

RESULTS

Bivariate genomewide linkage analyses localized a significant quantitative trait locus influencing WB and regional (ependymal) HWM volumes and pulse pressure and systolic BP to chromosomal location 1q24 between markers D1S196 and D1S1619. Several other chromosomal regions (1q42, 10q24-q26, and 15q26) exhibited suggestive linkages. The results of the post hoc analyses that excluded 55 subjects taking antihypertensive medication showed no substantive differences from the results obtained in the full cohort.

CONCLUSIONS

This study confirms several previously observed quantitative trait loci influencing BP and cerebral integrity and identifies a novel significant quantitative trait locus at chromosome 1q24. The genetic results strongly support a role for pleiotropically acting genes jointly influencing BP and cerebral white matter integrity.

Variance component methods for analysis of complex phenotypes

Variance component methods have a long history in human quantitative genetics as well as in agricultural genetics and animal breeding. They are designed for genetic analysis of continuously varying quantitative traits such as body mass index (BMI), cholesterol levels, or intelligence quotient. They can be used to assess the strength of genetic effects on a trait, to localize genes influencing a trait through either linkage or association methods, to assess whether associated variants are likely to be the functional variants behind a given localization signal, to explore whether related traits have shared genetic influences in multivariate analyses, and to characterize the genetic effects on a trait through analyses of gene-gene and gene-environment interactions.

Genome-wide linkage using the Social Responsiveness Scale in Utah autism pedigrees

BACKGROUND

Autism Spectrum Disorders (ASD) are phenotypically heterogeneous, characterized by impairments in the development of communication and social behaviour and the presence of repetitive behaviour and restricted interests. Dissecting the genetic complexity of ASD may require phenotypic data reflecting more detail than is offered by a categorical clinical diagnosis. Such data are available from the Social Responsiveness Scale (SRS) which is a continuous, quantitative measure of social ability giving scores that range from significant impairment to above average ability.

METHODS

We present genome-wide results for 64 multiplex and extended families ranging from two to nine generations. SRS scores were available from 518 genotyped pedigree subjects, including affected and unaffected relatives. Genotypes from the Illumina 6 k single nucleotide polymorphism panel were provided by the Center for Inherited Disease Research. Quantitative and qualitative analyses were done using MCLINK, a software package that uses Markov chain Monte Carlo (MCMC) methods to perform multilocus linkage analysis on large extended pedigrees.

RESULTS

When analysed as a qualitative trait, linkage occurred in the same locations as in our previous affected-only genome scan of these families, with findings on chromosomes 7q31.1-q32.3 [heterogeneity logarithm of the odds (HLOD) = 2.91], 15q13.3 (HLOD = 3.64), and 13q12.3 (HLOD = 2.23). Additional positive qualitative results were seen on chromosomes 6 and 10 in regions that may be of interest for other neuropsychiatric disorders. When analysed as a quantitative trait, results replicated a peak found in an independent sample using quantitative SRS scores on chromosome 11p15.1-p15.4 (HLOD = 2.77). Additional positive quantitative results were seen on chromosomes 7, 9, and 19.

CONCLUSIONS

The SRS linkage peaks reported here substantially overlap with peaks found in our previous affected-only genome scan of clinical diagnosis. In addition, we replicated a previous SRS peak in an independent sample. These results suggest the SRS is a robust and useful phenotype measure for genetic linkage studies of ASD. Finally, analyses of SRS scores revealed linkage peaks overlapping with evidence from other studies of neuropsychiatric diseases. The information available from the SRS itself may, therefore, reveal locations for autism susceptibility genes that would not otherwise be detected.

Genetic control over the resting brain

The default-mode network, a coherent resting-state brain network, is thought to characterize basal neural activity. Aberrant default-mode connectivity has been reported in a host of neurological and psychiatric illnesses and in persons at genetic risk for such illnesses. Whereas the neurophysiologic mechanisms that regulate default-mode connectivity are unclear, there is growing evidence that genetic factors play a role. In this report, we estimate the importance of genetic effects on the default-mode network by examining covariation patterns in functional connectivity among 333 individuals from 29 randomly selected extended pedigrees. Heritability for default-mode functional connectivity was 0.424 +/- 0.17 (P = 0.0046). Although neuroanatomic variation in this network was also heritable, the genetic factors that influence default-mode functional connectivity and gray-matter density seem to be distinct, suggesting that unique genes influence the structure and function of the network. In contrast, significant genetic correlations between regions within the network provide evidence that the same genetic factors contribute to variation in functional connectivity throughout the default mode. Specifically, the left parahippocampal region was genetically correlated with all other network regions. In addition, the posterior cingulate/precuneus region, medial prefrontal cortex, and right cerebellum seem to form a subnetwork. Default-mode functional connectivity is influenced by genetic factors that cannot be attributed to anatomic variation or a single region within the network. By establishing the heritability of default-mode functional connectivity, this experiment provides the obligatory evidence required before these measures can be considered as endophenotypes for psychiatric or neurological illnesses or to identify genes influencing intrinsic brain function.

Neurocognitive endophenotypes for bipolar disorder identified in multiplex multigenerational families

CONTEXT

Although genetic influences on bipolar disorder are well established, localization of genes that predispose to the illness has proven difficult. Given that genes predisposing to bipolar disorder may be transmitted without expression of the categorical clinical phenotype, a strategy for identifying risk genes is to identify and map quantitative intermediate phenotypes or endophenotypes.

OBJECTIVE

To adjudicate neurocognitive endophenotypes for bipolar disorder.

DESIGN

All participants underwent diagnostic interviews and comprehensive neurocognitive evaluations. Neurocognitive measures found to be heritable were entered into analyses designed to determine which test results are impaired in affected individuals, are sensitive to the genetic liability for the illness, and are genetically correlated with affection status.

SETTING

Central valley of Costa Rica; Mexico City, Mexico; and San Antonio, Texas.

PARTICIPANTS

Seven hundred nine Latino individuals participated in the study. Of these, 660 were members of extended pedigrees with at least 2 siblings diagnosed as having bipolar disorder (n = 230). The remaining subjects were community control subjects drawn from each site who did not have a personal or family history of bipolar disorder or schizophrenia.

MAIN OUTCOME MEASURE

Neurocognitive test performance.

RESULTS

Two of the 22 neurocognitive variables were not significantly heritable and were excluded from subsequent analyses. Patients with bipolar disorder were impaired on 6 cognitive measures compared with nonrelated healthy controls. Nonbipolar first-degree relatives were impaired on 5 of these, and the following 3 tests were genetically correlated with affection status: Digit Symbol Coding Task, Object Delayed Response Task, and immediate facial memory.

CONCLUSION

This large-scale extended pedigree study of cognitive functioning in bipolar disorder identifies measures of processing speed, working memory, and declarative (facial) memory as candidate endophenotypes for bipolar disorder.

Use of genetic variation as biomarkers for mild cognitive impairment and progression of mild cognitive impairment to dementia

Cognitive impairment is prevalent in the elderly. The high estimates of conversion to dementia have spurred the interest in identification of genetic risk factors associated with development of cognitive impairment and or its progression. However, despite notable achievements in human genetics over the years, in particular technological advances in gene mapping and in statistical methods that relate genetic variants to disease, to date only a small proportion of the genetic contribution to late-life cognitive impairment can be explained. A likely explanation for the difficulty in gene identification is that it is a multifactorial disorder with both genetic and environmental components, in which several genes with small effects each are likely to contribute to the quantitative traits associated with the disease. The motivation for identifying the underlying genetic risk factors elderly is clear. Not only could it shed light on disease pathogenesis, but it may also provide potential targets for effective treatment, screening, and prevention. In this article we review the current knowledge on underlying genetic variants and the usefulness of genetic variation as diagnostic tools and biomarkers. In addition, we discuss the potentials and difficulties researchers face in designing appropriate studies for gene discovery.

Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

Endophenotypes in normal brain morphology and Alzheimer's disease: a review

Late-onset Alzheimer's disease is a common complex disorder of old age. Though these types of disorders can be highly heritable, they differ from single-gene (Mendelian) diseases in that their causes are often multifactorial with both genetic and environmental components. Genetic risk factors that have been firmly implicated in the cause are mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes, which are found in large multi-generational families with an autosomal dominant pattern of disease inheritance, the apolipoprotein E (APOE)epsilon4 allele and the sortilin-related receptor (SORL1) gene. Environmental factors that have been associated with late-onset Alzheimer's disease include depressive illness, various vascular risk factors, level of education, head trauma and estrogen replacement therapy. This complexity may help explain their high prevalence from an evolutionary perspective, but the etiologic complexity makes identification of disease-related genes much more difficult. The "endophenotype" approach is an alternative method for measuring phenotypic variation that may facilitate the identification of susceptibility genes for complexly inherited traits. The usefulness of endophenotypes in genetic analyses of normal brain morphology and, in particular for Alzheimer's disease will be reviewed as will the implications of these findings for models of disease causation. Given that the pathways from genotypes to end-stage phenotypes are circuitous at best, identifying endophenotypes more proximal to the effects of genetic variation may expedite the attempts to link genetic variants to disorders.

Use of genetic variation as biomarkers for Alzheimer's disease

Late-onset Alzheimer's disease (LOAD) is the most common cause of late-onset dementia in western societies. Despite remarkable achievements in human genetics throughout the years, in particular technological advances in gene mapping and in statistical methods that relate genetic variants to disease, to date only a small proportion of the genetic contribution to LOAD can be explained leaving several remaining genetic risk factors to be identified. A possible explanation for the difficulty in gene identification is that LOAD is a multifactorial complex disorder with both genetic and environmental components. Multiple genes with small effects each ("quantitative trait loci"[QTLs]) are likely to contribute to the quantitative traits associated with the disease, such as memory performance, amyloid/tau pathology, or hippocampal atrophy. The motivation for identifying the genetics of LOAD is clear. Not only could it shed light on disease pathogenesis, but it may also provide potential targets for effective treatment, screening, and prevention. Here, we review the usefulness of genetic variation as diagnostic tools and biomarkers in LOAD and discuss the potentials and difficulties researchers face in designing appropriate studies for gene discovery.

Determinants of variation in human serum paraoxonase activity

Paraoxonase-1 (PON1) is associated with high-density lipoprotein (HDL) particles and is believed to contribute to antiatherogenic properties of HDLs. We assessed the determinants of PON1 activity variation using different substrates of the enzyme. PON1 activity in serum samples from 922 participants in the San Antonio Family Heart Study was assayed using a reliable microplate format with three substrates: paraoxon, phenyl acetate and the lactone dihydrocoumarin. There were major differences among results from the three substrates in degree of effect by various environmental and genetic factors, suggesting that knowledge of one substrate activity alone may not provide a complete sense of PON1 metabolism. Three significant demographic covariates (age, smoking status and contraceptive usage) together explained 1-6% of phenotypic variance, whereas four metabolic covariates representing lipoprotein metabolism (apoAII, apoAI, triglycerides and non-HDL cholesterol) explained 4-19%. Genes explained 65-92% of phenotypic variance and the dominant genetic effect was exerted by a locus mapping at or near the protein structural locus (PON1) on chromosome 7. Additional genes influencing PON1 activity were localized to chromosomes 3 and 14. Our study identified environmental and genetic determinants of PON1 activity that accounted for 88-97% of total phenotypic variance, suggesting that few, if any, major biological determinants are unrepresented in the models.

Human genetics of osteoporosis

A family history of hip fracture carries a twofold increased risk of fracture among descendants. Genetic factors indeed play a major role in the determination of bone mineral density (BMD) and osteoporosis risk. Multiple chromosomal loci have been mapped by linkage approaches which potentially carry hundreds of genes involved in the determination of bone mass and quality. Association studies based on candidate gene polymorphisms and subsequent meta-analyses, and the more recent genome-wide association studies (GWAS), have clearly identified a handful of genes associated with BMD and/or fragility fractures. Among them are genes coding for the LDL-receptor related protein 5 (LRP5), estrogen receptor alpha (ESR1) and osteoprotegerin, OPG (TNFRSf11b). However, the percentage of osteoporosis risk explained by any of these polymorphisms is small, indicating that most genetic risk factors remain to be discovered and/or that interaction with environmental factors needs further consideration.

Human QTL linkage mapping

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10-15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.

Contribution of gender-specific genetic factors to osteoporosis risk

Common diseases result from the complex relationship between genetic and environmental factors. The aim of this review is to provide perspective for a conceptual framework aimed at studying the interplay of gender-specific genetic and environmental factors in the etiology of complex disease, using osteoporosis as an example. In recent years, gender differences in the heritability of the osteoporosis-related phenotypes have been reported and sex-specific quantitative-trait loci were discovered by linkage studies in humans and mice. Results of numerous allelic association studies also differed by gender. In most cases, it was not clear whether or not this phenomenon should be attributed to the effect of sex-chromosomes, sex hormones, or other intrinsic or extrinsic differences between the genders, such as the level of bioavailable estrogen and of physical activity. We conclude that there is need to consider gender-specific genetic and environmental factors in the planning of future association studies on the etiology of osteoporosis and other complex diseases prevalent in the general population.

Contemporary model-free methods for linkage analysis

Model-free linkage methods are based on identifying regions of the genome in which patterns of allele sharing among family members correspond to patterns of phenotype correlation among family members. Two general classes of model-free linkage methods are discussed in this chapter, relative pair methods designed primarily for analysis of discrete traits and variance component methods designed primarily for analysis of quantitative traits. These methods have been used to identify numerous genes influencing complex human phenotypes and remain viable approaches to gene localization in the twenty-first century.