Common variants at 6q22 and 17q21 are associated with intracranial volume

Exploring the Genetic Patterns of Complex Diseases via the Integrative Genome-Wide Approach

Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However, most of the variants that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. This is the so-called missing heritability problem. Evidence has indicated that many complex diseases are genetically related, meaning these diseases share common genetic risk variants. Therefore, exploring the genetic correlations across multiple related studies could be a promising strategy for removing spurious associations and identifying underlying genetic risk variants, and thereby uncovering the mystery of missing heritability in complex diseases. We present a general and robust method to identify genetic patterns from multiple large-scale genomic datasets. We treat the summary statistics as a matrix and demonstrate that genetic patterns will form a low-rank matrix plus a sparse component. Hence, we formulate the problem as a matrix recovering problem, where we aim to discover risk variants shared by multiple diseases/traits and those for each individual disease/trait. We propose a convex formulation for matrix recovery and an efficient algorithm to solve the problem. We demonstrate the advantages of our method using both synthesized datasets and real datasets. The experimental results show that our method can successfully reconstruct both the shared and the individual genetic patterns from summary statistics and achieve comparable performances compared with alternative methods under a wide range of scenarios. The MATLAB code is available at: http://www.comp.hkbu.edu.hk/~xwan/iga.zip.

Brain reserve against physical disability progression over 5 years in multiple sclerosis

OBJECTIVE

The brain reserve hypothesis links larger maximal lifetime brain growth (MLBG, estimated with intracranial volume [ICV]) with lower risk for cognitive decline/dementia. We examined whether larger MLBG is also linked to less physical disability progression over 5 years in a prospective sample of treatment-naive patients with multiple sclerosis (MS).

METHODS

Physical disability was measured with the Expanded Disability Status Scale (EDSS) at baseline and 5-year follow-up in 52 treatment-naive Serbian patients with MS. MRI measured disease burden (cerebral atrophy, T2 lesion volume) and MLBG: a genetically determined, premorbid (established during adolescence, stable thereafter) patient characteristic estimated with ICV (adjusted for sex). Logistic regression tested whether MLBG (smaller vs larger) predicts disability progression (stable vs worsened) independently of disease burden.

RESULTS

Disability progression was observed in 29 (55.8%) patients. Larger MLBG predicted lower risk for progression (odds ratio 0.13, 95% confidence interval 0.02-0.78), independently of disease burden. We also calculated absolute change in EDSS scores, and observed that patients with smaller MLBG showed worse EDSS change (0.91 ± 0.71) than patients with larger MLBG (0.42 ± 0.87).

CONCLUSIONS

Larger MLBG was linked to lower risk for disability progression in patients with MS over 5 years, which is the first extension of the brain reserve hypothesis to physical disability. MLBG (ICV) represents a clinically available metric that may help gauge risk for future disability in patients with MS, which may advance the science and practice of early intervention. Potential avenues for future research are discussed.

Heritability and reliability of automatically segmented human hippocampal formation subregions

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.

Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations

BACKGROUND

Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles.

FINDINGS

A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders.

CONCLUSIONS

We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.

Adaptive evolution of interleukin-3 (IL3), a gene associated with brain volume variation in general human populations

Greatly expanded brain volume is one of the most characteristic traits that distinguish humans from other primates. Recent studies have revealed genes responsible for the dramatically enlarged human brain size (i.e., the microcephaly genes), and it has been well documented that many microcephaly genes have undergone accelerated evolution along the human lineage. In addition to being far larger than other primates, human brain volume is also highly variable in general populations. However, the genetic basis underlying human brain volume variation remains elusive and it is not known whether genes regulating human brain volume variation also have experienced positive selection. We have previously shown that genetic variants (near the IL3 gene) on 5q33 were significantly associated with brain volume in Chinese population. Here, we provide further evidence that support the significant association of genetic variants on 5q33 with brain volume. Bioinformatic analyses suggested that rs31480 is likely to be the causal variant among the studied SNPs. Molecular evolutionary analyses suggested that IL3 might have undergone positive selection in primates and humans. Neutrality tests further revealed signatures of positive selection of IL3 in Han Chinese and Europeans. Finally, extended haplotype homozygosity (EHH) and relative EHH analyses showed that the C allele of SNP rs31480 might have experienced recent positive selection in Han Chinese. Our results suggest that IL3 is an important genetic regulator for human brain volume variation and implied that IL3 might have experienced weak or modest positive selection in the evolutionary history of humans, which may be due to its contribution to human brain volume.

A novel Alzheimer disease locus located near the gene encoding tau protein

APOE ɛ4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ɛ4+ (10 352 cases and 9207 controls) and APOE ɛ4- (7184 cases and 26 968 controls) subgroups as well as in the total sample testing for interaction between a single-nucleotide polymorphism (SNP) and APOE ɛ4 status. Suggestive associations (P<1 × 10(-4)) in stage 1 were evaluated in an independent sample (stage 2) containing 4203 subjects (APOE ɛ4+: 1250 cases and 536 controls; APOE ɛ4-: 718 cases and 1699 controls). Among APOE ɛ4- subjects, novel genome-wide significant (GWS) association was observed with 17 SNPs (all between KANSL1 and LRRC37A on chromosome 17 near MAPT) in a meta-analysis of the stage 1 and stage 2 data sets (best SNP, rs2732703, P=5·8 × 10(-9)). Conditional analysis revealed that rs2732703 accounted for association signals in the entire 100-kilobase region that includes MAPT. Except for previously identified AD loci showing stronger association in APOE ɛ4+ subjects (CR1 and CLU) or APOE ɛ4- subjects (MS4A6A/MS4A4A/MS4A6E), no other SNPs were significantly associated with AD in a specific APOE genotype subgroup. In addition, the finding in the stage 1 sample that AD risk is significantly influenced by the interaction of APOE with rs1595014 in TMEM106B (P=1·6 × 10(-7)) is noteworthy, because TMEM106B variants have previously been associated with risk of frontotemporal dementia. Expression quantitative trait locus analysis revealed that rs113986870, one of the GWS SNPs near rs2732703, is significantly associated with four KANSL1 probes that target transcription of the first translated exon and an untranslated exon in hippocampus (P ⩽ 1.3 × 10(-8)), frontal cortex (P ⩽ 1.3 × 10(-9)) and temporal cortex (P⩽1.2 × 10(-11)). Rs113986870 is also strongly associated with a MAPT probe that targets transcription of alternatively spliced exon 3 in frontal cortex (P=9.2 × 10(-6)) and temporal cortex (P=2.6 × 10(-6)). Our APOE-stratified GWAS is the first to show GWS association for AD with SNPs in the chromosome 17q21.31 region. Replication of this finding in independent samples is needed to verify that SNPs in this region have significantly stronger effects on AD risk in persons lacking APOE ɛ4 compared with persons carrying this allele, and if this is found to hold, further examination of this region and studies aimed at deciphering the mechanism(s) are warranted.

The Rotterdam Scan Study: design update 2016 and main findings

Imaging plays an essential role in research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to elucidate the causes of neurological disease by performing imaging of the brain in a prospective population-based setting. Initially, in 1995 and 1999, random subsamples of participants from the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. Moreover, we describe the imaging protocol, image post-processing techniques, and the main findings to date. Finally, we provide recommendations for future research, which will also be topics of investigation in the Rotterdam Scan Study.

Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities

Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115-8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.

Comparative reliability analysis of publicly available software packages for automatic intracranial volume estimation

Intracranial volume is an important measure in brain research often used as a correction factor in inter subject studies. The current study investigates the resulting outcome in terms of the type of software used for automatically estimating ICV measure. Five groups of 70 subjects are considered, including adult controls (AC) (n=11), adult with dementia (AD) (n=11), pediatric controls (PC) (n=18) and two groups of pediatric epilepsy subjects (PE1.5 and PE3) (n=30) using 1.5 T and 3T scanners, respectively. Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. Four publicly available software packages (AFNI, Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the five groups. Linear regression analyses suggest that reference measurement discrepancy could be explained best by SPM [R(2)= 0.67;p <; 0.01] for the AC group, Freesurfer [R(2) = 0.46; p = 0.02] for the AD group, AFNI [R(2)=0.97;p<; 0.01] for the PC group and FSL [R(2) = 0.6; p = 0.1] for the PE1.5 and [R(2) = 0.6; p <; 0.01] for PE3 groups. The study demonstrates that the choice of the automated software for ICV estimation is dependent on the population under consideration and whether the software used is atlas-based or not.

The Rotterdam Study: 2016 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Genomic structural variants are linked with intellectual disability

Mutations in more than 500 genes have been associated with intellectual disability (ID) and related disorders of cognitive function, such as autism and schizophrenia. Here we aimed to unravel the molecular epidemiology of non-specific ID in a genetic isolate using a combination of population and molecular genetic approaches. A large multigenerational pedigree was ascertained within a Dagestan Genetic Heritage research program in a genetic isolate of indigenous ethnics. Clinical characteristics of the affected members were based on combining diagnoses from regional psychiatric hospitals with our own clinical assessment, using a Russian translation of the structured psychiatric interviews, the Diagnostic Interview for Genetic Studies and the Family Interview for Genetic Studies, based on DSM-IV criteria. Weber/CHLC 9.0 STRs set was used for multipoint parametric linkage analyses (Simwalk2.91). Next, we checked CNVs and LOH (based on Affymetrix SNP 5.0 data) in the linked with ID genomic regions with the aim to identify candidate genes associated with mutations in linked regions. The number of statistically significant (p ≤ 0.05) suggestive linkage peaks with 1.3 < LOD < 3.0 we detected in a total of 10 genomic regions: 1q41, 2p25.3-p24.2, 3p13-p12.1, 4q13.3, 10p11, 11q23, 12q24.22-q24.31, 17q24.2-q25.1, 21q22.13 and 22q12.3-q13.1. Three significant linkage signals with LOD >3 were obtained at 2p25.3-p24.2 under the dominant model, with a peak at 21 cM flanked by loci D2S2976 and D2S2952; at 12q24.22-q24.31 under the recessive model, with a peak at -120 cM flanked by marker D12S2070 and D12S395 and at 22q12.3 under the dominant model, with a peak at 32 cM flanked by marker D22S683 and D22S445. After a set of genes had been designated as possible candidates in these specific chromosomal regions,we conducted an exploratory search for LOH and CNV based on microarray data to detect structural genomic variants within five ID-linked regions with LOD scores between 2.0 and 3.9. In these selected regions we obtained 173 ROH segments and 98 CN segments. Further analysis of region 2p25.3-p24.2 revealed deletions within genes encoding MYTL, SNTG2 and TPO among five of 21 affected cases at 2p25.3-p24.2. In the ID-linked region at 12q24.22-12q24.31 19 out of 21 ID cases carried segmental CNV and 20 of 21 them displayed ROH segments with mean size lengths for ID cases 2512 kb (500-6,472 kb) and for healthy control 682 kb (531-986 kb), including the genes MED13L, HRK, FBXW8, TESC, CDK2AP1 and SBNO1. Seven of 21 affected pedigree members displayed segmental deletions at 22q12.3 that includes the gene LARGE. Eight affected pedigree members carried ROH segments and 6 CN segments at 10p11.23-p11.21 containing the genes ZEB1, c10orf68 and EPC1. Our linkage and structural genomic variation analyses in a remote highland genetic isolate with aggregation of ID demonstrated that even highly isolated single kindred ID has oligo/polygenic pathogenesis. The results obtained implicate 10 genomic regions linked with ID that contain some of previously reported candidate genes, including HRK, FBXW8, TESC, CDK2AP1 and SBNO1 at 12q24 that were shown in recent studies as associated with brain measures derived from MRI scans.

Reciprocal Relationship between Head Size, an Autism Endophenotype, and Gene Dosage at 19p13.12 Points to AKAP8 and AKAP8L

Microcephaly and macrocephaly are overrepresented in individuals with autism and are thought to be disease-related risk factors or endophenotypes. Analysis of DNA microarray results from a family with a low functioning autistic child determined that the proband and two additional unaffected family members who carry a rare inherited 760 kb duplication of unknown clinical significance at 19p13.12 are macrocephalic. Consideration alongside overlapping deletion and duplication events in the literature provides support for a strong relationship between gene dosage at this locus and head size, with losses and gains associated with microcephaly (p=1.11x10(-11)) and macrocephaly (p=2.47x10(-11)), respectively. Data support A kinase anchor protein 8 and 8-like (AKAP8 and AKAP8L) as candidate genes involved in regulation of head growth, an interesting finding given previous work implicating the AKAP gene family in autism. Towards determination of which of AKAP8 and AKAP8L may be involved in the modulation of head size and risk for disease, we analyzed exome sequencing data for 693 autism families (2591 individuals) where head circumference data were available. No predicted loss of function variants were observed, precluding insights into relationship to head size, but highlighting strong evolutionary conservation. Taken together, findings support the idea that gene dosage at 19p13.12, and AKAP8 and/or AKAP8L in particular, play an important role in modulation of head size and may contribute to autism risk. Exome sequencing of the family also identified a rare inherited variant predicted to disrupt splicing of TPTE / PTEN2, a PTEN homologue, which may likewise contribute to both macrocephaly and autism risk.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease

Background

Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure.

Methods

Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups.

Results

Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study.

Conclusions

This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

A novel common variant in DCST2 is associated with length in early life and height in adulthood

Common genetic variants have been identified for adult height, but not much is known about the genetics of skeletal growth in early life. To identify common genetic variants that influence fetal skeletal growth, we meta-analyzed 22 genome-wide association studies (Stage 1; N = 28 459). We identified seven independent top single nucleotide polymorphisms (SNPs) (P < 1 × 10(-6)) for birth length, of which three were novel and four were in or near loci known to be associated with adult height (LCORL, PTCH1, GPR126 and HMGA2). The three novel SNPs were followed-up in nine replication studies (Stage 2; N = 11 995), with rs905938 in DC-STAMP domain containing 2 (DCST2) genome-wide significantly associated with birth length in a joint analysis (Stages 1 + 2; β = 0.046, SE = 0.008, P = 2.46 × 10(-8), explained variance = 0.05%). Rs905938 was also associated with infant length (N = 28 228; P = 5.54 × 10(-4)) and adult height (N = 127 513; P = 1.45 × 10(-5)). DCST2 is a DC-STAMP-like protein family member and DC-STAMP is an osteoclast cell-fusion regulator. Polygenic scores based on 180 SNPs previously associated with human adult stature explained 0.13% of variance in birth length. The same SNPs explained 2.95% of the variance of infant length. Of the 180 known adult height loci, 11 were genome-wide significantly associated with infant length (SF3B4, LCORL, SPAG17, C6orf173, PTCH1, GDF5, ZNFX1, HHIP, ACAN, HLA locus and HMGA2). This study highlights that common variation in DCST2 influences variation in early growth and adult height.

Common genetic variants influence human subcortical brain structures

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Heritability of subcortical and limbic brain volume and shape in multiplex-multigenerational families with schizophrenia

BACKGROUND

Brain abnormalities of subcortical and limbic nuclei are common in patients with schizophrenia, and variation in these structures is considered a putative endophenotype for the disorder. Multiplex-multigenerational families with schizophrenia provide an opportunity to investigate the impact of shared genetic ancestry, but these families have not been previously examined to study structural brain abnormalities. We estimate the heritability of subcortical and hippocampal brain volumes in multiplex-multigenerational families and the heritability of subregions using advanced shape analysis.

METHODS

The study comprised 439 participants from two sites who underwent 3T structural magnetic resonance imaging. The participants included 190 European-Americans from 32 multiplex-multigenerational families with schizophrenia and 249 healthy comparison subjects. Subcortical and hippocampal volume and shape were measured in 14 brain structures. Heritability was estimated for volume and shape.

RESULTS

Volume and shape were heritable in families. Estimates of heritability in subcortical and limbic volumes ranged from .45 in the right hippocampus to .84 in the left putamen. The shape of these structures was heritable (range, .40-.49), and specific subregional shape estimates of heritability tended to exceed heritability estimates of volume alone.

CONCLUSIONS

These results demonstrate that volume and shape of subcortical and limbic brain structures are potential endophenotypic markers in schizophrenia. The specificity obtained using shape analysis may improve selection of imaging phenotypes that better reflect the underlying neurobiology. Our findings can aid in the identification of specific genetic targets that affect brain structure and function in schizophrenia.

Association of Alzheimer's disease GWAS loci with MRI markers of brain aging

Whether novel risk variants of Alzheimer's disease (AD) identified through genome-wide association studies also influence magnetic resonance imaging-based intermediate phenotypes of AD in the general population is unclear. We studied association of 24 AD risk loci with intracranial volume, total brain volume, hippocampal volume (HV), white matter hyperintensity burden, and brain infarcts in a meta-analysis of genetic association studies from large population-based samples (N = 8175-11,550). In single-SNP based tests, AD risk allele of APOE (rs2075650) was associated with smaller HV (p = 0.0054) and CD33 (rs3865444) with smaller intracranial volume (p = 0.0058). In gene-based tests, there was associations of HLA-DRB1 with total brain volume (p = 0.0006) and BIN1 with HV (p = 0.00089). A weighted AD genetic risk score was associated with smaller HV (beta ± SE = -0.047 ± 0.013, p = 0.00041), even after excluding the APOE locus (p = 0.029). However, only association of AD genetic risk score with HV, including APOE, was significant after multiple testing correction (including number of independent phenotypes tested). These results suggest that novel AD genetic risk variants may contribute to structural brain aging in nondemented older community persons.

The Generation R Study: Biobank update 2015

The Generation R Study is a population-based prospective cohort study from fetal life until adulthood. The study is designed to identify early environmental and genetic causes and causal pathways leading to normal and abnormal growth, development and health from fetal life, childhood and young adulthood. In total, 9,778 mothers were enrolled in the study. Data collection in children and their parents include questionnaires, interviews, detailed physical and ultrasound examinations, behavioural observations, Magnetic Resonance Imaging and biological samples. Efforts have been conducted for collecting biological samples including blood, hair, faeces, nasal swabs, saliva and urine samples and generating genomics data on DNA, RNA and microbiome. In this paper, we give an update of the collection, processing and storage of these biological samples and available measures. Together with detailed phenotype measurements, these biological samples provide a unique resource for epidemiological studies focused on environmental exposures, genetic and genomic determinants and their interactions in relation to growth, health and development from fetal life onwards.

Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease

Intermediate phenotypes are traits positioned somewhere between genetic variation and disease. They represent a target for attempts to find disease-associated genetic variants and elucidation of mechanisms. Psychiatry has been particularly enamoured with intermediate phenotypes, due to uncertainty about disease aetiology, inconclusive results in early psychiatric genetic studies, and their appeal relative to traditional diagnostic categories. In this review, we argue that new genetic findings are relevant to the question of the utility of these constructs. In particular, results from genome-wide association studies of psychiatric disorders now allow an assessment of the potential role of particular intermediate phenotypes. Based on such an analysis, as well as other recent results, we conclude that intermediate phenotypes are likely to be most valuable in understanding mechanism.

Genetic architecture of subcortical brain regions: common and region-specific genetic contributions

Understanding the aetiology of patterns of variation within and covariation across brain regions is key to advancing our understanding of the functional, anatomical and developmental networks of the brain. Here we applied multivariate twin modelling and principal component analysis (PCA) to investigate the genetic architecture of the size of seven subcortical regions (caudate nucleus, thalamus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens) in a genetically informative sample of adolescents and young adults (N = 1038; mean age = 21.6 ± 3.2 years; including 148 monozygotic and 202 dizygotic twin pairs) from the Queensland Twin IMaging (QTIM) study. Our multivariate twin modelling identified a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In addition, we also found substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52). This provides further insight into the extent and organization of subcortical genetic architecture, which includes developmental and general growth pathways, as well as the functional specialization and maturation trajectories that influence each subcortical region.

Birth size and brain function 75 years later

BACKGROUND

There are several lines of evidence pointing to fetal and other early origins of diseases of the aging brain, but there are no data directly addressing the hypotheses in an older population. We investigated the association of fetal size to late-age measures of brain structure and function in a large cohort of older men and women and explored the modifying effect of education on these associations.

METHODS

Within the AGES (Age Gene/Environment Susceptibility)-Reykjavik population-based cohort (born between 1907 and 1935), archived birth records were abstracted for 1254 men and women who ∼75 years later underwent an examination that included brain MRI and extensive cognitive assessment.

RESULTS

Adjustment for intracranial volume, demographic and medical history characteristics, and lower Ponderal index at birth (per kg/m(3)), an indicator of third-trimester fetal wasting, was significantly associated with smaller volumes of total brain and white matter; βs (95% confidence intervals) were -1.0 (-1.9 to -0.0) and -0.5 (-1.0 to -0.0) mL. Furthermore, lower Ponderal index was associated with slower processing speed and reduced executive functioning but only in those with low education (β [95% confidence interval]: -0.136 [-0.235 to -0.036] and -0.077 [-0.153 to -0.001]).

CONCLUSIONS

This first study of its kind provides clinical measures suggesting that smaller birth size, as an indicator of a suboptimal intrauterine environment, is associated with late-life alterations in brain tissue volume and function. In addition, it shows that the effects of a suboptimal intrauterine environment on late-life cognitive function were present only in those with lower educational levels.

Characterizing the genetic differences between two distinct migrant groups from Indo-European and Dravidian speaking populations in India

Background

India is home to many ethnically and linguistically diverse populations. It is hypothesized that history of invasions by people from Persia and Central Asia, who are referred as Aryans in Hindu Holy Scriptures, had a defining role in shaping the Indian population canvas. A shift in spoken languages from Dravidian languages to Indo-European languages around 1500 B.C. is central to the Aryan Invasion Theory. Here we investigate the genetic differences between two sub-populations of India consisting of: (1) The Indo-European language speaking Gujarati Indians with genome-wide data from the International HapMap Project; and (2) the Dravidian language speaking Tamil Indians with genome-wide data from the Singapore Genome Variation Project.

Results

We implemented three population genetics measures to identify genomic regions that are significantly differentiated between the two Indian populations originating from the north and south of India. These measures singled out genomic regions with: (i) SNPs exhibiting significant variation in allele frequencies in the two Indian populations; and (ii) differential signals of positive natural selection as quantified by the integrated haplotype score (iHS) and cross-population extended haplotype homozygosity (XP-EHH). One of the regions that emerged spans the SLC24A5 gene that has been functionally shown to affect skin pigmentation, with a higher degree of genetic sharing between Gujarati Indians and Europeans.

Conclusions

Our finding points to a gene-flow from Europe to north India that provides an explanation for the lighter skin tones present in North Indians in comparison to South Indians.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Whole-genome analyses of whole-brain data: working within an expanded search space

Large-scale comparisons of patients and healthy controls have unearthed genetic risk factors associated with a range of neurological and psychiatric illnesses. Meanwhile, brain imaging studies are increasing in size and scope, revealing disease and genetic effects on brain structure and function, and implicating neural pathways and causal mechanisms. With the advent of global neuroimaging consortia, imaging studies are now well powered to discover genetic variants that reliably affect the brain. Genetic analyses of brain measures from tens of thousands of people are being extended to test genetic associations with signals at millions of locations in the brain, and connectome-wide, genome-wide scans can jointly screen brain circuits and genomes; these analyses and others present new statistical challenges. There is a growing need for the community to establish and enforce standards in this developing field to ensure robust findings. Here we discuss how neuroimagers and geneticists have formed alliances to discover how genetic factors affect the brain. The field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. We recommend a rigorous approach to neuroimaging genomics that capitalizes on its recent successes and ensures the reliability of future discoveries.

Neuroimaging, nutrition, and iron-related genes

Several dietary factors and their genetic modifiers play a role in neurological disease and affect the human brain. The structural and functional integrity of the living brain can be assessed using neuroimaging, enabling large-scale epidemiological studies to identify factors that help or harm the brain. Iron is one nutritional factor that comes entirely from our diet, and its storage and transport in the body are under strong genetic control. In this review, we discuss how neuroimaging can help to identify associations between brain integrity, genetic variations, and dietary factors such as iron. We also review iron's essential role in cognition, and we note some challenges and confounds involved in interpreting links between diet and brain health. Finally, we outline some recent discoveries regarding the genetics of iron and its effects on the brain, suggesting the promise of neuroimaging in revealing how dietary factors affect the brain.

The Rotterdam Study: 2014 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Measurement and genetics of human subcortical and hippocampal asymmetries in large datasets

Functional and anatomical asymmetries are prevalent features of the human brain, linked to gender, handedness, and cognition. However, little is known about the neurodevelopmental processes involved. In zebrafish, asymmetries arise in the diencephalon before extending within the central nervous system. We aimed to identify genes involved in the development of subtle, left-right volumetric asymmetries of human subcortical structures using large datasets. We first tested the feasibility of measuring left-right volume differences in such large-scale samples, as assessed by two automated methods of subcortical segmentation (FSL|FIRST and FreeSurfer), using data from 235 subjects who had undergone MRI twice. We tested the agreement between the first and second scan, and the agreement between the segmentation methods, for measures of bilateral volumes of six subcortical structures and the hippocampus, and their volumetric asymmetries. We also tested whether there were biases introduced by left-right differences in the regional atlases used by the methods, by analyzing left-right flipped images. While many bilateral volumes were measured well (scan-rescan r = 0.6-0.8), most asymmetries, with the exception of the caudate nucleus, showed lower repeatabilites. We meta-analyzed genome-wide association scan results for caudate nucleus asymmetry in a combined sample of 3,028 adult subjects but did not detect associations at genome-wide significance (P < 5 × 10(-8) ). There was no enrichment of genetic association in genes involved in left-right patterning of the viscera. Our results provide important information for researchers who are currently aiming to carry out large-scale genome-wide studies of subcortical and hippocampal volumes, and their asymmetries.

Disorders and borders: psychiatric genetics and nosology

Over the past century, the definition and classification of psychiatric disorders has evolved through a combination of historical trends, clinical observations, and empirical research. The current nosology, instantiated in the DSM-5 and ICD-10, rests on descriptive criteria agreed upon by a consensus of experts. While the development of explicit criteria has enhanced the reliability of diagnosis, the validity of the current diagnostic categories has been the subject of debate and controversy. Genetic studies have long been regarded as a key resource for validating the boundaries among diagnostic categories. Genetic epidemiologic studies have documented the familiality and heritability of clinically defined psychiatric disorders and molecular genetic studies have begun to identify specific susceptibility variants. At the same time, there is growing evidence from family, twin and genomic studies that genetic influences on psychiatric disorders transcend clinical boundaries. Here I review this evidence for cross-disorder genetic effects and discuss the implications of these findings for psychiatric nosology. Psychiatric genetic research can inform a bottom-up reappraisal of psychopathology that may help the field move beyond a purely descriptive classification and toward an etiology-based nosology.

Mapping the genetic variation of regional brain volumes as explained by all common SNPs from the ADNI study

Typically twin studies are used to investigate the aggregate effects of genetic and environmental influences on brain phenotypic measures. Although some phenotypic measures are highly heritable in twin studies, SNPs (single nucleotide polymorphisms) identified by genome-wide association studies (GWAS) account for only a small fraction of the heritability of these measures. We mapped the genetic variation (the proportion of phenotypic variance explained by variation among SNPs) of volumes of pre-defined regions across the whole brain, as explained by 512,905 SNPs genotyped on 747 adult participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We found that 85% of the variance of intracranial volume (ICV) (p = 0.04) was explained by considering all SNPs simultaneously, and after adjusting for ICV, total grey matter (GM) and white matter (WM) volumes had genetic variation estimates near zero (p = 0.5). We found varying estimates of genetic variation across 93 non-overlapping regions, with asymmetry in estimates between the left and right cerebral hemispheres. Several regions reported in previous studies to be related to Alzheimer's disease progression were estimated to have a large proportion of volumetric variance explained by the SNPs.

GSK3B and MAPT polymorphisms are associated with grey matter and intracranial volume in healthy individuals

The microtubule-associated protein tau gene (MAPT) codes for a protein that plays an integral role in stabilisation of microtubules and axonal transport in neurons. As well as its role in susceptibility to neurodegeneration, previous studies have found an association between the MAPT haplotype and intracranial volume and regional grey matter volumes in healthy adults. The glycogen synthase kinase-3β gene (GSK3B) codes for a serine/threonine kinase that phosphorylates various proteins, including tau, and has also been associated with risk for neurodegenerative disorders and schizophrenia. We examined the effects of MAPT and two functional promoter polymorphisms in GSK3B (rs3755557 and rs334558) on total grey matter and intracranial volume in three independent cohorts totaling 776 neurologically healthy individuals. In vitro analyses revealed a significant effect of rs3755557 on gene expression, and altered binding of at least two transcription factors, Octamer transcription factor 1 (Oct-1) and Pre-B-cell leukemia transcription factor 1 (Pbx-1), to the GSK3B promoter. Meta-analysis across the three cohorts revealed a significant effect of rs3755557 on total grey matter volume (summary B = 0.082, 95% confidence interval = 0.037–0.128) and intracranial volume (summary B = 0.113, 95% confidence interval = 0.082–0.144). No significant effect was observed for MAPT H1/H2 diplotype or GSK3B rs334558 on total grey matter or intracranial volume. Our genetic and biochemical analyses have identified a role for GSK3B in brain development, which could have important aetiological implications for neurodegenerative and neurodevelopmental disorders.

Heritability of subcortical brain measures: a perspective for future genome-wide association studies

Several large imaging-genetics consortia aim to identify genetic variants influencing subcortical brain volumes. We investigated the extent to which genetic variation accounts for the variation in subcortical volumes, including thalamus, amygdala, putamen, caudate nucleus, globus pallidus and nucleus accumbens and obtained the stability of these brain volumes over a five-year period. The heritability estimates for all subcortical regions were high, with the highest heritability estimates observed for the thalamus (.80) and caudate nucleus (.88) and lowest for the left nucleus accumbens (.44). Five-year stability was substantial and higher for larger [e.g., thalamus (.88), putamen (.86), caudate nucleus (.87)] compared to smaller [nucleus accumbens (.45)] subcortical structures. These results provide additional evidence that subcortical structures are promising starting points for identifying genetic variants that influence brain structure.

Migraine, depression, and brain volume: the AGES-Reykjavik Study

OBJECTIVE

To examine the joint association of migraine headache and major depressive disorder on brain volume in older persons without dementia.

METHODS

Participants (n = 4,296, 58% women) from the population-based Age, Gene/Environment Susceptibility-Reykjavik Study were assessed for migraine headache in 1967-1991 (age 51 years [range 33-65]) according to modified International Classification of Headache Disorders-II criteria. In 2002-2006 (age 76 years [range 66-96]), lifetime history of major depressive disorder (depression) was diagnosed according to DSM-IV criteria, and full-brain MRI was acquired, which was computer postprocessed into total brain volume (TBV) (gray matter [GM], white matter [WM], white matter hyperintensities) and CSF volume for each study subject. We compared brain tissue volumes by headache categories with or without depression using linear regression, adjusting for intracranial volume and other factors.

RESULTS

Compared with the reference group (no headache, no depression) TBV and WM and GM volumes were smaller in those with both migraine and depression (TBV -19.2 mL, 95% confidence interval [CI] -35.3, -3.1, p = 0.02; WM -12.8 mL, CI -21.3, -4.3, p = 0.003; GM -13.0 mL, CI -26.0, 0.1, p = 0.05) but not for those with migraine alone (TBV 0.4 mL, WM 0.2 mL, GM 0.6 mL) or depression alone (TBV -3.9 mL, WM -0.9 mL, GM -2.9 mL).

CONCLUSIONS

Reporting both migraine and major depressive disorder was associated with smaller brain tissue volumes than having one or neither of these conditions. Migraineurs with depression may represent a distinct clinical phenotype with different long-term sequelae. Nonetheless, the number of subjects in the current study is relatively small and these findings need to be confirmed in future studies.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk

BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7 × 10(-8), HR = 1.14, 95% CI: 1.09-1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4 × 10(-8), HR = 1.27, 95% CI: 1.17-1.38) and 4q32.3 (rs4691139, P = 3.4 × 10(-8), HR = 1.20, 95% CI: 1.17-1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific association. The 17q21.31 locus was also associated with ovarian cancer risk in 8,211 BRCA2 carriers (P = 2×10(-4)). These loci may lead to an improved understanding of the etiology of breast and ovarian tumors in BRCA1 carriers. Based on the joint distribution of the known BRCA1 breast cancer risk-modifying loci, we estimated that the breast cancer lifetime risks for the 5% of BRCA1 carriers at lowest risk are 28%-50% compared to 81%-100% for the 5% at highest risk. Similarly, based on the known ovarian cancer risk-modifying loci, the 5% of BRCA1 carriers at lowest risk have an estimated lifetime risk of developing ovarian cancer of 28% or lower, whereas the 5% at highest risk will have a risk of 63% or higher. Such differences in risk may have important implications for risk prediction and clinical management for BRCA1 carriers.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism

Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood. Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes and a second variant, near CCNL1, with no obvious link to adult traits. In an expanded genome-wide association meta-analysis and follow-up study of birth weight (of up to 69,308 individuals of European descent from 43 studies), we have now extended the number of loci associated at genome-wide significance to 7, accounting for a similar proportion of variance as maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes, ADRB1 with adult blood pressure and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.

Brain size and the compensation of Alzheimer's disease symptoms: a longitudinal cohort study

BACKGROUND

Greater intracranial volume (ICV) has been associated with less severe Alzheimer's disease (AD) symptoms at a given level of cerebral pathology. In this study we examine whether ICV modulates the association between clinical disease progression on the one hand and brain atrophy or the apolipoprotein E genotype on the other.

METHODS

Six hundred seventy-four subjects were studied from the AD Neuroimaging Initiative (ADNI). Subjects included 204 controls, 144 patients with AD dementia, and 326 with amnestic mild cognitive impairment (aMCI). Longitudinal analyses were conducted applying generalized estimating equations to examine the influence of ICV on clinical deterioration and atrophy progression. Follow-up data were available for up to 60 months after the baseline visit (mean 31.42 months, SD 13.12 months).

RESULTS

ICV was not directly associated with clinical worsening or atrophy progression. However, ICV attenuated the impact of atrophy and the apolipoprotein E ε4 allele on clinical disease progression in aMCI.

CONCLUSION

Greater ICV, that is, premorbid brain size, seems to protect against clinical deterioration in the face of AD-related brain atrophy in aMCI. The results support the theory of a compensatory role of brain reserve in contrast to a neuroprotective role. The protective effects of morphologic reserve seem to be limited to early clinical AD; once a certain threshold of neurodegenerative burden is passed, a larger premorbid brain no longer offers an advantage in this context.

The interleukin 3 gene (IL3) contributes to human brain volume variation by regulating proliferation and survival of neural progenitors

One of the most significant evolutionary changes underlying the highly developed cognitive abilities of humans is the greatly enlarged brain volume. In addition to being far greater than in most other species, the volume of the human brain exhibits extensive variation and distinct sexual dimorphism in the general population. However, little is known about the genetic mechanisms underlying normal variation as well as the observed sex difference in human brain volume. Here we show that interleukin-3 (IL3) is strongly associated with brain volume variation in four genetically divergent populations. We identified a sequence polymorphism (rs31480) in the IL3 promoter which alters the expression of IL3 by affecting the binding affinity of transcription factor SP1. Further analysis indicated that IL3 and its receptors are continuously expressed in the developing mouse brain, reaching highest levels at postnatal day 1–4. Furthermore, we found IL3 receptor alpha (IL3RA) was mainly expressed in neural progenitors and neurons, and IL3 could promote proliferation and survival of the neural progenitors. The expression level of IL3 thus played pivotal roles in the expansion and maintenance of the neural progenitor pool and the number of surviving neurons. Moreover, we found that IL3 activated both estrogen receptors, but estrogen didn’t directly regulate the expression of IL3. Our results demonstrate that genetic variation in the IL3 promoter regulates human brain volume and reveals novel roles of IL3 in regulating brain development.

The Generation R Study: design and cohort update 2012

The Generation R Study is a population-based prospective cohort study from fetal life until adulthood. The study is designed to identify early environmental and genetic causes and causal pathways leading to normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on six areas of research: (1) maternal health; (2) growth and physical development; (3) behavioural and cognitive development; (4) respiratory health and allergies; (5) diseases in childhood; and (6) health and healthcare for children and their parents. Main exposures of interest include environmental, endocrine, genetic and epigenetic, lifestyle related, nutritional and socio-demographic determinants. In total, n = 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. Response at baseline was 61 %, and general follow-up rates until the age of 6 years exceed 80 %. Data collection in mothers, fathers and children include questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome and epigenome wide association screen is available in the participating children. From the age of 5 years, regular detailed hands-on assessments are performed in a dedicated research center including advanced imaging facilities such as Magnetic Resonance Imaging. Eventually, results forthcoming from the Generation R Study contribute to the development of strategies for optimizing health and healthcare for pregnant women and children.

The genetic pleiotropy of musculoskeletal aging

Musculoskeletal aging is detrimental to multiple bodily functions and starts early, probably in the fourth decade of an individual's life. Sarcopenia is a health problem that is expected to only increase as a greater portion of the population lives longer; prevalence of the related musculoskeletal diseases is similarly expected to increase. Unraveling the biological and biomechanical associations and molecular mechanisms underlying these diseases represents a formidable challenge. There are two major problems making disentangling the biological complexity of musculoskeletal aging difficult: (a) it is a systemic, rather than "compartmental," problem, which should be approached accordingly, and (b) the aging per se is neither well defined nor reliably measurable. A unique challenge of studying any age-related condition is a need of distinguishing between the "norm" and "pathology," which are interwoven throughout the aging organism. We argue that detecting genes with pleiotropic functions in musculoskeletal aging is needed to provide insights into the potential biological mechanisms underlying inter-individual differences insusceptibility to the musculoskeletal diseases. However, exploring pleiotropic relationships among the system's components is challenging both methodologically and conceptually. We aimed to focus on genetic aspects of the cross-talk between muscle and its "neighboring" tissues and organs (tendon, bone, and cartilage), and to explore the role of genetics to find the new molecular links between skeletal muscle and other parts of the "musculoskeleton." Identification of significant genetic variants underlying the musculoskeletal system's aging is now possible more than ever due to the currently available advanced genomic technologies. In summary, a "holistic" genetic approach is needed to study the systems's normal functioning and the disease predisposition in order to improve musculoskeletal health.

Candidate and non-candidate genes in behavior genetics

In this review we discuss recent developments in psychiatric genetics: on the one hand, studies using whole genome approaches (genome-wide association studies (GWAS) and exome sequencing) are coming close to finding genes and molecular variants that contribute to disease susceptibility; on the other candidate genes, such as the serotonin transporter, continue to dominate in genetic studies of brain imaging phenotypes and in protracted searches for gene by environment interactions. These two areas intersect, in that new information about genetic effects from whole genome approaches, should (but does not always) inform the single locus analyses.

Common variants at 12q15 and 12q24 are associated with infant head circumference

To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 × 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 × 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height, their effects on infant head circumference were largely independent of height (P = 3.8 × 10(-7) for rs7980687 and P = 1.3 × 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 × 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume, Parkinson's disease and other neurodegenerative diseases, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.