Explore the vast range of titles available.

The last few years have seen extensive efforts to catalogue human genetic variation and elucidate its relationship to phenotypes, especially disease. Important challenges lie ahead in this area, particularly in relation to the contribution of rare and copy number variants. Key Points Human genetic variants are typically referred to as either common or rare, to denote the frequency of the minor allele in the human population. Genetic variants can also be divided into two different nucleotide composition classes — single nucleotide variants and structural variants. The alleles of SNPs located in the same genomic interval are often correlated with one another. This correlation structure, or linkage disequilibrium (LD), varies in a complex and unpredictable manner across the genome and between different populations. Structural variants seem to behave similarly to SNPs in terms of both genomic and population distribution, indicating a similar evolutionary history: both types of variants are 'ancestral' having arisen once in human history and then shared among individuals by descent rather than being the result of recurrent mutations. Full sequencing of human genomes has shown that in any given individual there are, on average, ∼4 million genetic variants encompassing ∼12 Mb of sequence. The challenge is to determine which of these variants underlie or are responsible for the inherited components of phenotypes. Over the last decade or so the human genetics field has debated the common disease–common variant hypothesis, which posits that common complex traits are largely due to common variants with small-to-modest affect sizes. The opposing theory, the rare variant hypothesis, posits that common complex traits are the summation of low-frequency, high-penetrance variants. Genome-wide association (GWA) studies are the most widely used contemporary approach to relate genetic variation to phenotypic diversity. Over the past 2 years these studies have identified statistical association between hundreds of loci across the genome and common complex traits. Most of the genes or genomic loci that have been identified by GWA studies have not previously been known to be related to the complex trait under investigation. Surprisingly, there have been several instances in which one genomic interval has been associated with two or more seemingly distinct diseases. An unforeseen limitation of GWA studies is that the genomic markers that are found to be associated with any given complex trait each have less impact on susceptibility than was anticipated. Most of the odds ratios for the heterozygote genotypes of the associated variants that have been identified so far are approximately 1.1, a figure that can increase to 1.5–1.6 for homozygote genotypes. At this point, there are almost no complex traits for which more than 10% of the genetic variance is explained, and many are far below that threshold, leaving the bulk of heritability unexplained by the common variants identified so far. One possibility is that the missing variation is accounted for by common genetic variants with small effect sizes that have not yet been identified. Some of the missing heritability is probably accounted for by rare and novel variants. Additionally, there are statistical limitations of the GWA approach in identifying gene–gene and gene–environment interactions, which are likely to be profoundly important. The last few years have seen extensive efforts to catalogue human genetic variation and correlate it with phenotypic differences. Most common SNPs have now been assessed in genome-wide studies for statistical associations with many complex traits, including many important common diseases. Although these studies have provided new biological insights, only a limited amount of the heritable component of any complex trait has been identified and it remains a challenge to elucidate the functional link between associated variants and phenotypic traits. Technological advances, such as the ability to detect rare and structural variants, and a clear understanding of the challenges in linking different types of variation with phenotype, will be essential for future progress.

Socioeconomic status is associated with cognitive development, but the extent to which this reflects neuroanatomical differences is unclear. In 1,099 children and adolescents, family income was nonlinearly associated with brain surface area, and this association was greatest among disadvantaged children. Further, surface area mediated links between income and executive functioning. Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.

LDL receptor-related proteins (LRPs) are transmembrane receptors involved in endocytosis, cell-signaling, and trafficking of other cellular proteins. Considerable work has focused on LRPs in the fields of vascular biology and neurobiology. How these receptors affect cancer progression in humans remains largely unknown. Herein, we mined provisional databases in The Cancer Genome Atlas (TCGA) to compare expression of thirteen LRPs in ten common solid malignancies in patients. Our first goal was to determine the abundance of LRP mRNAs in each type of cancer. Our second goal was to determine whether expression of LRPs is associated with improved or worsened patient survival. In total, data from 4,629 patients were mined. In nine of ten cancers studied, the most abundantly expressed LRP was LRP1; however, a correlation between LRP1 mRNA expression and patient survival was observed only in bladder urothelial carcinoma. In this malignancy, high levels of LRP1 mRNA were associated with worsened patient survival. High levels of LDL receptor (LDLR) mRNA were associated with decreased patient survival in pancreatic adenocarcinoma. High levels of LRP10 mRNA were associated with decreased patient survival in hepatocellular carcinoma, lung adenocarcinoma, and pancreatic adenocarcinoma. LRP2 was the only LRP for which high levels of mRNA expression correlated with improved patient survival. This correlation was observed in renal clear cell carcinoma. Insights into LRP gene expression in human cancers and their effects on patient survival should guide future research.

Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi‐tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment. Synopsis Analyses of the relationship between blood gene expression and brain size in 142 Autism Spectrum Disorder (ASD) and control male toddlers reveal peripheral blood signatures of ASD and genetic mechanisms underlying abnormal early neural development. In ASD, the correlation of brain size measures with cell cycle and protein folding gene networks is lost, while cell adhesion networks significantly correlate with brain size. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. In ASD, cell cycle networks display changes in topological organization and these alterations are more pronounced in bigger brains. A predicted high‐confidence network indicates dysregulation of neurogenesis and cell adhesion processes in ASD brain development. Graphical Abstract Analyses of the relationship between blood gene expression and brain size in 142 Autism Spectrum Disorder (ASD) and control male toddlers reveal peripheral blood signatures of ASD and genetic mechanisms underlying abnormal early neural development.

Telomeres are engaged in a host of cellular functions, and their length is regulated by multiple genes. Telomere shortening, in the course of somatic cell replication, ultimately leads to replicative senescence. In humans, rare mutations in genes that regulate telomere length have been identified in monogenic diseases such as dyskeratosis congenita and idiopathic pulmonary fibrosis, which are associated with shortened leukocyte telomere length (LTL) and increased risk for aplastic anemia. Shortened LTL is observed in a host of aging-related complex genetic diseases and is associated with diminished survival in the elderly. We report results of a genome-wide association study of LTL in a consortium of four observational studies (n = 3,417 participants with LTL and genome-wide genotyping). SNPs in the regions of the oligonucleotide/oligosaccharide-binding folds containing one gene (OBFC1; rs4387287; P = 3.9 x 10⁻⁹) and chemokine (C-X-C motif) receptor 4 gene (CXCR4; rs4452212; P = 2.9 x 10⁻⁸) were associated with LTL at a genome-wide significance level (P < 5 x 10⁻⁸). We attempted replication of the top SNPs at these loci through de novo genotyping of 1,893 additional individuals and in silico lookup in another observational study (n = 2,876), and we confirmed the association findings for OBFC1 but not CXCR4. In addition, we confirmed the telomerase RNA component (TERC) as a gene associated with LTL (P = 1.1 x 10⁻⁵). The identification of OBFC1 through genome-wide association as a locus for interindividual variation in LTL in the general population advances the understanding of telomere biology in humans and may provide insights into aging-related disorders linked to altered LTL dynamics.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Recent genome-wide association (GWA) studies have pinpointed many loci associated with CVD risk factors in adults. It is unclear, however, if these loci predict trait levels at all ages, if they are associated with how a trait develops over time, or if they could be used to screen individuals who are pre-symptomatic to provide the opportunity for preventive measures before disease onset. We completed a genome-wide association study on participants in the longitudinal Bogalusa Heart Study (BHS) and have characterized the association between genetic factors and the development of CVD risk factors from childhood to adulthood. We report 7 genome-wide significant associations involving CVD risk factors, two of which have been previously reported. Top regions were tested for replication in the Young Finns Study (YF) and two associations strongly replicated: rs247616 in CETP with HDL levels (combined P = 9.7 x 10(-24)), and rs445925 at APOE with LDL levels (combined P = 8.7 x 10(-19)). We show that SNPs previously identified in adult cross-sectional studies tend to show age-independent effects in the BHS with effect sizes consistent with previous reports. Previously identified variants were associated with adult trait levels above and beyond those seen in childhood; however, variants with time-dependent effects were also promising predictors. This is the first GWA study to evaluate the role of common genetic variants in the development of CVD risk factors in children as they advance through adulthood and highlights the utility of using longitudinal studies to identify genetic predictors of adult traits in children.

BackgroundTumor mutation burden (TMB) is a biomarker frequently reported by clinical laboratories, which is derived by quantifying of the number of single nucleotide or indel variants (mutations) identified by next-generation sequencing of tumors. TMB values can inform prognosis or predict the response of a patient’s tumor to immune checkpoint inhibitor therapy. Methods for the calculation of TMB are not standardized between laboratories, with significant variables being the gene content of the panels sequenced and the inclusion or exclusion of synonymous variants in the calculations. The impact of these methodological differences has not been investigated and the concordance of reported TMB values between laboratories is unknown.MethodsSequence variant lists from more than 9000 tumors of various types were downloaded from The Cancer Genome Atlas. Variant lists were filtered to include only appropriate variant types (ie, non-synonymous only or synonymous and non-synonymous variants) within the genes found in five commonly used targeted solid tumor gene panels as well as an in-house gene panel. Calculated TMB was paired with corresponding overall survival (OS) data of each patient.ResultsRegression analysis indicates high concordance of TMB as derived from the examined panels. TMB derived from panels was consistently and significantly lower than that derived from a whole exome. TMB, as derived from whole exome or the examined panels, showed a significant correlation with OS in the examined data.ConclusionsTMB derived from the examined gene panels was analytically equivalent between panels, but not between panels and whole-exome sequencing. Correlation between TMB and OS is significant if TMB method-specific cut-offs are used. These results suggest that TMB values, as derived from the gene panels examined, are analytically and prognostically equivalent.

Pauline C. Ng, Sarah S. Murray, Samuel Levy and J. Craig Venter find differences in results from two direct-toconsumer genetics-testing companies. They therefore give nine recommendations to improve predictions.

Advances in high-throughput genotyping and the International HapMap Project have enabled association studies at the whole-genome level. We have constructed whole-genome genotyping panels of over 550,000 (HumanHap550) and 650,000 (HumanHap650Y) SNP loci by choosing tag SNPs from all populations genotyped by the International HapMap Project. These panels also contain additional SNP content in regions that have historically been overrepresented in diseases, such as nonsynonymous sites, the MHC region, copy number variant regions and mitochondrial DNA. We estimate that the tag SNP loci in these panels cover the majority of all common variation in the genome as measured by coverage of both all common HapMap SNPs and an independent set of SNPs derived from complete resequencing of genes obtained from SeattleSNPs. We also estimate that, given a sample size of 1,000 cases and 1,000 controls, these panels have the power to detect single disease loci of moderate risk (lambda approximately 1.8-2.0). Relative risks as low as lambda approximately 1.1-1.3 can be detected using 10,000 cases and 10,000 controls depending on the sample population and disease model. If multiple loci are involved, the power increases significantly to detect at least one locus such that relative risks 20%-35% lower can be detected with 80% power if between two and four independent loci are involved. Although our SNP selection was based on HapMap data, which is a subset of all common SNPs, these panels effectively capture the majority of all common variation and provide high power to detect risk alleles that are not represented in the HapMap data.

The main objective of the multi-site Pediatric Imaging, Neurocognition, and Genetics (PING) study was to create a large repository of standardized measurements of behavioral and imaging phenotypes accompanied by whole genome genotyping acquired from typically-developing children varying widely in age (3 to 20years). This cross-sectional study produced sharable data from 1493 children, and these data have been described in several publications focusing on brain and cognitive development. Researchers may gain access to these data by applying for an account on the PING portal and filing a data use agreement. Here we describe the recruiting and screening of the children and give a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available. We also cite sources of more detailed information about the methods and data. Finally we describe the procedures for accessing the data and for using the PING data exploration portal. •We provide a brief description of the Pediatric Imaging Neurocognition and Genetics (PING) Study.•We describe the data in the PING Data Repository.•We outline the methods used to generate the data.•We describe the procedure for accessing and exploring the data through the PING Portal.