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Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N = 12378 , replication set: N = 4456 ) yielded two sequence variants, rs1452628-T ( β = − 0.08 , P = 1.15 × 10 − 9 ) and rs2435204-G ( β = 0.102 , P = 9.73 × 1 0 − 12 ). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.

Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 Icelandic parent–offspring trios at high coverage. We show that in our samples, with an average father’s age of 29.7, the average de novo mutation rate is 1.20 × 10 −8 per nucleotide per generation. Most notably, the diversity in mutation rate of single nucleotide polymorphisms is dominated by the age of the father at conception of the child. The effect is an increase of about two mutations per year. An exponential model estimates paternal mutations doubling every 16.5 years. After accounting for random Poisson variation, father’s age is estimated to explain nearly all of the remaining variation in the de novo mutation counts. These observations shed light on the importance of the father’s age on the risk of diseases such as schizophrenia and autism. Whole-genome sequencing of 78 Icelandic parent–offspring trios is used to study the de novo mutation rate at the genome-wide level; the rate is shown to increase by about two mutations a year as a function of the increasing age of the father at conception, highlighting the importance of father’s age on the risk of diseases such as autism and schizophrenia. Fathers' ages linked to disease risk De novo mutations are important both as sources of diversity in evolution and for their immediate impact on diseases. Scientists at deCODE genetics and their colleagues have used whole-genome sequencing data from 78 Icelandic parent–offspring trios to study mutation rates in humans at the genome-wide level. They find that diversity in the mutation rate of single nucleotide polymorphisms is dominated by the age of the father at the time a child is conceived. For each year increase in the father's age at conception, the number of mutations increases by about two, and once the effects of random variation are accounted for the father's age is estimated to explain almost all of the remaining variation in the de novo mutation counts. Furthermore, the results show that demographic transitions that affect the age at which males reproduce can have a considerable effect on the rate of mutations, and consequently on the risk of diseases such as schizophrenia and autism.

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment. A genome-wide meta-analysis of data from six US and European cohorts involving 1.3 million individuals identifies 243 genetic variants associated with risk and pathophysiology of depression, which is used to develop polygenic risk scores for the prediction of depression recurrence and comorbid psychiatric disorders.

Endophenotypes are heritable and quantifiable traits indexing genetic liability for a disorder. Here, we examined three potential endophenotypes, working memory function, response inhibition, and reaction time variability, for attention-deficit hyperactivity disorder (ADHD) measured as a dimensional latent trait in a large general population sample derived from the Adolescent Brain Cognitive DevelopmentSM Study. The genetic risk for ADHD was estimated using polygenic risk scores (PRS) whereas ADHD traits were quantified as a dimensional continuum using Bartlett factor score estimates, derived from Attention Problems items from the Child Behaviour Checklist and Effortful Control items from the Early Adolescent Temperament Questionnaire-Revised. The three candidate cognitive endophenotypes were quantified using task-based performance measures. Higher ADHD PRSs were associated with higher ADHD traits, as well as poorer working memory performance and increased reaction time variability. Lower working memory performance, poorer response inhibition, and increased reaction time variability were associated with more pronounced ADHD traits. Working memory and reaction time variability partially statistically mediated the relationship between ADHD PRS and ADHD traits, explaining 14% and 16% of the association, respectively. The mediation effect was specific to the genetic risk for ADHD and did not generalise to genetic risk for four other major psychiatric disorders. Together, these findings provide robust evidence from a large general population sample that working memory and reaction time variability can be considered endophenotypes for ADHD that mediate the relationship between ADHD PRS and ADHD traits.

Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal ( cis ) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits. Obesity gets complicated Complex human diseases result from the interplay of many genetic and environmental factors. To build up a picture of the factors contributing to one such disease, obesity, gene expression was evaluated as a quantitative trait in blood and adipose tissue samples from hundreds of Icelandic subjects aged 18 to 85. The results reveal a tendency to certain characteristic patterns of gene activation in the fatty tissues — though to a much lesser extent in the blood — of people with a higher body mass index. A transcriptional network constructed from the adipose tissue data has significant overlap with a network based on mouse adipose tissue data. Experimental support for the idea that complex diseases are emergent properties of molecular networks influenced by genes and environment comes from a study in mice. Mice were examined for disturbances in genetic expression networks that correlate with metabolic traits associated with obesity, diabetes and atherosclerosis. Three genes — Lpl , Lactb and Ppm1l — were identified as previously unknown obesity genes. This 'molecular network' approach raises the prospect that therapies might be directed at whole 'disease networks', rather than at one or two specific genes. In this paper gene expression is treated as a quantitative trait in both blood and adipose tissue, and associations between specific genetic loci and body mass index are identified using a molecular network approach.

Genetic risk scores derived from GWAS of psychotic disorders are greater in creative professionals unaffected by psychosis. This association cannot be explained by shared environment or education. Thus, a shared genetic architecture underlies the propensity for creativity and psychosis. We tested whether polygenic risk scores for schizophrenia and bipolar disorder would predict creativity. Higher scores were associated with artistic society membership or creative profession in both Icelandic ( P = 5.2 × 10 −6 and 3.8 × 10 −6 for schizophrenia and bipolar disorder scores, respectively) and replication cohorts ( P = 0.0021 and 0.00086). This could not be accounted for by increased relatedness between creative individuals and those with psychoses, indicating that creativity and psychosis share genetic roots.

Recombination maps reveal differences between the sexes High-resolution recombination maps serve many purposes in genetic research. The currently available maps, which use linkage disequilibrium patterns of high-density SNP (single nucleotide polymorphism) data from the HapMap project, have proved to be very useful. But they have some limitations; for instance, they do not provide information on differences in recombination characteristics between and within the sexes. A team at biopharmaceutical firm deCODE genetics in Reykjavik has used genome-wide SNP data from more than 15,000 parent–offspring pairs to construct the first recombination maps based on directly observed recombination events, providing resolution down to 10 kilobases. Their data reveal interesting recombination differences between the sexes. In males, for example, recombination tends to shuffle exons, whereas in females it generates new combinations of nearby genes. Comparisons of these maps with those based on linkage disequilibrium reveal previously unrecognized differences between populations in Europe, Africa and the United States. Here, human genome-wide single-nucleotide polymorphism (SNP) data from more than 15,000 parent–offspring pairs have been used to construct the first recombination maps that are based on directly observed recombination events. The data reveal interesting differences between the sexes: for instance, in males recombination tends to shuffle exons, whereas in females it generates new combinations of nearby genes. Comparison of these maps with others also reveals population differences. Meiotic recombinations contribute to genetic diversity by yielding new combinations of alleles. Recently, high-resolution recombination maps were inferred from high-density single-nucleotide polymorphism (SNP) data using linkage disequilibrium (LD) patterns that capture historical recombination events 1 , 2 . The use of these maps has been demonstrated by the identification of recombination hotspots 2 and associated motifs 3 , and the discovery that the PRDM9 gene affects the proportion of recombinations occurring at hotspots 4 , 5 , 6 . However, these maps provide no information about individual or sex differences. Moreover, locus-specific demographic factors like natural selection 7 can bias LD-based estimates of recombination rate. Existing genetic maps based on family data avoid these shortcomings 8 , but their resolution is limited by relatively few meioses and a low density of markers. Here we used genome-wide SNP data from 15,257 parent–offspring pairs to construct the first recombination maps based on directly observed recombinations with a resolution that is effective down to 10 kilobases (kb). Comparing male and female maps reveals that about 15% of hotspots in one sex are specific to that sex. Although male recombinations result in more shuffling of exons within genes, female recombinations generate more new combinations of nearby genes. We discover novel associations between recombination characteristics of individuals and variants in the PRDM9 gene and we identify new recombination hotspots. Comparisons of our maps with two LD-based maps inferred from data of HapMap populations of Utah residents with ancestry from northern and western Europe (CEU) and Yoruba in Ibadan, Nigeria (YRI) reveal population differences previously masked by noise and map differences at regions previously described as targets of natural selection.

Gudmar Thorleifsson and colleagues report results of a large-scale genome-wide association and replication study for obesity-related traits. The newly discovered loci are enriched for genes expressed in the central nervous system, and may thus contribute to weight gain by modulating food intake. Similar results are reported in a related study by Joel Hirschhorn and colleagues. Obesity results from the interaction of genetic and environmental factors. To search for sequence variants that affect variation in two common measures of obesity, weight and body mass index (BMI), both of which are highly heritable, we performed a genome-wide association (GWA) study with 305,846 SNPs typed in 25,344 Icelandic, 2,998 Dutch, 1,890 European Americans and 1,160 African American subjects and combined the results with previously published results from the Diabetes Genetics Initiative (DGI) on 3,024 Scandinavians. We selected 43 variants in 19 regions for follow-up in 5,586 Danish individuals and compared the results to a genome-wide study on obesity-related traits from the GIANT consortium. In total, 29 variants, some correlated, in 11 chromosomal regions reached a genome-wide significance threshold of P < 1.6 × 10 −7 . This includes previously identified variants close to or in the FTO , MC4R , BDNF and SH2B1 genes, in addition to variants at seven loci not previously connected with obesity.

Creatine kinase (CK) and lactate dehydrogenase (LDH) are widely used markers of tissue damage. To search for sequence variants influencing serum levels of CK and LDH, 28.3 million sequence variants identified through whole-genome sequencing of 2,636 Icelanders were imputed into 63,159 and 98,585 people with CK and LDH measurements, respectively. Here we describe 13 variants associating with serum CK and 16 with LDH levels, including four that associate with both. Among those, 15 are non-synonymous variants and 12 have a minor allele frequency below 5%. We report sequence variants in genes encoding the enzymes being measured ( CKM and LDHA ), as well as in genes linked to muscular ( ANO5 ) and immune/inflammatory function ( CD163/CD163L1, CSF1, CFH, HLA-DQB1, LILRB5, NINJ1 and STAB1 ). A number of the genes are linked to the mononuclear/phagocyte system and clearance of enzymes from the serum. This highlights the variety in the sources of normal diversity in serum levels of enzymes. Creatine kinase (CK) and lactate dehydrogenase (LDH) are biomarkers of tissue damages including myopathy and myocardial infarction. Here, Patrick Sulem and colleagues perform a genome-wide association study to identify common and rare genetic variants that associates with serum CK or LDH levels.

Hilma Holm et al . report a rare missense variant MYH6 that is associated with a high risk of sick sinus syndrome in Icelanders. This heart condition is found most often in elderly people and is the most frequent reason a heart pacemaker is implanted. Through complementary application of SNP genotyping, whole-genome sequencing and imputation in 38,384 Icelanders, we have discovered a previously unidentified sick sinus syndrome susceptibility gene, MYH6 , encoding the alpha heavy chain subunit of cardiac myosin. A missense variant in this gene, c.2161C>T, results in the conceptual amino acid substitution p.Arg721Trp, has an allelic frequency of 0.38% in Icelanders and associates with sick sinus syndrome with an odds ratio = 12.53 and P = 1.5 × 10 −29 . We show that the lifetime risk of being diagnosed with sick sinus syndrome is around 6% for non-carriers of c.2161C>T but is approximately 50% for carriers of the c.2161C>T variant.