Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20 000 individuals in the Generation Scotland family cohort genotyped for ~700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.
The pathophysiology of antisocial personality disorder (ASPD) remains unclear. Although the most consistent biological finding is reduced grey matter volume in the frontal cortex, about 50% of the total liability to developing ASPD has been attributed to genetic factors. The contributing genes remain largely unknown. Therefore, we sought to study the genetic background of ASPD. We conducted a genome-wide association study ( ) and a replication analysis of Finnish criminal offenders fulfilling DSM-IV criteria for ASPD (n = 370, n = 5850 for controls, ; n = 173, n = 3766 for controls and replication sample). The resulted in suggestive associations of two clusters of single-nucleotide polymorphisms at 6p21.2 and at 6p21.32 at the human leukocyte antigen (HLA) region. Imputation of HLA alleles revealed an independent association with DRB1*01:01 (odds ratio (OR) = 2.19 (1.53–3.14), p = 1.9 × 10-5). Two polymorphisms at 6p21.2 LINC00951–LRFN2 gene region were replicated in a separate data set, and rs4714329 reached genome-wide statistical-significance (OR = 1.59 (1.37–1.85), p = 1.6 × 10−9) in the meta-analysis. The risk allele also associated with antisocial features in the general population conditioned for severe problems in childhood family (β = 0.68, p = 0.012). Functional analysis in brain tissue in open access GTEx and Braineac databases revealed eQTL associations of rs4714329 with LINC00951 and LRFN2 in cerebellum. In humans, LINC00951 and LRFN2 are both expressed in the brain, especially in the frontal cortex, which is intriguing considering the role of the frontal cortex in behavior and the neuroanatomical findings of reduced gray matter volume in ASPD. To our knowledge, this is the first study showing genome-wide statistically-significant and replicable findings on genetic variants associated with any personality disorder.
Human populations living in Eurasia during the Holocene experienced significant evolutionary change. It has been predicted that the transition of Holocene populations into agrarianism and urbanization brought about culture-gene co-evolution that favoured via directional selection genetic variants associated with higher general cognitive ability (GCA). Population expansion and replacement has also been proposed as an important source of GCA gene-frequency change during this time period. To examine whether GCA might have risen during the Holocene, we compare a sample of 99 ancient Eurasian genomes (ranging from 4,557 to 1,208 years of age) with a sample of 503 modern European genomes, using three different cognitive polygenic scores. Significant differences favouring the modern genomes were found for all three polygenic scores (Odds Ratio = 0.92, p = 0.037; 0.81, p = 0.001 and 0.81, p = 0.02). Furthermore, a statistically-significant increase in positive allele count over 3,249 years was found using a sample of 66 ancient genomes (r = 0.217, pone-tailed = 0.04). These observations are consistent with the expectation that GCA rose during the Holocene.
Scans for positive selection in human populations have identified hundreds of sites across the genome with evidence of recent adaptation. These signatures often overlap across populations, but the question of how often these overlaps represent a single ancestral event remains unresolved. If a single positive selection event spread across many populations, the same sweeping haplotype should appear in each population and the selective pressure could be common across diverse populations and environments. Identifying such shared selective events would be of fundamental interest, pointing to genomic loci and human traits important in recent history across the globe. Additionally, genomic annotations that recently became available could help attach these signatures to a potential gene and molecular phenotype that may have been selected across multiple populations. We performed a scan for positive selection using the integrated haplotype score on 20 populations, and compared sweeping haplotypes using the haplotype-clustering capability of fastPHASE to create a catalog of shared and unshared overlapping selective sweeps in these populations. Using additional genomic annotations, we connect these multi-population sweep overlaps with potential biological mechanisms at several loci, including potential new sites of adaptive introgression, the glycophorin locus associated with malarial resistance, and the alcohol dehydrogenase cluster associated with alcohol dependency.
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function. We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training. Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous reinforcement learning algorithm (A3C).
“Deep Voice: Real-time Neural Text-to-Speech”, (2017-02-25):
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400× speedups over existing implementations.
2017-debarra.pdf: “Reporting bias inflates the reputation of medical treatments: A comparison of outcomes in clinical trials and online product reviews”, Mícheál de Barra
1989-omni-walterstewartinterview.pdf: “Walter Stewart: Fighting Fraud in Science (They call him the 'terrorist of the lab', but this self-appointed scourge of scientific fraud has reason to suspect that as much as 25 percent of all research papers may be intentionally fudged) [interview]”, Walter Stewart, Doug Stewart
The widespread reluctance to share published research data is often hypothesized to be due to the authors’ fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically.
Methods and Findings:
We related the reluctance to share research data for reanalysis to 1148results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on .
Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.
A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.
Author Summary: A new wave of wearable sensors allows frequent and continuous measurements of body functions (physiology), including heart rate, skin temperature, blood oxygen levels, and physical activity. We investigated the ability of wearable sensors to follow physiological changes that occur over the course of a day, during illness and other activities. Data from these sensors revealed personalized differences in daily patterns of activities. Interestingly, we discovered striking changes in particular environments such as airline flights. Blood oxygen levels decreased during high-altitude flights, and this decrease was associated with fatigue. By combining sensor information with frequent medical measurements, we made two important health-related observations. First, wearable sensors were useful in identifying the onset of Lyme disease and inflammation. From this observation, we then developed a computational algorithm for personalized disease detection using such sensors. Second, we found that wearable sensors can reveal physiological differences between insulin-sensitive and insulin-resistant individuals, raising the possibility that these sensors could help detect risk for type 2 diabetes. Overall, these results indicate that the information provided by wearable sensors is physiologically meaningful and actionable. Wearable sensors are likely to play an important role in managing health.