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Everything is heritable: population registry methods using siblings/half-siblings continue to confirm twin study results: "Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls", Pettersson et al 2018

"*Background*: Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.
Methods: We assessed heritability based on data of Swedish siblings ( N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes ( N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.
Results: Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.5
Conclusions: Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.

Using two different methods, this study capitalizes on the largest and most powerful data-sets to date, to estimate the heritability of eight psychiatric conditions: (1) alcohol dependence (AD), (2) anorexia nervosa (AN), (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder (ASD), (5) bipolar disorder (BIP), (6) major depressive disorder (MDD), (7) obsessivecompulsive disorder (OCD), and (8) SCZ. First, we use a large Swedish national cohort (h 2 national) that currently includes over 20 million full and maternal half-sibling pairs. Unlike most twin studies, the Swedish sibling sample uses clinical diagnoses derived from medical in and out-patient treatment registers, instead of surveys. Second, we use summary data of eight large samples of subjects with measured SNPs (h 2 -SNP). The uniqueness is estimating heritability from very large samples, based on genetic similarities inferred from distantly related people. As in the h 2 -national design, case status in the h 2 -SNP design is based on diagnostic criteria

By comparing the observed tetrachoric correlations between the binary diagnoses for full and maternal half-siblings, we estimated the contribution of genetic variance (h 2 national), and shared and non-shared environmental variance. The analyses were carried out in Mplus (Muthén and Muthén, 1998 ) using the mean and variance-adjusted unweighted least squares estimator. We regressed out the effects of sex and age from all diagnoses. For ADHD and ASD, we limited the birth year to 1990 and beyond because these diagnoses only existed in ICD 9 and 10

Estimates of shared environmental effects were non-significant in the sibling analyses. The heritability estimates ( Fig. 1 )showed significant differences between the h 2 -national estimates and the h 2 -SNP estimates. The latter were significantly lower (corrected p < 0.02), except for AN, BIP, and OCD where the h 2 -SNP estimates did not significantly differ from h 2 -national (corrected p > 0.30). However, these differences should be interpreted with caution as due to the somewhat smaller samples sizes of these particular disorders, the standard errors ( S . E .) of h 2 -national were relatively wide for AN, BIP, and OCD. Of note, the S . E . is in general sensitive to sample size, and in particular for h 2 -national because the full and half-sibling groups only differ by 0.25 in genetic relatedness. Additionally, the nature of summary data of large consortium designs implies that included samples have been genotyped on different platforms and chips, potentially increasing the S . E .ofh 2 -SNP. Heritability estimates from the two designs correlated positively ( r 0.19). However, this correlation was mainly driven by OCD that showed the highest h 2 -SNP and lowest h 2 -national; when removing OCD this correlation increased to 0.50. The high h 2 -SNP is probably due to the fact that the OCD sample is heavily ascertained from highly multiplex families and early age of onset cases, and consists thus of the most severe and genetically loaded cases

The heritability estimates based on the large national sibling study (h 2 -national) were remarkably similar to previous twin studies of psychiatric traits (Polderman et al ., 2015 ), despite different assessment strategies, with twin studies being survey-based, and as such based on psychiatric trait measures, and the national sibling study based on clinical diagnoses. This might suggest that heritability estimates are robust across different diagnostic tools and measures. It is also in line with studies that reported high genetic correlations between survey-based psychiatric traits and clinical diagnoses, e.g. for ASD (Colvert et al ., 2015 ), ADHD (Lubke et al ., 2009 ), and psychosis (Zavos et al ., 2014 ), suggesting an overlap in genetic factors between psychiatric traits as measured in the general population and clinical disorders."
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More replication of Lee et 2018: 11% IQ/16% education, increasing with age (Wilson effect): "Genomic prediction of cognitive traits in childhood and adolescence", Allegrini et al 2018:

"Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status."

The betas will continue until morale improves.
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"Genome-edited skin epidermal stem cells protect mice from cocaine-seeking behaviour and cocaine overdose", Li et al 2018:

"Cocaine addiction is associated with compulsive drug seeking, and exposure to the drug or to drug-associated cues leads to relapse, even after long periods of abstention. A variety of pharmacological targets and behavioural interventions have been explored to counteract cocaine addiction, but to date no market-approved medications for treating cocaine addiction or relapse exist, and effective interventions for acute emergencies resulting from cocaine overdose are lacking. We recently demonstrated that skin epidermal stem cells can be readily edited using CRISPR (clustered regularly interspaced short palindromic repeats) and then transplanted back into the donor mice. Here, we show that the transplantation, into mice, of skin cells modified to express an enhanced form of butyrylcholinesterase—an enzyme that hydrolyses cocaine—enables the long-term release of the enzyme and efficiently protects the mice from cocaine-seeking behaviour and cocaine overdose. Cutaneous gene therapy through skin transplants that elicit drug elimination may offer a therapeutic option to address drug abuse."
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The power of intense selection in large populations: Weber's fruit-fly flying speed experiment was able to increase peak flying speed in a wind tunnel by >85x over 100 generations. Summary:

"Large Genetic Change at Small Fitness Cost in Large Populations of Drosophila melanogaster Selected for Wind Tunnel Flight: Rethinking Fitness Surfaces", Weber 1996:

"The fitness effects of extreme genetic change by selection were studied in large populations subjected to prolonged, intense selection. Two replicate populations of Drosophila melanogaster, with estimated effective sizes 500<=Ne<=1000, were selected for increased performance in a wind tunnel, selecting on average the fastest 4.5% of flies. The mean apparent flying speed of both lines increased from ~2 to 170 cm/sec and continued to respond at diminishing rates, without reaching a plateau, for 100 generations. Competitive fitness tests in generations 50 and 85 showed minimal or no fitness loss in selected lines compared to controls. Sublines relaxed in generations 65 and 85 showed minimal or no regression in apparent flying speed. Hybrid lines, from a cross of selected X control lines in generation 75, responded to reselection saltationally, showing that the chromosomes of the selected lines had been assembled from alleles at many loci, from many different chromosomes in the base population. Thus, major genetic change was achieved, but without the costs usually associated with strong directional selection. Large population size has been interpreted, in opposing models, as either a brake or an accelerator in its effects on long-term change by selection. These results favor the second model, and challenge the concept of rugged fitness surfaces underlying the first model."
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The value of writing WP articles? "Examining Wikipedia With a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities", Vincent et al 2018:

"The extensive Wikipedia literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between Google and Wikipedia that are highly relevant to many areas of Wikipedia based research and practice. This paper extends this recent research beyond search engines to examine Wikipedia's relationships with large scale online communities, Stack Overflow and Reddit in particular. We find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but we find little evidence th at these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, our results also emphasize the key role that volunteer created Wikipedia content plays in improving other websites, even contributing to revenue generation.

Wikipedia content appears to play a substantially more important role in the Internet ecosystem than anticipated, with other websites having critical dependencies on Wikipedia content. In particular, McMahon et al. [33] showed that the click through rates of Google SERPs (search engine results pages) drop dramatically when Wikipedia links are removed, suggesting that Google is quite reliant on Wikipedia to satisfy user information needs. Among other implications, this means that the Wikipedia peer production processes studied in the social computing literature likely have a substantial - and largely unstudied - impact on other websites. McMahon et al.'s results also raised important questions related to the revenue being generated by for profit institutions using volunteer created Wikipedia con tent, especially in light of Wikipedia's limited donation income.

Following the high level structure of McMahon et al [33], this paper asks two overarching research questions about Wikipedia's relationship with SO and Reddit :
RQ1: What value is Wikipedia providing to other large scale online communities like Stack Overflow and Reddit? (i.e. Does Wikipedia con tent increase community engagement and/ or company revenue?)
RQ2: What value do these large scale online communities provide to Wikipedia? (i.e. Are they contributing page views? Editors?)
We additionally take an important step beyond McMahon et al. and investigate how the quality of Wikipedia articles affects the relationships examined in RQ1 and RQ2. In other words, we look at the association between the quality of articles on Wikipedia and the value that Wikipedia provides to external entities, and vice versa.
We address our RQs using a combined framework of associative and causal analyses that allows us to estimate Wikipedia's relationships with SO and Reddit under a range of conditions. For instance, at the upper bound of this range, our associative analyses allow us to ask, " How much value would be lost from SO and Reddit if posts containing Wikipedia links never appeared?" Similarly, to estimate a lower bound on the value Wikipedia could be providing to SO and Reddit, we use causal analysis to examine the counterfactual scenario, "What if the posts containing Wikipedia links remained unchanged content wise, but instead had a link to a site other than Wikipedia? " The results of our analyses indicate that Wikipedia creates a large amount of value for SO and Reddit, even in our lower bound estimates.
More specifically, we observe that posts containing Wikipedia links on both sites are exceptionally valuable posts, with engagement metrics like user voted scores much higher than posts that do not contain Wikipedia links (often by a factor of at least 2x, and sometimes as much as 4x 5x). This results in an estimated increase in revenue on the order of $100K/year for both sites. However, we find little evidence that posts with Wikipedia links provide direct value to the Wikipedia community. We were able to replicate work that showed that Wikipedia posts on the popular Reddit community "TIL" (" Today I Learned") were responsible for a large spike in viewership. However, our results suggest that this large effect does not generalize beyond the "TIL" community or beyond Reddit. Moreover, we see negligible increases in Wikipedia edits and editor signup despite the large volume of links posted on both sites.

Specifically, we measure post value in RQ1 through four metrics. The fir st three metrics are user engagement statistics: (1) Score, equal to upvotes minus downvotes, (2) Comments, the number of comments a post receives, (3) Page Views, the number of views a post receives. To contextualize these metrics, we also calculate (4) Revenue, or the financial gain generated by Wikipedia posts.
Revenue is calculated directly from the engagement statistics using publicly available financial information (described in detail in Study 1 - Results). In the case of Reddit (which does not release page view data), it is important to note that score controls post visibility and correlates with page views [48].
With respect to RQ2, we assess the value that Wikipedia receives from external communities as contributions to the editing community and increased readership. Specifically, we measure this value with four metrics that capture changes in edits, editors, and viewership in a given week : (1) Edit Count is the number of times an article was edited, (2) Editors Gained is the number of new editors who edited an article, (3) Editors Retained is the number of new editors who made another edit in the future (we measured at one mo nth and six months later, following past research on editor retention [6,37]), and (4) Article Page Views is the number of views that each Wikipedia article received. To capture the effect of Reddit and SO on Wikipedia, we calculated the metrics for the week before and the week after each post containing a Wikipedia link.

Overall, we were surprised to find that Wikipedia links represent only 0. 13% of posted content on Reddit. However, further examining this result, we found that Wikipedia links are substantially over represented in high value locations. For instance, Wikipedia is the third most linked site (after YouTube and Imgur) on the ten most popular subreddits. This relatively low quantity/high quality dynamic is one we see frequently in our formal analyses below. On SO, Wikipedia links appear in 1.28% of posts, but this makes them the fourth most common type of external link (after,, and the popular "code playground" Notably, github and jsfiddle are used to share code, and MSDN is Microsoft's c ode documentation library, meaning that Wikipedia is the most important conceptual reference for programming on SO

Effects on Reddit posts:
Table 3a shows that Wikipedia linked posts on Reddit are exceptionally valuable. To a post's score, Wikipedia adds between 10.8 points (ATT, Δ in Table 3a) and 15.1 points (Mean Values: Has WP Link, with a middle ground estimate of 1.41 points (ATT: Has WP Link vs No External Link). Relative to the average post's score of 3.0 points, this is a 4x-5x increase. Aggregating these findings across all 120K Wikipedia linked posts from 2016, this means that Wikipedia is responsible for an increase in user voted score of between 13.1M and 18.4M points in 2016 (up to 0.7% of all points on the site). Additionally, Table 3a also shows that Wikipedia adds 11.19 comments per post. This means Wikipedia linked posts generate twice as much discussion as average posts. In total, this amounted to between 1.3M and 2.3M comments in 2016.
Effects on Stack Overflow posts: For SO, Table 3 a displays a similar trend to what we saw with Reddit. For instance, Wikipedia linked content on SO adds 2.9-6.5 points per post, with a middle ground of about 3.4 points. This means that Wikipedia linked answers are roughly twice as valuable as other answers and, across the 280K Wikipedia linked answers on SO, increased total score between 0.8M and 1.7M points. This score increase is accompanied by a page view increase of 954-3473 per question, with a middle ground of 1337. Estimating based on the 12K questions with Wikipedia linked answers in our page view analysis, Wikipedia may have added 64M to 234M views (which we use for revenue estimation). However, we see no evidence of an effect on SO comments. Overall, the presented estimates show that even assuming all authors could continue writing the same posts, except with non-Wikipedia alternative links, Wikipedia still adds significant value (i.e. through its brand or other factors)

Our coding analysis revealed that 79% of Reddit posts had quoted or summarized text from the linked Wikipedia article, whereas this was true of 33% of SO posts. This result shows that many posts can be characterized by "direct reuse"."
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"Association Between Population Density and Genetic Risk for Schizophrenia", Colodro-Conde et al 2018:

"IMPORTANCE: Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas.
OBJECTIVE: To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk.
DESIGN, SETTING, AND PARTICIPANTS: Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and Mendelian randomization (MR) were included. The study was conducted between 2016 and 2018.
EXPOSURES: Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models.
MAIN OUTCOMES AND MEASURES: Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested.
RESULTS: The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia ( r 2 =0.12%; P =5.69×10 −5 ), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P =3.7×10 −7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations.
CONCLUSIONS AND RELEVANCE: The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions.

...Variance Component Analysis
Population density, remoteness, and SES were all significantly correlated at a phenotypic level in all samples considered (eAppendix 10 and eTable 4 in the
Supplement ). Population density and remoteness were heritable (heritability [ h 2 ]) in the QIMR sample ( Figure 1 )( h 2 for population density = 16.9%; 95% CI, 3.4-30.4; P = .01; h 2 for remoteness = 16.3%; 95% CI, 3.5-29.0; P = .01); the heritability of SES was not significant ( h 2 = 11.0%; 95% CI, 0.00-24.6; P = .12). Shared environment (common environment [ c 2 ]) effects explained a more substantial and highly significant proportion of the trait variance (Figure 1) ( c 2 for population density = 24.3%; 95% CI, 13.1-35.1; c 2 for remoteness = 29.1%; 95% CI, 19.0-40.0; and c 2 for socioeconomic status = 26.8%; 95% CI, 15.6-37.1; all P < .001), which highlights that people tend to live with or close to their parents or other relatives. Population density was also heritable in the NTR ( h 2 = 12.1%; 95% CI, 1.3-23.2; P = .28) and showed shared environment sources of variance ( c 2 = 36.5; 95% CI, 26.8-45.7; P =7×10 −12 ). In the QIMR cohort, population density was more heritable and less influenced by shared environmental sources as participants became older (eAppendix 11 in the Supplement ), with the heritability increasing from 9.0% to 25.6% between ages 20 and 80 years. Over the same lifespan, c 2 decreased from 44.5% to less than 10.0% (eAppendix 11 in the Supplement ), but variance explained by unique environmental sources (including measurement error) remained constant (eAppendix 11 in the Supplement ). Similar results were obtained using standardized estimates, suggesting constant phenotypic variance across age. In addition, population density, remoteness, and SES shared environmental influences as indicated by significant environmental correlations from the twin models (eTable 4 in the Supplement ).
Polygenic Risk Scores Analysis
Figure 2 shows the percentage of variance of the population density of the place of residence explained by the PRS for schizophrenia, with and without controlling for SES. In the QIMR sample, PRS calculated from all semi-independent SNPs across the genome (Figure 2) explained the greatest amount of variance in population density ( r 2 = 0.12%; P =5.69×10 −5 ), and still explained ( r 2 = 0.074%; P < .001) when accounting for SES. Schizophrenia PRS also were significantly associated with remoteness ( r 2 = 0.06%; P = .003) when including all of the independent SNPs, although the association disappeared when correcting for SES. eFigure 6 and eAppendix 12 in the Supplement provide information on the association between genetic risk for schizophrenia, remoteness, and SES. We did not find evidence of interactions between sex or age and PRS for schizophrenia ( P > .05) contributing to population density or remoteness in the QIMR sample. The association between schizophrenia risk score and population density was replicated in the NTR ( r 2 =0.14%; P =8.3×10 −4 and r 2 = 0.073%; P = .002 when correcting for SES), in the UKB ( r 2 = 0.088%; P =7.7×10 −59 ; r 2 = 0.012%; P =1.2×10 −11 when accounting for SES) and in QSKIN ( r 2 = 0.027%; P = .02 and r 2 = 0.015%; P = .047 when correcting for SES) (Figure 2). All correlations were in the same direction, pointing to increased PRS for participants living in more densely populated areas. Results for remoteness and SES in QSKIN were consistent with those in QIMR and are presented in eFigure 7 in the Supplement . In addition, we tested the association between SES and schizophrenia PRS. The association did not reach statistical significance in the QIMR or QSKIN sample when taking into account multiple testing ( P > .01) (eFigures 6 and 7 in the Supplement ) but was significant in the UKB ( r 2 = 0.084%; P =8.6×10 −64 , correcting for population density) (eFigure 8 in the Supplement ) likely because of the gain of power owing to its very large sample size.
Genome-wide Association Analyses
Six genomic regions reached genome-wide significance for population density in the UKB, and this number increased to 12 when correcting for SES. Similarly, we identified 13 loci associated with SES in the UKB when correcting for population density. We observed fewer significant associations with population density or remoteness in the smaller samples, but they did not correspond to the SNP associations found in the UKB. The SNP heritability ranged from 0.6% (QIMR: SE, 3.2%) to 9.3% (QSKIN: SE, 4.0%) as estimated by LD score regression (eTable 5 in the Supplement ). The genetic correlation between population density across samples ranged from 0.30 (SE, 0.44; P = .49, QSKIN-NTR) to 0.61 (SE, 0.29; P = .04, UKB-NTR). eAppendix 13, eFigures 9-16, and eTable 5 in the Supplement provide all GWAS and LD score regression results (including GWAS meta-analysis). Mendelian Randomization Finally, we selected between 88 and 94 genome-wide significant SNPs for schizophrenia as instruments to perform MR analyses with population density as the outcome variable after excluding SNPs showing evidence of pleiotropic effects by the heterogeneity in dependent instruments outlier analysis (implemented in the GSMR software). 37 These numbers are consistent with the 108 independent associations reported by the Psychiatric Genomics Consortium 11 ; the difference arose from SNPs not being present or not passing quality control in GWAS of population density and SES. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P =3.7×10 −7 using GSMR) ( Table 2 , Figure 3 ), suggesting that the genetic liability to schizophrenia has a causal association with the tendency to live in urbanized locations. We observed similar effect sizes in all other samples, although the MR results were not significant. We found no evidence of confounding heterogeneity of effect sizes ( P > .30) or from pleiotropy ( P > .05) using the tests implemented in MR-Base. Reverse MR testing (propensity to live in a more dense or low socioeconomic status area as a cause of schizophrenia) was only suggestive of a (larger) association with population density or SES on schizophrenia (b = 0.20; P =.01 using GSMR) as it did not survive multiple testing correction. This lack of evidence could have been the result of reduced statistical power, as both genetic instruments had a smaller number of SNPs (n = 12) (Table 2; eAppendix 14 and eTables 6-10 in the Supplement provide detailed MR results).

...We found a large environmental correlation between SES and population density in Australia (bivariate model includ- ing additive genetic, common environmental, and unique environmental factors, r C = 0.86, P < .0001; r E = 0.33, P < .0001) compared with the estimated genetic correlation ( r G = 0.35, P = .34.) (eTable 4 in the Supplement ). Thus, SES is a potential confounder of genetic analyses of population density, and conversely, population density is a confounder of SES genetic analyses. Composition of SES measures 23 studied here differed in each country and also differ from the Swedish study. 3"
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"A Polygenic Score for Higher Educational Attainment is Associated With Larger Brains", Elliott et al 2018:

"People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants' genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants' education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples [due to range restriction]. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance."
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Creating 4000 BRCA mutations with CRISPR to measure their harmful effects: "Accurate classification of BRCA1 variants with saturation genome editing", Findlay et al 2018:

"Variants of uncertain significance fundamentally limit the clinical utility of genetic information. The challenge they pose is epitomized by BRCA1, a tumour suppressor gene in which germline loss-of-function variants predispose women to breast and ovarian cancer. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Here we use saturation genome editing to assay 96.5% of all possible single-nucleotide variants (SNVs) in 13 exons that encode functionally critical domains of BRCA1. Functional effects for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Over 400 non-functional missense SNVs are identified, as well as around 300 SNVs that disrupt expression. We predict that these results will be immediately useful for the clinical interpretation of BRCA1 variants, and that this approach can be extended to overcome the challenge of variants of uncertain significance in additional clinically actionable genes."
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Lasso regression improves osteoporosis fracture prediction from 18% to 25% - not using informative priors has real world consequences, kids. (Call it 'regularization' if you must.) "Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes", Forgetta et al 2018:

"Background: Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS) — a risk factor for osteoporotic fracture. Methods: We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic prescreening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX. Results: gSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic prescreening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%. Interpretation: The use of genotypes in a machine learning algorithm resulted in a clinically relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization."
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