newsletter/2017/09 (Link Bibliography)

“newsletter/​2017/​09” links:

  1. https://gwern.substack.com

  2. 08

  3. newsletter

  4. Changelog

  5. https://www.patreon.com/gwern

  6. Ads

  7. ⁠, Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D. H. Hsu (2017-10-07):

    We construct genomic predictors for heritable and extremely complex human quan-titative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated heritability from (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem—i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier for out-of-sample validation of our results.

  8. ⁠, Stephen D. H. Hsu (2014-08-14):

    How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be significant in the near future. I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a “general factor” or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation. Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a significant fraction of total g variation. In the case of height analogous efforts are well under way. I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation. Using results from Compressed Sensing (L1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits. The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.

  9. https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-3-10

  10. https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0081-6

  11. 2013-rietveld.pdf: ⁠, Cornelius A. Rietveld, Sarah E. Medland, Jaime Derringer, Jian Yang, Tõnu Esko, Nicolas W. Martin, Harm-Jan Westra, Konstantin Shakhbazov, Abdel Abdellaoui, Arpana Agrawal, Eva Albrecht, Behrooz Z. Alizadeh, Najaf Amin, John Barnard, Sebastian E. Baumeister, Kelly S. Benke, Lawrence F. Bielak, Jeffrey A. Boatman, Patricia A. Boyle, Gail Davies, Christiaan de Leeuw, Niina Eklund, Daniel S. Evans, Rudolf Ferhmann, Krista Fischer, Christian Gieger, Håkon K. Gjessing, Sara Hägg, Jennifer R. Harris, Caroline Hayward, Christina Holzapfel, Carla A. Ibrahim-Verbaas, Erik Ingelsson, Bo Jacobsson, Peter K. Joshi, Astanand Jugessur, Marika Kaakinen, Stavroula Kanoni, Juha Karjalainen, Ivana Kolcic, Kati Kristiansson, Zoltán Kutalik, Jari Lahti, Sang H. Lee, Peng Lin, Penelope A. Lind, Yongmei Liu, Kurt Lohman, Marisa Loitfelder, George McMahon, Pedro Marques Vidal, Osorio Meirelles, Lili Milani, Ronny Myhre, Marja-Liisa Nuotio, Christopher J. Oldmeadow, Katja E. Petrovic, Wouter J. Peyrot, Ozren Polašek, Lydia Quaye, Eva Reinmaa, John P. Rice, Thais S. Rizzi, Helena Schmidt, Reinhold Schmidt, Albert V. Smith, Jennifer A. Smith, Toshiko Tanaka, Antonio Terracciano, Matthijs J. H. M. van der Loos, Veronique Vitart, Henry Völzke, Jürgen Wellmann, Lei Yu, Wei Zhao, Jüri Allik, John R. Attia, Stefania Bandinelli, François Bastardot, Jonathan Beauchamp, David A. Bennett, Klaus Berger, Laura J. Bierut, Dorret I. Boomsma, Ute Bültmann, Harry Campbell, Christopher F. Chabris, Lynn Cherkas, Mina K. Chung, Francesco Cucca, Mariza de Andrade, Philip L. De Jager, Jan-Emmanuel De Neve, Ian J. Deary, George V. Dedoussis, Panos Deloukas, Maria Dimitriou, Guðný Eiríksdóttir, Martin F. Elderson, Johan G. Eriksson, David M. Evans, Jessica D. Faul, Luigi Ferrucci, Melissa E. Garcia, Henrik Grönberg, Vilmundur Guðnason, Per Hall, Juliette M. Harris, Tamara B. Harris, Nicholas D. Hastie, Andrew C. Heath, Dena G. Hernandez, Wolfgang Hoffmann, Adriaan Hofman, Rolf Holle, Elizabeth G. Holliday, Jouke-Jan Hottenga, William G. Iacono, Thomas Illig, Marjo-Riitta Järvelin, Mika Kähönen, Jaakko Kaprio, Robert M. Kirkpatrick, Matthew Kowgier, Antti Latvala, Lenore J. Launer, Debbie A. Lawlor, Terho Lehtimäki, Jingmei Li, Paul Lichtenstein, Peter Lichtner, David C. Liewald, Pamela A. Madden, Patrik K. E. Magnusson, Tomi E. Mäkinen, Marco Masala, Matt McGue, Andres Metspalu, Andreas Mielck, Michael B. Miller, Grant W. Montgomery, Sutapa Mukherjee, Dale R. Nyholt, Ben A. Oostra, Lyle J. Palmer, Aarno Palotie, Brenda W. J. H. Penninx, Markus Perola, Patricia A. Peyser, Martin Preisig, Katri Räikkönen, Olli T. Raitakari, Anu Realo, Susan M. Ring, Samuli Ripatti, Fernando Rivadeneira, Igor Rudan, Aldo Rustichini, Veikko Salomaa, Antti-Pekka Sarin, David Schlessinger, Rodney J. Scott, Harold Snieder, Beate St Pourcain, John M. Starr, Jae Hoon Sul, Ida Surakka, Rauli Svento, Alexander Teumer, The LifeLines Cohort Study, Henning Tiemeier, Frank J. A. van Rooij, David R. Van Wagoner, Erkki Vartiainen, Jorma Viikari, Peter Vollenweider, Judith M. Vonk, Gérard Waeber, David R. Weir, H.-Erich Wichmann, Elisabeth Widen, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Dalton Conley, George Davey-Smith, Lude Franke, Patrick J. F. Groenen, Albert Hofman, Magnus Johannesson, Sharon L. R. Kardia, Robert F. Krueger, David Laibson, Nicholas G. Martin, Michelle N. Meyer, Danielle Posthuma, A. Roy Thurik, Nicholas J. Timpson, André G. Uitterlinden, Cornelia M. van Duijn, Peter M. Visscher, Daniel J. Benjamin, David Cesarini, Philipp D. Koellinger (2013-06-21; iq):

    A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide statistically-significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our estimates can power analyses in social-science genetics.

    [A landmark study in behavioral genetics and intelligence: the first well-powered GWAS to detect genetic variants for intelligence and education which replicate out of sample and are proven to be causal in a between-sibling study.]

  12. ⁠, Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw, Mats Nagel, Swapnil Awasthi, Peter B. Barr, Jonathan R. I Coleman, Katrina L. Grasby, Anke R. Hammerschlag, Jakob Kaminski, Robert Karlsson, Eva Krapohl, Max Lam, Marianne Nygaard, Chandra A. Reynolds, Joey W. Trampush, Hannah Young, Delilah Zabaneh, Sara Hägg, Narelle K. Hansell, Ida K. Karlsson, Sten Linnarsson, Grant W. Montgomery, Ana B. Muñoz-Manchado, Erin B. Quinlan, Gunter Schumann, Nathan Skene, Bradley T. Webb, Tonya White, Dan E. Arking, Deborah K. Attix, Dimitrios Avramopoulos, Robert M. Bilder, Panos Bitsios, Katherine E. Burdick, Tyrone D. Cannon, Ornit Chiba-Falek, Andrea Christoforou, Elizabeth T. Cirulli, Eliza Congdon, Aiden Corvin, Gail Davies, Ian J. Deary, Pamela DeRosse, Dwight Dickinson, Srdjan Djurovic, Gary Donohoe, Emily Drabant Conley, Johan G. Eriksson, Thomas Espeseth, Nelson A. Freimer, Stella Giakoumaki, Ina Giegling, Michael Gill, David C. Glahn, Ahmad R. Hariri, Alex Hatzimanolis, Matthew C. Keller, Emma Knowles, Bettina Konte, Jari Lahti, Stephanie Le Hellard, Todd Lencz, David C. Liewald, Edythe London, Astri J. Lundervold, Anil K. Malhotra, Ingrid Melle, Derek Morris, Anna C. Need, William Ollier, Aarno Palotie, Antony Payton, Neil Pendleton, Russell A. Poldrack, Katri Räikkönen, Ivar Reinvang, Panos Roussos, Dan Rujescu, Fred W. Sabb, Matthew A. Scult, Olav B. Smeland, Nikolaos Smyrnis, John M. Starr, Vidar M. Steen, Nikos C. Stefanis, Richard E. Straub, Kjetil Sundet, Aristotle N. Voineskos, Daniel R. Weinberger, Elisabeth Widen, Jin Yu, Goncalo Abecasis, Ole A. Andreassen, Gerome Breen, Lene Christiansen, Birgit Debrabant, Danielle M. Dick, Andreas Heinz, Jens Hjerling-Leffler, M. Arfan Ikram, Kenneth S. Kendler, Nicholas G. Martin, Sarah E. Medland, Nancy L. Pedersen, Robert Plomin, Tinca JC Polderman, Stephan Ripke, Sophie van der Sluis, Patrick F. Sullivan, Henning Tiemeier, Scott I. Vrieze, Margaret J. Wright, Danielle Posthuma (2017-09-06):

    Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (n = 279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ⁠, and bidirectional causation with strong pleiotropy for ⁠. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

  13. ⁠, Mats Nagel, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Christiaan A. de Leeuw, Julien Bryois, Jeanne E. Savage, Anke R. Hammerschlag, Nathan Skene, Ana B. Muñoz-Manchado, the 23andMe Research Team, Sten Linnarsson, Jens Hjerling-Leffler, Tonya JH White, Henning Tiemeier, Tinca JC Polderman, Patrick F. Sullivan, Sophie van der Sluis, Danielle Posthuma (2017-09-05):

    Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (n = 449,484), and identify 136 independent genome-wide loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P = 3×10-8), medium spiny neurons (P = 4×10-8) and serotonergic neurons (P = 1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P = 4.4×10-9), behavioural response to cocaine processes (P = 1.84×10-7), and axon part (p = 5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg = 0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

  14. Embryo-selection

  15. 2018-evangelou.pdf: “Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits”⁠, Evangelos Evangelou, Helen R. Warren, David Mosen-Ansorena, Borbala Mifsud, Raha Pazoki, He Gao, Georgios Ntritsos, Niki Dimou, Claudia P. Cabrera, Ibrahim Karaman, Fu Liang Ng, Marina Evangelou, Katarzyna Witkowska, Evan Tzanis, Jacklyn N. Hellwege, Ayush Giri, Digna R. Velez Edwards, Yan V. Sun, Kelly Cho, J. Michael Gaziano, Peter W. F. Wilson, Philip S. Tsao, Csaba P. Kovesdy, Tonu Esko, Reedik Mamp#x000E4;gi, Lili Milani, Peter Almgren, Thibaud Boutin, Stamp#x000E9;phanie Debette, Jun Ding, Franco Giulianini, Elizabeth G. Holliday, Anne U. Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jianamp#x02019;an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Peter Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Samp#x000E9;bastien Thamp#x000E9;riault, Niek Verweij, Sara M. Willems, Jing-Hua Zhao, Philippe Amouyel, John Connell, Renamp#x000E9;e Mutsert, Alex S. F. Doney, Martin Farrall, Cristina Menni, Andrew D. Morris, Raymond Noordam, Guillaume Paramp#x000E9;, Neil R. Poulter, Denis C. Shields, Alice Stanton, Simon Thom, Gonamp#x000E7;alo Abecasis, Najaf Amin, Dan E. Arking, Kristin L. Ayers, Caterina M. Barbieri, Chiara Batini, Joshua C. Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I. Boomsma, Erwin P. Bottinger, Peter S. Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C. Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J. Cordell, Gail Davies, Martin H. de Borst, Eco J. de Geus, Ian J. Deary, Joris Deelen, Fabiola Del Greco M., Cumhur Yusuf Demirkale, Marcus Damp#x000F6;rr, Georg B. Ehret, Roberto Elosua, Stefan Enroth, A. Mesut Erzurumluoglu, Teresa Ferreira, Mattias Framp#x000E5;nberg, Oscar H. Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J. Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B. Harris, Sarah E. Harris, Catharina A. Hartman, Aki S. Havulinna, Andrew A. Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E. Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, amp#x000C5;sa Johansson, Andrew D. Johnson, Peter K. Joshi, Pekka Jousilahti, J. Wouter Jukema, Antti Jula, Mika Kamp#x000E4;hamp#x000F6;nen, Sekar Kathiresan, Bernard D. Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S. Kooner, Seppo Koskinen, Kati Kristiansson, Zoltan Kutalik, Maris Laan, Marty Larson, Lenore J. Launer, Benjamin Lehne, Terho Lehtimamp#x000E4;ki, David C. M. Liewald, Li Lin, Lars Lind, Cecilia M. Lindgren, YongMei Liu, Ruth J. F. Loos, Lorna M. Lopez, Yingchang Lu, Leo-Pekka Lyytikamp#x000E4;inen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P. Morris, Alanna C. Morrison, Peter J. Munson, Mike A. Nalls, Priyanka Nandakumar, Christopher P. Nelson, Teemu Niiranen, Ilja M. Nolte, Teresa Nutile, Albertine J. Oldehinkel, Ben A. Oostra, Paul F. Oamp#x02019;Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Aarno Palotie, Alison Pattie, Brenda W. J. H. Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P. Pramstaller, Quang Tri Nguyen, Olli T. Raitakari, Meixia Ren, Rainer Rettig, Kenneth Rice, Paul M. Ridker, Janina S. Ried, Harriamp#x000EB;tte Riese, Samuli Ripatti, Antonietta Robino, Lynda M. Rose, Jerome I. Rotter, Igor Rudan, Daniela Ruggiero, Yasaman Saba, Cinzia F. Sala, Veikko Salomaa, Nilesh J. Samani, Antti-Pekka Sarin, Reinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert V. Smith, Harold Snieder, Siim Samp#x000F5;ber, Rossella Sorice, John M. Starr, David J. Stott, David P. Strachan, Rona J. Strawbridge, Johan Sundstramp#x000F6;m, Morris A. Swertz, Kent D. Taylor, Alexander Teumer, Martin D. Tobin, Maciej Tomaszewski, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, Andramp#x000E9, G. Uitterlinden, Ahmad Vaez, Peter J. Most, Cornelia M. Duijn, Anne-Claire Vergnaud, Germaine C. Verwoert, Veronique Vitart, Uwe Vamp#x000F6;lker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H. Wild, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R. Attia, Adam S. Butterworth, Daniel I. Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna M. M. Howson, Markku Laakso, Edward G. Lakatta, Claudia Langenberg, Olle Melander, Dennis O. Mook-Kanamori, Colin N. A. Palmer, Lorenz Risch, Robert A. Scott, Rodney J. Scott, Peter Sever, Tim D. Spector, Pim Harst, Nicholas J. Wareham, Eleftheria Zeggini, Daniel Levy, Patricia B. Munroe, Christopher Newton-Cheh, Morris J. Brown, Andres Metspalu, Adriana M. Hung, Christopher J. Oamp#x02019;Donnell, Todd L. Edwards, Bruce M. Psaty, Ioanna Tzoulaki, Michael R. Barnes, Louise V. Wain, Paul Elliott, Mark J. Caulfield

  16. 2017-akiyama.pdf: ⁠, Masato Akiyama, Yukinori Okada, Masahiro Kanai, Atsushi Takahashi, Yukihide Momozawa, Masashi Ikeda, Nakao Iwata, Shiro Ikegawa, Makoto Hirata, Koichi Matsuda, Motoki Iwasaki, Taiki Yamaji, Norie Sawada, Tsuyoshi Hachiya, Kozo Tanno, Atsushi Shimizu, Atsushi Hozawa, Naoko Minegishi, Shoichiro Tsugane, Masayuki Yamamoto, Michiaki Kubo Yoichiro Kamatani (2017-09-11; genetics  /​ ​​ ​correlation):

    Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of (BMI) in Japanese people (n = 173,430), we found 85 loci statistically-significantly associated with obesity (p < 5.0 × 10−8), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19+ cells. We also found statistically-significant between BMI and lymphocyte count (p = 6.46 × 10−5, rg = 0.18) and between BMI and multiple complex diseases. These findings provide genetic evidence that lymphocytes are relevant to body weight regulation and offer insights into the pathogenesis of obesity.

  17. https://www.sciencedirect.com/science/article/pii/S0917504016300673

  18. 2017-tropf.pdf: “Hidden heritability due to heterogeneity across seven populations”⁠, Felix C. Tropf, S. Hong Lee, Renske M. Verweij, Gert Stulp, Peter J. van der Most, Ronald de Vlaming, Andrew Bakshi, Daniel A. Briley, Charles Rahal, Robert Hellpap, Anastasia N. Iliadou, Tamp#x000F5;nu Esko, Andres Metspalu, Sarah E. Medland, Nicholas G. Martin, Nicola Barban, Harold Snieder, Matthew R. Robinson, Melinda C. Mills

  19. ⁠, Genevieve L. Wojcik, Mariaelisa Graff, Katherine K. Nishimura, Ran Tao, Jeffrey Haessler, Christopher R. Gignoux, Heather M. Highland, Yesha M. Patel, Elena P. Sorokin, Christy L. Avery, Gillian M. Belbin, Stephanie A. Bien, Iona Cheng, Chani J. Hodonsky, Laura M. Huckins, Janina Jeff, Anne E. Justice, Jonathan M. Kocarnik, Unhee Lim, Bridget M. Lin, Yingchang Lu, Sarah C. Nelson, Sung-Shim L. Park, Michael H. Preuss, Melissa A. Richard, Claudia Schurmann, Veronica W. Setiawan, Karan Vahi, Abhishek Vishnu, Marie Verbanck, Ryan Walker, Kristin L. Young, Niha Zubair, Jose Luis Ambite, Eric Boerwinkle, Erwin Bottinger, Carlos D. Bustamante, Christian Caberto, Matthew P. Conomos, Ewa Deelman, Ron Do, Kimberly Doheny, Lindsay Fernandez-Rhodes, Myriam Fornage, Gerardo Heiss, Lucia A. Hindorff, Rebecca D. Jackson, Regina James, Cecelia A. Laurie, Cathy C. Laurie, Yuqing Li, Dan-Yu Lin, Girish Nadkarni, Loreall C. Pooler, Alexander P. Reiner, Jane Romm, Chiara Sabati, Xin Sheng, Eli A. Stahl, Daniel O. Stram, Timothy A. Thornton, Christina L. Wassel, Lynne R. Wilkens, Sachi Yoneyama, Steven Buyske, Chris Haiman, Charles Kooperberg, Loic Le Marchand, Ruth JF Loos, Tara C. Matise, Kari E. North, Ulrike Peters, Eimear E. Kenny, Christopher S. Carlson (2017-09-15):

    Genome-wide association studies (GWAS) have laid the foundation for many downstream investigations, including the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of human variation and hinders the translation of genetic associations into clinical and public health applications. To demonstrate the benefit of studying underrepresented populations, the Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using novel strategies for multi-ethnic analysis of admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 28 novel loci and 42 residual signals in known loci. Our data show strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, which substantially restricts genetically-guided precision medicine. We advocate for new, large genome-wide efforts in diverse populations to reduce health disparities.

  20. ⁠, Annah B. Wyss, Tamar Sofer, Mi Kyeong Lee, Natalie Terzikhan, Jennifer N. Nguyen, Lies Lahousse, Jeanne C. Latourelle, Albert Vernon Smith, Traci M. Bartz, Mary F. Feitosa, Wei Gao, Tarunveer S. Ahluwalia, Wenbo Tang, Christopher Oldmeadow, Qing Duan, Kim de Jong, Mary K. Wojczynski, Xin-Qun Wang, Raymond Noordam, Fernando Pires Hartwig, Victoria E. Jackson, Tianyuan Wang, Ma’en Obeidat, Brian D. Hobbs, Tianxiao Huan, Gleb Kichaev, Jianping Jin, Mariaelisa Graff, Tamara B. Harris, Ravi Kalhan, Susan R. Heckbert, Lavinia Paternoster, Kristin M. Burkart, Yongmei Liu, Elizabeth G. Holliday, James G. Wilson, Judith M. Vonk, Jason Sanders, R. Graham Barr, Renée de Mutsert, Ana Maria Baptista Menezes, Hieab H. H. Adams, Maarten van den Berge, Roby Joehanes, Lenore J. Launer, Alanna C. Morrison, Colleen M. Sitlani, Juan C. Celedón, Stephen B. Kritchevsky, Rodney J. Scott, Kaare Christensen, Jerome I. Rotter, Tobias N. Bonten, Fernando César Wehrmeister, Yohan Bossé, Nora Franceschini, Jennifer A. Brody, Robert C. Kaplan, Kurt Lohman, Mark McEvoy, Michael A. Province, Frits R. Rosendaal, Kent D. Taylor, David C. Nickle, International COPD Genetics Consortium Investigators, Vilmundur Gudnason, Kari E. North, Myriam Fornage, Bruce M. Psaty, Richard H. Myers, George O’Connor, Torben Hansen, Cathy C. Laurie, Pat Cassano, Joohon Sung, Woo Jin Kim, John R. Attia, Leslie Lange, H. Marike Boezen, Bharat Thyagarajan, Stephen S. Rich, Dennis O. Mook-Kanamori, Bernardo Lessa Horta, André G. Uitterlinden, Don D. Sin, Hae Kyung Im, Michael H. Cho, Guy G. Brusselle, Sina A. Gharib, Josée Dupuis, Ani Manichaikul, Stephanie J. London (2017-10-05):

    Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (n = 60,552), African (n = 8,429), Asian (n = 9,959), and Hispanic/​​​​Latino (n = 11,775) ethnicities. We identified over 50 novel loci at genome-wide statistical-significance in ancestry-specific and/​​​​or multiethnic meta-analyses. Recent fine mapping methods incorporating functional annotation, gene expression, and/​​​​or differences in linkage disequilibrium between ethnicities identified potential causal variants and genes at known and newly identified loci. Sixteen of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12.

  21. ⁠, Hakhamanesh Mostafavi, Tomaz Berisa, Felix R. Day, John R. B. Perry, Molly Przeworski, Joseph K. Pickrell (2017-08-03):

    A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

    Author summary:

    Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study in humans.

  22. https://www.nature.com/news/massive-genetic-study-shows-how-humans-are-evolving-1.22565

  23. 2017-gazal.pdf: ⁠, Steven Gazal, Hilary K. Finucane, Nicholas A. Furlotte, Po-Ru Loh, Pier Francesco Palamara, Xuanyao Liu, Armin Schoech, Brendan Bulik-Sullivan, Benjamin M. Neale, Alexander Gusev, Alkes L. Price (2017-09-11; genetics  /​ ​​ ​selection):

    Recent work has hinted at the linkage disequilibrium ()-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average n = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have statistically-significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (p = 2.38 × 10−104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly statistically-significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

  24. ⁠, Armin P. Schoech, Daniel Jordan, Po-Ru Loh, Steven Gazal, Luke O’Connor, Daniel J. Balick, Pier F. Palamara, Hilary K. Finucane, Shamil R. Sunyaev, Alkes L. Price (2017-09-13):

    Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (n = 113,851). All traits produced negative α estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically-significant heterogeneity across traits (p = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.

  25. 2017-marciniak.pdf: “Harnessing ancient genomes to study the history of human adaptation”⁠, Stephanie Marciniak, George H. Perry

  26. https://www.cell.com/cell/fulltext/S0092-8674(17)31008-5

  27. https://www.cell.com/ajhg/fulltext/S0002-9297(17)30379-8

  28. 2017-ostrander.pdf: “Demographic history, selection and functional diversity of the canine genome”⁠, Elaine A. Ostrander, Robert K. Wayne, Adam H. Freedman, Brian W. Davis

  29. ⁠, Stephen E. Harris, Jason Munshi-South (2017-09-26):

    Urbanization significantly alters natural ecosystems and has accelerated globally. Urban wildlife populations are often highly fragmented by human infrastructure, and isolated populations may adapt in response to local urban pressures. However, relatively few studies have identified genomic signatures of adaptation in urban animals. We used a landscape genomics approach to examine signatures of selection in urban populations of white-footed mice (Peromyscus leucopus) in New York City. We analyzed 154,770 SNPs identified from transcriptome data from 48 P. leucopus individuals from three urban and three rural populations, and used outlier tests to identify evidence of urban adaptation. We accounted for demography by simulating a neutral SNP dataset under an inferred demographic history as a null model for outlier analysis. We also tested whether candidate genes were associated with environmental variables related to urbanization. In total, we detected 381 outlier loci and after stringent filtering, identified and annotated 19 candidate loci. Many of the candidate genes were involved in metabolic processes, and have well-established roles in metabolizing lipids and carbohydrates. Our results indicate that white-footed mice in NYC are adapting at the biomolecular level to local selective pressures in urban habitats. Annotation of outlier loci suggest selection is acting on metabolic pathways in urban populations, likely related to novel diets in cities that differ from diets in less disturbed areas.

  30. 2017-chari.pdf: “Beyond editing to writing large genomes”⁠, Raj Chari, George M. Church

  31. 2017-normile.pdf: “Science Magazine”

  32. ⁠, Laura Spinney (2017-09-27):

    In the three decades since the first predictive genetic tests became available, a great deal of data has accumulated to show how people respond to knowing previously unknowable things. The rise of genetic testing has presented scientists with a 30-year experiment that has yielded some surprising insights into human behavior. The data suggest that the vast majority react in ways that at first seem counterintuitive, or at least flout what experts predicted. But as genetic testing becomes more widespread, the irrational behavior of a frightened few might start to look like the rational behavior of an enlightened majority. Doctors’ repeatedly failed attempts to anticipate people’s responses to genetic testing is not for want of preparation. Starting in the 1980s, they conducted surveys in which they asked how people might approach the test, were one available. They noted the answers and planned accordingly. The trouble was, when the test became a reality, their respondents didn’t do what they had said they would.

    …In those preparatory surveys, roughly 70% of those at risk of Huntington’s said they would take a test if it existed. In fact, only around 15% do—a proportion that has proved stable across countries and decades. A similar pattern emerged when tests became available for other incurable brain diseases…Prenatal genetic testing is widely available, but the uptake by expecting couples in which one partner is a known carrier of an incurable disease is even lower than that of testing among at-risk adults. Most opt to have a child whose risk of developing that disease is the same as theirs was at birth. Why do people act in this seemingly irresponsible way with respect to their offspring?

    and colleagues at the Pitié-Salpêtrière Hospital in Paris unpacks that decision-making process. They interviewed 54 women—either Huntington’s carriers or wives of carriers—and found that if a couple received a favorable result in a first prenatal test, the majority had the child and stopped there. Most of those who got an unfavorable result terminated the pregnancy and tried again. If a second prenatal test produced a “good” result, they had the child and stopped. But if it produced a “bad” result and another termination, most changed strategy. Some opted for preimplantation genetic diagnosis, removing the need for termination, since only mutation-free embryos are implanted. Some abandoned the idea of having a child altogether. But nearly half, 45%, conceived naturally again, and this time they did not seek prenatal testing. Summarizing the findings, the geneticist on the team, Alexandra Dürr, says, “The desire to have a child overrides all else.”

    …In a study that has yet to be published, Tibben has corroborated the French group’s conclusion. He followed 13 couples who, following counseling but to taking a prenatal test, agreed they would terminate in the case of an unfavorable result. None of them did so when they got that result. “That means there are 13 children alive in the Netherlands today, whom we can be 100% sure are [Huntington’s] carriers”, he says.

  33. ⁠, Chen Sun, Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta (2017-07-10):

    The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10× or 100×? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between ‘enormous data’ and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

  34. https://research.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html

  35. Scaling

  36. http://www.mitpressjournals.org/doi/pdf/10.1162/neco_a_00990

  37. ⁠, Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch (2017-09-13):

    Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent ⁠, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent’s policy on the anticipated parameter update of the other agents. Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners’ dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators. We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modelling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at github.com/​​​​alshedivat/​​​​lola.

  38. https://openai.com/blog/learning-to-model-other-minds/

  39. ⁠, Victor Shih, David C. Jangraw, Paul Sajda, Sameer Saproo (2017-09-14):

    Reinforcement Learning AI commonly uses reward/​​​​penalty signals that are objective and explicit in an environment—e.g. game score, completion time, etc.—in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective—e.g. human preferences for certain AI behavior—in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions. Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual’s level of interest in objects/​​​​events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novelly, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20% viewing time for subjectively interesting objects. This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.

  40. ⁠, Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, Peter Stone (2017-09-28):

    While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER’s success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling—a task that has proven difficult for even state-of-the-art reinforcement learning methods.

  41. {#linkBibliography-o’’donoghue-et-al-2017 .docMetadata}, Brendan O''Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih (2017-09-15):

    We consider the exploration/​​​​exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps.

    In this paper we consider a similar uncertainty Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the posterior distribution of the Q-values induced by any policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance.

    Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for ε-greedy improves performance on 51 out of 57 games in the Atari suite.

  42. ⁠, Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, Chunping Liu (2018-05-04):

    Github repo with screenshot samples of style2paints, a neural network for colorizing anime-style illustrations (trained on Danbooru2018), with or without user color hints, which was available as an online service in 2018. style2paints produces high-quality colorizations often on par with human colorizations. Many examples can be seen on Twitter or the repo:

    Example style2paints colorization of a character from Prison School

    style2paints has been described in more detail in ⁠, Zhang et al 2018:

    Sketch or line art colorization is a research field with substantial market demand. Different from photo colorization which strongly relies on texture information, sketch colorization is more challenging as sketches may not have texture. Even worse, color, texture, and gradient have to be generated from the abstract sketch lines. In this paper, we propose a semi-automatic learning-based framework to colorize sketches with proper color, texture as well as gradient. Our framework consists of two stages. In the first drafting stage, our model guesses color regions and splashes a rich variety of colors over the sketch to obtain a color draft. In the second refinement stage, it detects the unnatural colors and artifacts, and try to fix and refine the result.Comparing to existing approaches, this two-stage design effectively divides the complex colorization task into two simpler and goal-clearer subtasks. This eases the learning and raises the quality of colorization. Our model resolves the artifacts such as water-color blurring, color distortion, and dull textures.

    We build an interactive software based on our model for evaluation. Users can iteratively edit and refine the colorization. We evaluate our learning model and the interactive system through an extensive user study. Statistics shows that our method outperforms the state-of-art techniques and industrial applications in several aspects including, the visual quality, the ability of user control, user experience, and other metric

  43. http://www.jodrellbank.manchester.ac.uk/media/eps/jodrell-bank-centre-for-astrophysics/news-and-events/2017/uksrn-slides/Anders-Sandberg---Dissolving-Fermi-Paradox-UKSRN.pdf

  44. Pipeline

  45. ⁠, Nigel Dallow, Nicky Best, Timothy Montague (2017-08-31):

    With the continued increase in the use of in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/​​​​or our understanding may preclude direct construction of a data-based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations. Within GlaxoSmithKline (GSK), we have adopted a structured approach to prior elicitation based on the SHELF elicitation framework, and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones. The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate. As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company-wide decision making.

  46. https://www.bmj.com/content/355/bmj.i5432

  47. http://amstat.tandfonline.com/doi/full/10.1080/01621459.2016.1240079

  48. https://www.straighttalkonevidence.org/2017/09/22/disappointing-findings-on-conditional-cash-transfers-as-a-tool-to-break-the-poverty-cycle-in-the-united-states/

  49. https://pdfs.semanticscholar.org/4fb9/ff8e9affa466078ff5b6d23500f75d3e5079.pdf

  50. https://www.theatlantic.com/international/archive/2017/10/red-famine-anne-applebaum-ukraine-soviet-union/542610/

  51. https://tannerlectures.utah.edu/_documents/a-to-z/h/Hrdy_02.pdf

  52. https://www.econstor.eu/bitstream/10419/76612/1/cesifo_wp1173.pdf

  53. 2009-rey.pdf

  54. https://www.buzzfeed.com/mollyhensleyclancy/meet-the-young-saints-of-bethel-who-go-to-college-to

  55. http://www.stats.ox.ac.uk/~snijders/PadgettAnsell1993.pdf

  56. https://academic.oup.com/humupd/article-lookup/doi/10.1093/humupd/dmx022

  57. https://www.nytimes.com/2017/08/16/health/male-sperm-count-problem.html

  58. 2015-gaukler.pdf: ⁠, James S. Ruff, Tessa Galland, Kirstie A. Kandaris, Tristan K. Underwood, Nicole M. Liu, Elizabeth L. Young, Linda C. Morrison, Garold S. Yost, Wayne K. Potts (2015; statistics):

    is a selective serotonin reuptake inhibitor (SSRI) that is currently available on the market and is suspected of causing congenital malformations in babies born to mothers who take the drug during the first trimester of pregnancy.

    We utilized organismal performance assays (OPAs), a novel toxicity assessment method, to assess the safety of paroxetine during pregnancy in a rodent model. OPAs utilize genetically diverse wild mice (Mus musculus) to evaluate competitive performance between experimental and control animals as they compete amongst each other for limited resources in semi-natural enclosures. Performance measures included reproductive success, male competitive ability and survivorship.

    Paroxetine-exposed males weighed 13% less, had 44% fewer offspring, dominated 53% fewer territories and experienced a 2.5-fold increased trend in mortality, when compared with controls. Paroxetine-exposed females had 65% fewer offspring early in the study, but rebounded at later time points. In cages, paroxetine-exposed breeders took 2.3 times longer to produce their first litter and pups of both sexes experienced reduced weight when compared with controls. Low-dose paroxetine-induced health declines detected in this study were undetected in preclinical trials with dose 2.5-8 times higher than human therapeutic doses.

    These data indicate that OPAs detect phenotypic adversity and provide unique information that could useful towards safety testing during pharmaceutical development.

    [Keywords: intraspecific competition, pharmacodynamics, reproductive success, semi-natural enclosures, SSRI, toxicity assessment.]

  59. https://www.newyorker.com/magazine/2017/09/11/cancers-invasion-equation

  60. 2017-carharttharris.pdf: ⁠, R. L. Carhart-Harris, D. J. Nutt (2017-08-31; nootropic):

    Previous attempts to identify a unified theory of brain serotonin function have largely failed to achieve consensus. In this present synthesis, we integrate previous perspectives with new and older data to create a novel bipartite model centred on the view that serotonin neurotransmission enhances two distinct adaptive responses to adversity, mediated in large part by its two most prevalent and researched brain receptors: the 5-HT1A and 5-HT2A receptors. We propose that passive coping (ie. tolerating a source of stress) is mediated by postsynaptic 5-HT1AR signalling and characterised by stress moderation. Conversely, we argue that active coping (ie. actively addressing a source of stress) is mediated by 5-HT2AR signalling and characterised by enhanced plasticity (defined as capacity for change). We propose that 5-HT1AR-mediated stress moderation may be the brain’s default response to adversity but that an improved ability to change one’s situation and/​​​​or relationship to it via 5-HT2AR-mediated plasticity may also be important—and increasingly so as the level of adversity reaches a critical point. We propose that the 5-HT1AR pathway is enhanced by conventional 5-HT reuptake blocking antidepressants such as the selective serotonin reuptake inhibitors (SSRIs), whereas the 5-HT2AR pathway is enhanced by 5-HT2AR-agonist psychedelics. This bipartite model purports to explain how different drugs (SSRIs and psychedelics) that modulate the serotonergic system in different ways, can achieve complementary adaptive and potentially therapeutic outcomes.

  61. https://slatestarcodex.com/2017/10/10/ssc-journal-club-serotonin-receptors/

  62. 2017-campbell.pdf: ⁠, Jared M. Campbell, Susan M. Bellman, Matthew D. Stephenson, Karolina Lisy (2017-11-01; longevity):

    • Diabetics on have lower morality than non-diabetics and other diabetics.
    • Diabetics on metformin have less cancer than non-diabetics and other diabetics.
    • Diabetics on metformin have less cardiovascular disease than other diabetics.
    • Metformin appears to extend health and life spans independent of its effect on diabetes.
    • Metformin may be able to extend health and lifespans in the general population.

    This systematic review investigated whether the insulin sensitizer metformin has a geroprotective effect in humans.

    Pubmed and Embase were searched along with databases of unpublished studies. Eligible research investigated the effect of metformin on all-cause mortality or diseases of ageing relative to non-diabetic populations or diabetics receiving other therapies with adjustment for disease control achieved. Overall, 260 full-texts were reviewed and 53 met the inclusion criteria.

    Diabetics taking metformin had statistically-significantly lower all-cause mortality than non-diabetics (hazard ratio (HR) = 0.93, 95% 0.88–0.99), as did diabetics taking metformin compared to diabetics receiving non-metformin therapies (HR = 0.72, 95% CI 0.65–0.80), insulin (HR = 0.68, 95% CI 0.63–0.75) or sulphonylurea (HR = 0.80, 95% CI 0.66–0.97). Metformin users also had reduced cancer compared to non-diabetics (rate ratio = 0.94, 95% CI 0.92–0.97) and cardiovascular disease (CVD) compared to diabetics receiving non-metformin therapies (HR = 0.76, 95% CI 0.66–0.87) or insulin (HR = 0.78, 95% CI 0.73–0.83).

    Differences in baseline characteristics were observed which had the potential to bias findings, although statistical adjustments were made.

    The apparent reductions in all-cause mortality and diseases of ageing associated with metformin use suggest that metformin could be extending life and healthspans by acting as a geroprotective agent.

    [Keywords: metformin, ageing, insulin sensitizer, lifespan, longevity, geroprotection]

  63. http://jamanetwork.com/journals/jamapsychiatry/fullarticle/2649277

  64. 2017-kjaer.pdf

  65. https://folks.pillpack.com/my-father-the-werewolf/

  66. 2017-mukadam.pdf

  67. https://kuscholarworks.ku.edu/bitstream/1808/6468/1/Ryan_ku_0099D_10733_DATA_1.pdf

  68. 1949-lazarsfeld.pdf

  69. https://blog.archive.org/2017/10/10/books-from-1923-to-1941-now-liberated/

  70. 2017-gard.pdf: ⁠, Elizabeth Townsend Gard (2017-10-02; economics):

    [IA blog] Section 108(h) has not been utilized by libraries and archives, in part because of the uncertainty over definitions (eg. “normal commercial exploitation”), determination of the eligibility window (last 20 years of the copyright term of published works), and how to communicate the information in the record to the general public.

    This paper seeks to explore the elements necessary to implement the Last Twenty exception, otherwise known as Section 108(h) and create a Last Twenty (L20) collection. In short, published works in the last 20 years of the copyright may be digitized and distributed by libraries, archives, and museums, as long as there is no commercial sale of the works and no reasonably priced copy is available. This means that Section 108(h) is available for the forgotten and neglected works, 1923-1941, including millions of foreign works restored by ⁠. Section 108(h) is less effective for big, commercially available works.

    In many ways, that is the dividing line created by Section 108(h): allow for commercial exploitation of works throughout their term, but allow libraries to rescue works that had no commercial exploitation or copies available for sale and make them available through copying and distribution for research, scholarship, and preservation. In fact, Section 108(h) when it was being debated in Congress was called labeled This paper suggests ways to think about the requirements of Section 108(h) and to make it more usable for libraries. Essentially, by confidently using Section 108(h) we can continue to make the past usable one query at a time.

    The paper ends with an evaluation of the recent Discussion Paper by the U.S. Copyright Office on Section 108 and suggests changes/​​​​recommendations related to the proposed changes to Section 108(h).

    [Keywords: copyright, ⁠, library, archives, museum, Section 108(h), ⁠, orphan works]

  71. Copyright-deadweight

  72. https://eprint.iacr.org/2002/160.pdf

  73. http://leipper.org/manuals/zip-fill/safelocks_for_compscientist.pdf

  74. ⁠, Matt Blaze (2004-03-06):

    This position paper initiates and advocates the study of “Human-Scale Security Protocols” as a core activity of computing and network security research. The Human-Scale Security Protocols (HSSP) project treats “human scale” security problems and protocols as a central part of computer science. Our aim is to identify, stimulate research on, analyze, and improve “non-traditional” protocols that might either have something to teach us or be susceptible to improvement via the techniques and tools of computer security. There are compelling security problems across a wide spectrum of areas that do not outwardly involve computers or electronic communication and yet are remarkably similar in structure to the systems computer scientists routinely study. Interesting and relevant problem spaces that computer security has traditionally ignored range from the very serious (preventing terrorists from subverting aviation security) to the trivial and personal (ensuring that a restaurant serves the same wine that was ordered and charged for).

  75. https://www.theatlantic.com/science/archive/2017/10/benzene-tree-organic-compounds/530655/

  76. https://vfxblog.com/2017/08/23/the-tech-of-terminator-2-an-oral-history/

  77. https://www.theringer.com/movies/2021/6/30/22555687/terminator-2-judgement-day-t2-oral-history

  78. https://venturebeat.com/2017/10/01/globalfoundries-next-generation-chip-factories-will-cost-at-least-10-billion/view-all/

  79. http://lukemuehlhauser.com/industrial-revolution/

  80. 2020-bloom.pdf: ⁠, Nicholas Bloom, Charles I. Jones, John Van Reenen, Michael Webb (2020-04-01; economics):

    Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore’s Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas, and the exponential growth they imply, are getting harder to find.

  81. 2017-barrett.pdf

  82. https://www.rand.org/pubs/research_reports/RR2109.html

  83. ⁠, Timur Kuran (2018):

    This essay critically evaluates the analytic literature concerned with causal connections between Islam and economic performance. It focuses on works since 1997, when this literature was last surveyed. Among the findings are the following: Ramadan fasting by pregnant women harms prenatal development; Islamic charities mainly benefit the middle class; Islam affects educational outcomes less through Islamic schooling than through structural factors that handicap learning as a whole; Islamic finance hardly affects Muslim financial behavior; and low generalized trust depresses Muslim trade. The last feature reflects the Muslim world’s delay in transitioning from personal to impersonal exchange. The delay resulted from the persistent simplicity of the private enterprises formed under Islamic law. Weak property rights reinforced the private sector’s stagnation by driving capital out of commerce and into rigid waqfs. Waqfs limited economic development through their inflexibility and democratization by restraining the development of civil society. Parts of the Muslim world conquered by Arab armies are especially undemocratic, which suggests that early Islamic institutions, including slave-based armies, were particularly critical to the persistence of authoritarian patterns of governance. States have contributed themselves to the persistence of authoritarianism by treating Islam as an instrument of governance. As the world started to industrialize, non-Muslim subjects of Muslim-governed states pulled ahead of their Muslim neighbors by exercising the choice of law they enjoyed under Islamic law in favor of a Western legal system.

  84. http://jenniferdoleac.com/wp-content/uploads/2020/07/Anker_Doleac_Landerso_DNA.pdf

  85. https://academic.oup.com/jla/article/9/2/247/4430792

  86. https://www.rand.org/content/dam/rand/pubs/research_memoranda/2006/RM1829-1.pdf

  87. https://qualiacomputing.com/2016/08/20/wireheading_done_right/

  88. 1996-berman.pdf

  89. https://www.lesswrong.com/posts/Fy2b55mLtghd4fQpx/the-zombie-preacher-of-somerset

  90. https://lareviewofbooks.org/article/the-secret-history-of-dune/

  91. http://www.esquire.com/news-politics/a20903/hugh-hefner-interview-0413/

  92. https://www.amazon.com/Grand-Strategy-Roman-Empire-Century/dp/1421419459/

  93. Books#the-grand-strategy-of-the-roman-empire-luttwak-2016

  94. https://archive.org/details/originsoftheoret00prov

  95. https://www.amazon.com/Origins-Theoretical-Population-Genetics/dp/0226684644

  96. Books#moby-dick-or-the-whale-melville-2003

  97. Movies#cool-hand-luke

  98. Movies#listen-to-me-marlon

  99. https://www.dropbox.com/s/eb5x7gvhzkpqorr/%E5%B9%B3%E8%8C%B8-touhousixstring04%E5%84%9A-stardust%E5%90%91%E3%81%93%E3%81%86%E5%81%B4%E3%81%AE%E6%9C%88%E4%BB%96.ogg?dl=0

  100. https://www.youtube.com/watch?v=bRilMKHZ4lA

  101. https://hyperdimensionfumo.bandcamp.com/track/our-dreams-alight-here-as-we-board-this-train-towards-nowhere

  102. https://hyperdimensionfumo.bandcamp.com/track/how-precious-death-how-senseless-life

  103. https://www.dropbox.com/s/crxurt9f5qv4zyc/erisfeat.rin-perpetualstuff-voicesinmyheartpfver.ogg?dl=0

  104. https://www.youtube.com/watch?v=M_Nv7hri0WM

  105. https://www.dropbox.com/s/rpjudx7y61vge9k/harito-hexeherz-rootofroot.ogg?dl=0

  106. https://www.youtube.com/watch?v=RmNAFi_7Glk

  107. https://soundcloud.com/wavforme/casual-killer-fusion-device?in=wavforme/sets/back-to-summer-again

  108. https://leme.me/verah/mp3/?C92%20%28MP3%20V1%29/As%20Like%20Music%20%E2%80%94%20Moments%20Of%20Passage%20%5BMP3-V1%5D%5BC92%5D#trk1

  109. https://www.youtube.com/watch?v=PObvTgv2EUM

  110. https://www.youtube.com/watch?v=3xnYLHhfnq4