Comparing Within- and Between-Family Polygenic Score Prediction

[1]  Salah Dauga.  Statistical Methods for Comparing Regression Coefficients Between Hybrid LMS and LMS Algorithms for Smart Antenna , 2021, NAECON 2021 - IEEE National Aerospace and Electronics Conference.

[2]  S. Choi,et al.  Tutorial: a guide to performing polygenic risk score analyses , 2020, Nature Protocols.

[3]  Alicia R. Martin,et al.  Predicting Polygenic Risk of Psychiatric Disorders , 2019, Biological Psychiatry.

[4]  J. Kaprio,et al.  Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases , 2019, bioRxiv.

[5]  R. Plomin,et al.  Polygenic score for educational attainment captures DNA variants shared between personality traits and educational achievement. , 2019, Journal of personality and social psychology.

[6]  B. Domingue,et al.  Genetic Nature or Genetic Nurture? Quantifying Bias in Analyses Using Polygenic Scores , 2019, bioRxiv.

[7]  D. Belsky,et al.  Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down , 2019, Current directions in psychological science.

[8]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[9]  R. Plomin,et al.  Genomic prediction of cognitive traits in childhood and adolescence , 2018, bioRxiv.

[10]  P. O’Reilly,et al.  Evidence for gene-environment correlation in child feeding: Links between common genetic variation for BMI in children and parental feeding practices , 2018, bioRxiv.

[11]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[12]  D. Gudbjartsson,et al.  Relatedness disequilibrium regression estimates heritability without environmental bias , 2018, Nature Genetics.

[13]  Jason M. Fletcher,et al.  Genetic analysis of social-class mobility in five longitudinal studies , 2018, Proceedings of the National Academy of Sciences.

[14]  G. Coop,et al.  Reduced signal for polygenic adaptation of height in UK Biobank , 2018, bioRxiv.

[15]  P. O’Reilly,et al.  Using genetic data to strengthen causal inference in observational research , 2018, Nature Reviews Genetics.

[16]  Christopher M. DeBoever,et al.  Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics , 2018, bioRxiv.

[17]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[18]  Timothy C. Bates,et al.  The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children's Educational Attainment in Genotyped Families , 2018, Twin Research and Human Genetics.

[19]  Jakob Grove,et al.  Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection , 2018, Nature Genetics.

[20]  John X. Morris,et al.  Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection , 2018, Nature Genetics.

[21]  P. Koellinger,et al.  Using nature to understand nurture , 2018, Science.

[22]  Robert Plomin,et al.  The new genetics of intelligence , 2018, Nature Reviews Genetics.

[23]  Ian J. Deary,et al.  Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism , 2017, Nature Genetics.

[24]  Bjarni V. Halldórsson,et al.  The nature of nurture: Effects of parental genotypes , 2017, Science.

[25]  R. Marioni,et al.  Molecular Genetic Contributions to Social Deprivation and Household Income in UK Biobank , 2016, Current Biology.

[26]  T. Banaschewski,et al.  Self-report of ADHD shows limited agreement with objective markers of persistence and remittance , 2016, Journal of psychiatric research.

[27]  T. Esko,et al.  Educational Attainment and Personality Are Genetically Intertwined , 2016, bioRxiv.

[28]  R. Plomin,et al.  Predicting educational achievement from DNA , 2016, Molecular Psychiatry.

[29]  D. Belsky,et al.  The Genetics of Success , 2016, Psychological science.

[30]  Stuart J. Ritchie,et al.  Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia , 2015, Molecular Psychiatry.

[31]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[32]  R Plomin,et al.  Phenome-wide analysis of genome-wide polygenic scores , 2015, Molecular Psychiatry.

[33]  M. Barreto,et al.  Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis , 2015, Environmental Health.

[34]  D. Belsky,et al.  Polygenic Influence on Educational Attainment , 2015, AERA open.

[35]  R. Plomin,et al.  Genetic link between family socioeconomic status and children's educational achievement estimated from genome-wide SNPs , 2015, Molecular Psychiatry.

[36]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[37]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[38]  Kathryn Asbury,et al.  The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence , 2014, Proceedings of the National Academy of Sciences.

[39]  N. Wray,et al.  Research review: Polygenic methods and their application to psychiatric traits. , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[40]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

[41]  Common genetic variants associated with cognitive performance identified using the proxy-phenotype method , 2014, Proceedings of the National Academy of Sciences.

[42]  John P. Rice,et al.  Polygenic scores associated with educational attainment in adults predict educational achievement and ADHD symptoms in children , 2014, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[43]  G. Davey Smith,et al.  Mendelian randomization: genetic anchors for causal inference in epidemiological studies , 2014, Human molecular genetics.

[44]  Peter M Visscher,et al.  Large-scale genomics unveils the genetic architecture of psychiatric disorders , 2014, Nature Neuroscience.

[45]  E. Tucker-Drob,et al.  Achievement-Relevant Personality: Relations with the Big Five and Validation of an Efficient Instrument. , 2014, Learning and individual differences.

[46]  R. Plomin,et al.  Genetic influence on family socioeconomic status and children's intelligence , 2014, Intelligence.

[47]  R. Plomin,et al.  Strong Genetic Influence on a UK Nationwide Test of Educational Achievement at the End of Compulsory Education at Age 16 , 2013, PloS one.

[48]  J. Bjørngaard,et al.  Adolescent Health and High School Dropout: A Prospective Cohort Study of 9000 Norwegian Adolescents (The Young-HUNT) , 2013, PloS one.

[49]  R. Plomin,et al.  Literacy and Numeracy Are More Heritable Than Intelligence in Primary School , 2013, Psychological science.

[50]  P. Visscher,et al.  Pitfalls of predicting complex traits from SNPs , 2013, Nature Reviews Genetics.

[51]  Guido Alessandri,et al.  Academic Achievement: The Unique Contribution of Self-Efficacy Beliefs in Self-Regulated Learning beyond Intelligence, Personality Traits, and Self-Esteem. , 2013 .

[52]  R. Plomin,et al.  Twins Early Development Study (TEDS): A Genetically Sensitive Investigation of Cognitive and Behavioral Development From Childhood to Young Adulthood , 2012, Twin Research and Human Genetics.

[53]  R. Tremblay,et al.  Childhood trajectories of inattention and hyperactivity and prediction of educational attainment in early adulthood: a 16-year longitudinal population-based study. , 2011, The American journal of psychiatry.

[54]  Jason M. Fletcher.  The promise and pitfalls of combining genetic and economic research. , 2011, Health economics.

[55]  J. Pais-Ribeiro,et al.  The Role of Positive Psychology Constructs in Predicting Mental Health and Academic Achievement in Children and Adolescents: A Two-Year Longitudinal Study , 2011 .

[56]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[57]  P. Visscher,et al.  Family-based genome-wide association studies. , 2009, Pharmacogenomics.

[58]  George Davey Smith,et al.  Capitalizing on Mendelian randomization to assess the effects of treatments , 2007 .

[59]  Robert Plomin,et al.  Predicting school achievement from general cognitive ability, self-perceived ability, and intrinsic value. , 2006 .

[60]  T. Widiger,et al.  Psychometric Properties of an Abbreviated Instrument of the Five-Factor Model , 2006, Assessment.

[61]  H. Ellis,et al.  The Cardiff Anomalous Perceptions Scale (CAPS): a new validated measure of anomalous perceptual experience. , 2006, Schizophrenia bulletin.

[62]  John B Carlin,et al.  Regression models for twin studies: a critical review. , 2005, International journal of epidemiology.

[63]  S. Ebrahim,et al.  'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.

[64]  P. Sham,et al.  A note on the calculation of empirical P values from Monte Carlo procedures. , 2002, American journal of human genetics.

[65]  Pak Chung Sham,et al.  Analytic approaches to twin data using structural equation models , 2002, Briefings Bioinform..

[66]  A. Piquero,et al.  USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS , 1998 .

[67]  R. Goodman.  The Strengths and Difficulties Questionnaire: a research note. , 1997, Journal of child psychology and psychiatry, and allied disciplines.

[68]  C. Clogg,et al.  Statistical Methods for Comparing Regression Coefficients Between Models , 1995, American Journal of Sociology.

[69]  R. Plomin.  Genetics and Experience: The Interplay between Nature and Nurture , 1994 .

[70]  C. Sherbourne,et al.  The MOS 36-Item Short-Form Health Survey (SF-36) , 1992 .

[71]  N. Andreasen.  The Scale for the Assessment of Negative Symptoms (SANS): Conceptual and Theoretical Foundations , 1989, British Journal of Psychiatry.

[72]  R Plomin,et al.  Genotype-environment interaction and correlation in the analysis of human behavior. , 1977, Psychological bulletin.

[73]  O. Kempthorne.  The correlation between relatives on the supposition of mendelian inheritance , 1968 .

[74]  Stuart J. Ritchie,et al.  Molecular genetic contributions to self-rated health , 2015, bioRxiv.

[75]  P. Visscher,et al.  Meta-analysis of the heritability of human traits based on fifty years of twin studies. , 2015, Nature genetics.

[76]  George Davey Smith,et al.  Capitalizing on Mendelian randomization to assess the effects of treatments. , 2007, Journal of the Royal Society of Medicine.

[77]  S. Ebrahim,et al.  Education and debate , 2005 .

[78]  C. Conners.  Conners' rating scales-revised : technical manual , 1997 .

[79]  A. Jensen,et al.  Genetics and experience: The interplay between nature and nurture , 1996 .

[80]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[81]  A. Fenigstein,et al.  Paranoia and self-consciousness. , 1992, Journal of personality and social psychology.

[82]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[83]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[84]  Edinburgh Research Explorer Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence , 2022 .