Common genetic variants associated with cognitive performance identified using the proxy-phenotype method

Rietveld, Cornelius A.; Esko, Tõnu; Davies, Gail; Pers, Tune H.; Turley, Patrick; Benyamin, Beben; Chabris, Christopher F.; Emilsson, Valur; Johnson, Andrew D.; Lee, James J.; Leeuw, Christiaan de; Marioni, Riccardo E.; Medland, Sarah E.; Miller, Michael B.; Rostapshova, Olga; Lee, Sven J. van der; Vinkhuyzen, Anna A. E.; Amin, Najaf; Conley, Dalton; Derringer, Jaime; Duijn, Cornelia M. van; Fehrmann, Rudolf S.N.; Franke, Lude; Glaeser, Edward L.; Hansell, Narelle K.; Hayward, Caroline; Iacono, William G.; Ibrahim-Verbaas, Carla A.; Jaddoe, Vincent W.V.; Karjalainen, Juha; Laibson, David; Lichtenstein, Paul; Liewald, David C.; Magnusson, Patrik K. E.; Martin, Nicholas G.; McGue, Matt; McMahon, George; Pedersen, Nancy L.; Pinker, Steven; Porteous, David J.; Posthuma, Daniëlle; Rivadeneira, Fernando; Smith, Blair H.; Starr, John M.; Tiemeier, Henning; Timpson, Nicholas J.; Trzaskowski, Maciej; Uitterlinden, André G.; Verhulst, Frank C.; Ward, Mary E; Wright, Margaret J.; Smith, George Davey; Deary, Ian J.; Johannesson, Magnus; Plomin, Robert; Visscher, Peter M.; Benjamin, Daniel J.; Cesarini, David; Koellinger, Philipp

doi:10.1073/pnas.1404623111

Cited by 248 publications

(278 citation statements)

References 23 publications

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“…Despite it being widely used in studies as a social-environmental variable, differences in education are under substantial genetic influence, with heritability frequently estimated at 60% and above in family studies (10)(11)(12), and 20-30% in molecular genetic studies (13,14). Some specific education-associated genetic variants have also been uncovered in genome-wide association studies (GWAS) (15)(16)(17). The present study uses previouslydiscovered genetic correlates of education to predict variation in arguably the most important life outcome of all: longevity.…”

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confidence: 99%

Genetic variants linked to education predict longevity

Marioni¹,

Ritchie²,

Joshi³

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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118

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Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members' polygenic profile score for education to predict their parents' longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total n deaths = 79,702) and ∼2.4% lower risk for fathers (total n deaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.genetics | education | longevity | prediction | polygenic score

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confidence: 99%

Genetic variants linked to education predict longevity

Marioni¹,

Ritchie²,

Joshi³

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

118

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“…These results strengthen the credibility of (most of) the original associations. For (ii), we use a "proxy phenotype" approach 6 : we treat the set of loci associated with SWB at p < 10 -4 as candidates, and we test them for association with DS and neuroticism. At the Bonferroni-adjusted 0.05 significance threshold, we identify two loci associated with both DS and neuroticism and another two associated with neuroticism.…”

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confidence: 99%

Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism

Okbay

Baselmans

Neve

et al. 2015

Preprint

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We conducted genome-wide association studies of three phenotypes: subjective wellbeing (SWB; N = 298,420), depressive symptoms (DS; N = 161,460), and neuroticism (N = 170,910). We identified three variants associated with SWB, two with DS, and eleven with neuroticism, including two inversion polymorphisms. The two DS loci replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (| | . ) strengthen the overall credibility of the findings, and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal/pancreas tissues are strongly enriched for association.Subjective well-being (SWB)-as measured by survey questions on life satisfaction, positive affect, or happiness-is a major topic of research within psychology, economics, and epidemiology. Twin studies have found that SWB is genetically correlated with depression (characterized by negative affect, anxiety, low energy, bodily aches and pains, pessimism, and other symptoms) and neuroticism (a personality trait characterized by easily experiencing negative emotions such as anxiety and fear) 1-3 . Depression and neuroticism have received much more attention than SWB in genetic-association studies, but the discovery of associated genetic variants with either of them has proven elusive 4,5 .In this paper, we report a series of separate and joint analyses of SWB, depressive symptoms (DS), and neuroticism. Our primary analysis is a genome-wide association study (GWAS) of SWB based on data from 59 cohorts (N = 298,420). This GWAS identifies three loci associated with SWB at genome-wide significance (p < 5×10 -8 ). We supplement this primary analysis with auxiliary GWAS meta-analyses of DS (N = 180,866) and neuroticism (N = 170,910), performed by combining publicly available summary statistics from published studies with new genome-wide analyses of additional data. In these auxiliary analyses we identify two loci associated with DS and eleven with neuroticism, including two inversion polymorphisms. In depression data from an independent sample (N = 368,890), both DS associations replicate (p = 0.004 and p = 0.015).In our two joint analyses, we exploit the high genetic correlation between SWB, DS, and neuroticism (i) to evaluate the credibility of the 16 genome-wide significant associations across the three phenotypes, and (ii) to identify novel associations (beyond those identified by the GWAS). For (i), we investigate whether our three SWB-associated SNPs "quasi-replicate" by testing them for association with DS and neuroticism. We similarly examine the quasi-replication record of the DS and neuroticism loci by testing them for association with SWB. We find that the quasi-replication record closely matches what would be expected given our statistical power if none of the genome-wide significant associations were chance findings. These results strengthen the credibility of (most of) the original associations. For (ii), we u...

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“…This process is simple if the pronunciation already exists in the memory. In case, the pronunciation doesn't exist in the memory, it enrols the new pronunciation in its memory and uses the same for future references [6]. This process is termed as unsupervised pronunciation adaptation [7].…”

Section: Introductionmentioning

confidence: 99%

Phonetic Distance Based Accent Classifier to Identify Pronunciation Variants and OOV Words

Babu

Ramadevi

Rao

2015

SIPIJ

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The state-of-the-art Automatic Speech Recognition (ASR) systems lack the ability to identify spoken words if they have non-standard pronunciations. In this paper, we present a new classification algorithm to identify pronunciation variants. It uses Dynamic Phone Warping (DPW) technique to compute the pronunciation-by-pronunciation phonetic distance and a threshold critical distance criterion for the classification. The proposed method consists of two steps; a training step to estimate a critical distance parameter using transcribed data and in the second step, use this critical distance criterion to classify the input utterances into the pronunciation variants and OOV words.The algorithm is implemented using Java language. The classifier is trained on data sets from TIMIT speech corpus and CMU pronunciation dictionary. The confusion matrix and precision, recall and accuracy performance metrics are used for the performance evaluation. Experimental results show significant performance improvement over the existing classifiers.

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Common genetic variants associated with cognitive performance identified using the proxy-phenotype method

Cited by 248 publications

References 23 publications

Genetic variants linked to education predict longevity

Genetic variants linked to education predict longevity

Genetic Associations with Subjective Well-Being Also Implicate Depression and Neuroticism

Phonetic Distance Based Accent Classifier to Identify Pronunciation Variants and OOV Words

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