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Metabolomics_Metabolomics_README/index.html

@@ -1581,14 +1581,14 @@ <h2 id="summary-table">Summary Table</h2>
 <tr>
 <th style="text-align: left;">NAME</th>
 <th style="text-align: left;">CITATION</th>
-<th style="text-align: right;">YEAR</th>
+<th style="text-align: left;">YEAR</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align: left;"><a href="#a-table-of-all-published-gwas-with-metabolomics">A Table of all published GWAS with metabolomics</a></td>
-<td style="text-align: left;">Kastenmüller G, Raffler J, Gieger C, Suhre K. (2015) Genetics of human metabolism: an update Hum. Mol. Genet., 24 (R1) R93-R101. doi:10.1093/hmg/ddv263. PMID 26160913</td>
-<td style="text-align: right;">2015</td>
+<td style="text-align: left;">NA</td>
+<td style="text-align: left;">NA</td>
 </tr>
 </tbody>
 </table>
@@ -1597,11 +1597,6 @@ <h2 id="a-table-of-all-published-gwas-with-metabolomics">A Table of all publishe
 <li><strong>NAME</strong> : A Table of all published GWAS with metabolomics </li>
 <li><strong>DESCRIPTION</strong> : This table was initially published in Kastenmüller et al., Genetics of human metabolism: an update. Hum. Mol. Genet. 2015 and has been updated as of 23 April 2024. </li>
 <li><strong>URL</strong> : <a href="http://www.metabolomix.com/list-of-all-published-gwas-with-metabolomics/"> http://www.metabolomix.com/list-of-all-published-gwas-with-metabolomics/</a> </li>
-<li><strong>TITLE</strong> : Genetics of human metabolism: an update </li>
-<li><strong>DOI</strong> : 10.1093/hmg/ddv263 </li>
-<li><strong>ABSTRACT</strong> : Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies. </li>
-<li><strong>CITATION</strong> : Kastenmüller G, Raffler J, Gieger C, Suhre K. (2015) Genetics of human metabolism: an update Hum. Mol. Genet., 24 (R1) R93-R101. doi:10.1093/hmg/ddv263. PMID 26160913 </li>
-<li><strong>JOURNAL_INFO</strong> : Human molecular genetics ; Hum. Mol. Genet. ; 2015 ; 24 ; R1 ; R93-R101 </li>
 <li><strong>PUBMED_LINK</strong> : <a href="https://pubmed.ncbi.nlm.nih.gov/26160913">26160913</a> </li>
 </ul>
 

Population_Genetics_Admixture_README/index.html

@@ -1599,19 +1599,19 @@ <h2 id="summary-table">Summary Table</h2>
 <tr>
 <th style="text-align: left;">NAME</th>
 <th style="text-align: left;">CITATION</th>
-<th style="text-align: right;">YEAR</th>
+<th style="text-align: left;">YEAR</th>
 </tr>
 </thead>
 <tbody>
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 <td style="text-align: left;"><a href="#admixture">ADMIXTURE</a></td>
-<td style="text-align: left;">Alexander DH, Novembre J, Lange K. (2009) Fast model-based estimation of ancestry in unrelated individuals Genome Res., 19 (9) 1655-1664. doi:10.1101/gr.094052.109. PMID 19648217</td>
-<td style="text-align: right;">2009</td>
+<td style="text-align: left;">Alexander, D. H., Novembre, J., &amp; Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome research, 19(9), 1655-1664.</td>
+<td style="text-align: left;">NA</td>
 </tr>
 <tr>
 <td style="text-align: left;"><a href="#openadmixture">OpenADMIXTURE</a></td>
-<td style="text-align: left;">Ko S, Chu BB, Peterson D, Okenwa C, ...&amp;, Lange KL. (2023) Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets Am. J. Hum. Genet., 0 (0) . doi:10.1016/j.ajhg.2022.12.008. PMID 36610401</td>
-<td style="text-align: right;">2023</td>
+<td style="text-align: left;">Ko, S., Chu, B. B., Peterson, D., Okenwa, C., Papp, J. C., Alexander, D. H., ... &amp; Lange, K. L. (2023). Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets. The American Journal of Human Genetics.</td>
+<td style="text-align: left;">NA</td>
 </tr>
 </tbody>
 </table>
@@ -1622,11 +1622,7 @@ <h2 id="admixture">ADMIXTURE</h2>
 <li><strong>FULL NAME</strong> : ADMIXTURE </li>
 <li><strong>DESCRIPTION</strong> : ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm. </li>
 <li><strong>URL</strong> : <a href="https://dalexander.github.io/admixture/">https://dalexander.github.io/admixture/</a> </li>
-<li><strong>TITLE</strong> : Fast model-based estimation of ancestry in unrelated individuals </li>
-<li><strong>DOI</strong> : 10.1101/gr.094052.109 </li>
-<li><strong>ABSTRACT</strong> : Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies. © 2009 by Cold Spring Harbor Laboratory Press. </li>
-<li><strong>CITATION</strong> : Alexander DH, Novembre J, Lange K. (2009) Fast model-based estimation of ancestry in unrelated individuals Genome Res., 19 (9) 1655-1664. doi:10.1101/gr.094052.109. PMID 19648217 </li>
-<li><strong>JOURNAL_INFO</strong> : Genome research ; Genome Res. ; 2009 ; 19 ; 9 ; 1655-1664 </li>
+<li><strong>CITATION</strong> : Alexander, D. H., Novembre, J., &amp; Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome research, 19(9), 1655-1664. </li>
 <li><strong>PUBMED_LINK</strong> : <a href="https://pubmed.ncbi.nlm.nih.gov/19648217">19648217</a> </li>
 </ul>
 <h2 id="openadmixture">OpenADMIXTURE</h2>
@@ -1636,11 +1632,7 @@ <h2 id="openadmixture">OpenADMIXTURE</h2>
 <li><strong>FULL NAME</strong> : OpenADMIXTURE </li>
 <li><strong>DESCRIPTION</strong> : This software package is an open-source Julia reimplementation of the ADMIXTURE package. It estimates ancestry with maximum-likelihood method for a large SNP genotype datasets, where individuals are assumed to be unrelated. </li>
 <li><strong>URL</strong> : <a href="https://github.com/OpenMendel/OpenADMIXTURE.jl">https://github.com/OpenMendel/OpenADMIXTURE.jl</a> </li>
-<li><strong>TITLE</strong> : Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets </li>
-<li><strong>DOI</strong> : 10.1016/j.ajhg.2022.12.008 </li>
-<li><strong>ABSTRACT</strong> : SummaryAdmixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free. </li>
-<li><strong>CITATION</strong> : Ko S, Chu BB, Peterson D, Okenwa C, ...&amp;, Lange KL. (2023) Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets Am. J. Hum. Genet., 0 (0) . doi:10.1016/j.ajhg.2022.12.008. PMID 36610401 </li>
-<li><strong>JOURNAL_INFO</strong> : American journal of human genetics ; Am. J. Hum. Genet. ; 2023 ; 0 ; 0 ; <NA> </li>
+<li><strong>CITATION</strong> : Ko, S., Chu, B. B., Peterson, D., Okenwa, C., Papp, J. C., Alexander, D. H., ... &amp; Lange, K. L. (2023). Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets. The American Journal of Human Genetics. </li>
 <li><strong>PUBMED_LINK</strong> : <a href="https://pubmed.ncbi.nlm.nih.gov/36610401">36610401</a> </li>
 </ul>
 

Population_Genetics_Phylogenetic_tree_README/index.html

@@ -1581,14 +1581,14 @@ <h2 id="summary-table">Summary Table</h2>
 <tr>
 <th style="text-align: left;">NAME</th>
 <th style="text-align: left;">CITATION</th>
-<th style="text-align: right;">YEAR</th>
+<th style="text-align: left;">YEAR</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align: left;"><a href="#treemix">TreeMix</a></td>
-<td style="text-align: left;">Pickrell JK, Pritchard JK. (2012) Inference of population splits and mixtures from genome-wide allele frequency data PLoS Genet., 8 (11) e1002967. doi:10.1371/journal.pgen.1002967. PMID 23166502</td>
-<td style="text-align: right;">2012</td>
+<td style="text-align: left;">Pickrell, J., &amp; Pritchard, J. (2012). Inference of population splits and mixtures from genome-wide allele frequency data. Nature Precedings, 1-1.</td>
+<td style="text-align: left;">NA</td>
 </tr>
 </tbody>
 </table>
@@ -1597,11 +1597,7 @@ <h2 id="treemix">TreeMix</h2>
 <li><strong>NAME</strong> : TreeMix </li>
 <li><strong>SHORT NAME</strong> : TreeMix </li>
 <li><strong>URL</strong> : <a href="https://bitbucket.org/nygcresearch/treemix/wiki/Home">https://bitbucket.org/nygcresearch/treemix/wiki/Home</a> </li>
-<li><strong>TITLE</strong> : Inference of population splits and mixtures from genome-wide allele frequency data </li>
-<li><strong>DOI</strong> : 10.1371/journal.pgen.1002967 </li>
-<li><strong>ABSTRACT</strong> : Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com. </li>
-<li><strong>CITATION</strong> : Pickrell JK, Pritchard JK. (2012) Inference of population splits and mixtures from genome-wide allele frequency data PLoS Genet., 8 (11) e1002967. doi:10.1371/journal.pgen.1002967. PMID 23166502 </li>
-<li><strong>JOURNAL_INFO</strong> : PLoS genetics ; PLoS Genet. ; 2012 ; 8 ; 11 ; e1002967 </li>
+<li><strong>CITATION</strong> : Pickrell, J., &amp; Pritchard, J. (2012). Inference of population splits and mixtures from genome-wide allele frequency data. Nature Precedings, 1-1. </li>
 <li><strong>PUBMED_LINK</strong> : <a href="https://pubmed.ncbi.nlm.nih.gov/23166502">23166502</a> </li>
 </ul>