{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Linear regression example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial demonstrates the application of linear regression to test for QTL associations." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#set up plotting ebvironment\n", "%matplotlib inline\n", "from setup import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we load the yeast cross dataset." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#import data\n", "#the data used in this study have been pre-converted into an hdf5 file. \n", "#to preprocess your own data, please use limix command line brinary\n", "file_name = tutorial_data.get_file('BYxRM')\n", "geno_reader = gr.genotype_reader_tables(file_name)\n", "pheno_reader = phr.pheno_reader_tables(file_name)\n", "\n", "#the data object allows to query specific genotype or phenotype data\n", "dataset = data.QTLData(geno_reader=geno_reader,pheno_reader=pheno_reader)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#the data object allows to query specific genotype or phenotype data\n", "dataset = data.QTLData(geno_reader=geno_reader,pheno_reader=pheno_reader)\n", "#getting genotypes\n", "snps = dataset.getGenotypes() #SNPs\n", "position = dataset.getPos()\n", "position['chrom']\n", "position,chromBounds = data_util.estCumPos(position=position,offset=0)\n", "\n", "#phenotype\n", "P_max = 3\n", "phenotype_ID = dataset.phenotype_ID[0:P_max]\n", "phenotype_vals, sample_idx = dataset.getPhenotypes(phenotype_ID)\n", "\n", "N = snps.shape[0] #number of individuals\n", "S = snps.shape[1] #number of SNPs\n", "P = phenotype_vals.shape[1]#number of phenotypes\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normal distributed phenotypes and phenotype transformations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To explore the phenotypic data, we create a histogram of the phenotype values." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Uk3OaH57jDPYu/642ou3zJ5GaKZFLsKI66nOBQcCdwKWNOnYG7gdmRrNnGows/Y5OlMel\nXpTIJWTfAbYBFgNnVjnYVRJ+g4/LMgg4y7z3s0jDKZFLkMzbh08C3gFOzsAbjY4h+sMxAx8VdDQ+\nnLNIwymRS3DMh07ODd72w4w/vSoVGXgdry/vBI4Yn1YgIg2iCkGpudWKQT+DS6IWI+cUaP7Xyzhq\nHhbg8wZzbwfbgH9X+TnVakVKUqsVaTlHADsDK4BzUqgXL+YqYO4Q4BTOoXnCEkmWzmypqURqsLXB\ng1FpfK9k46i9FGyw+T1gWdptP25TiVySohK5tAbzhtjfw1uGXB81/2sqGVh+fvT6ZM5lQ16uYW+F\n2p0XHXhL+jglcgnFkXjLkOX44wWb0g3Ag+zB+rzKNGbQ+4J1oXbnnesmE6VI7+lSUXpVtWAw3Hwg\nrLnW8+HfCcWRWHWGbcILdjd7W5Z225c7aqhaUXWLAKpakdCZt0qZCqwN3JyBh5Pcf5ZdyLJLkrvk\nJYZyIScA8G1+xLqoRkTqS4lcmt1EvBT+KnB+mW2bxg0cyqPsxEa8zPH8LO1wpMUpkUvTMlgf+FY0\ne0EG/pNmPNUw+jGDaXTSxie5np3TDkhamhK5NLOTgA2AucBNKcdStUVsya+iUW6nAgN5J92ApGUp\nkUtTimqtPwGsAs5uoo4/VbmCo1nEKEYBX+zxVDiRZCiRS9MZyDuc2jV7WQaeSy+a2rzLQGYwDYCj\nuYKtWJhyRCK1CbJEJYmqqJndV5hlWZ+51mBAY+JIrvlhoX1PjXp8XsYxluE9NT+UajTV995UwUgq\nyiasrVhgc/hILpGPbVwc9U3kg8FuYX/L0m6f5A9K5FINtSOXcGRYzTRm0EYnfwAy8FjaMSXldeA8\npgBwAheyESvSDUhaihK5NI3D+CNjeJwVbMxM+gOFno0W7ngjf2Zf7mcvBvM6Uzgv7XBEekWXhVK0\nCmFjXlrTrX0f7ixT5VCPOBqxb7NNWW73spdlabe9uFdVK1IJVa1IGKZwHoN5nfsYz13sk3Y4dfMC\nm3ERXwV8hMR1Uo5H+paJwDz8kVonF1j/Obw+83HgAWBMgW1UmpCCpczx3GNZ2u1e9rKhPF/BTcDk\n4sjSblnaG1YiB7N+dNpv+LxlabdvqEQu5SXyvfcHFgCj8KZgjwLb522zO96dGjzpP1SvYCRoPZLT\nOmB/4iDL0m5H8NsKW3MkF0caiRzMPsDT9jC72sNg2/OUErmUkkjVyjg8kS8C3gWuwZ9eHjcHH9QI\nfHS64RWHKH3aV4BNeYGn2Z5r+Uza4TTMfEZzFUfSD5jGDPrxXtohScAqSeTDgCWx+aXRsmKOBW6u\nJSjpG0bzNJ8FVkcDTK32lip9xsVM5nngA8znSK5KOxwJWCWJvJrLuQnAMRSuRxdZox/vRSVRuJoj\nmM/otENquLdZh7Oj15O5mM1Zlmo8Eq62CrZZBoyIzY/AS+X5xgCX4nXkxYYbnR57PTuapA86gqsZ\nzTyex0umfdUc4Db2Z39u42TO5UR+ij9LQ/qwjmhKVBuwEL/ZOZDCNztH4vXou5XYj27U9BltK6FQ\nZx6/gTeSRXY/e1iWdtujqlYbzdSOvPRnrObzbMgKu4sOy9Ju+3GbbnZKvsS+9wOA+XiynhotmxxN\nAJcBLwOPRNNf6hmMNL2iSasfnXYZx1iWdjuDM3qV+OoZXzL7qH75JK63LO12G/vZeryiRC5xTfW9\nN1UwUldFk9YR/NaytNst7G/r8qoSebQ8w3t2MV+2LO12JtOUyCWuqb73pgpG6qpg0hoJa6pUxnNP\nDYmvPvFVue9EEzmYDWex3cee0TAFSuSyRtnvXV30pSH68R7fBdbiHf6Pg7iPj6YdUtNZygh+yomA\n119uyMvpBiRSgEoTfUeP0uR/82vLQqxKpZYSbPLxNUOJ3KfVNpPjLAv2E04yWK0SuTTV995UwUhd\ndUtO2/PUmodF7MH9CSS+5OKroYt+nRK52Sa8YHfjTxT6BDcqkYuqViRd6/AmP+A02ujkWuBB9kw7\npKb3EkM5N3o9hfMYweJU45Hmp0QudfUtfsxIFrOAbbgw7WACcitwB/sxiDc5i1MZwKq0Q5ImpkQu\ndbMvdzKJG1nFQKYxQ6moSjOYxlKGM5p5UY9PkcKUyKUutmARp/N9AC7gJP7J1ilHFJ43GMypnEUn\nbXyGa5mQdkDStJTIJXHrAD/i2wziTW7n41zH4WmHFKyn2YELOAmA0wHTENGSMt1x7wMMMmdFLS6u\n5XBbhzfKtNyotpVHW671Rt5U1UOZm7j5YaHlq+2HTLGsz1xr8L4q4pTwNVXubKpgpD4MjsqCzeaj\ntgXPVpC0kkx81YQZUiI3G8Tr9jufmWvwY9PVdF/SVLmzqYKR5BnsY5DNgk3gzxUmLSXySpcP9xd3\nRcn8uCpiTZzBSINzDH5ocLvBp9KMp8U1Ve5sqmAkWQY7GtxvMPcLVSUtJfLKl7fZrmAPgWXBDqZX\n1Uo1M+hn8AuLnmdgsKXBG4bGXaiTpsqdTRWMJMdgmMFtUUnxuyiR12m5Lzucay1Luz3EONuLe6uN\nu5IPdoh1PUy90PoPGPzVYo98NLjH4Mok45A1yn6/qmeTmhhsAswCNgKysObpZVIn1/FpLueL9Oc9\nzmYqY5I/xIeBDUqsfw1/mMxmsWUrgA2TD0UqoUQuvWb+izsLL5n9HZiSgc50o6pOll3IskvaYVRt\nFsdxI5NYm7c5HzB6PvTU4LNGV08igwlG7U95zsDyDLw/A3Oj/WaAscC9sWMdavCkwXvmI3+9a7DK\n4MBajy89KZFLr0RJ/Of4IwAXAMdn4I1Ug+pTMpzFqcymg/V8wUUGO+ZtdCj+aMacg4Bn6xDMJ4DV\nwAUA5jc+T4mOvxHwV+CgDAzMwM11OH6fp0QuVTPYFH/Q9rbAIuBrGXg11aD6oNX0Zypnc7fPrgv8\n3OhW0zIeuC82vydwf5IxRH/QvwscnIF3DAYBPwT+KwMLMvAKcAdeXSN10pZ2ABIWgy3wkvimwD/w\nkriegJCSTgYwFfg23Al8DJhlcCrwPNAv41dLmHe43Ql4IP5+87rwH9O9ULcT8EGD12PLnsjAT/Le\n2x8vhX8+4+cCeOn86QwsjW26BZ7MpQWo1UrgDMZZVzvmX5qXAotsGkarlcrGI29bGa3Lm9JptVJo\nuUF/gzOi7+Yv5s0D11RjGHRYVM1i0FHmh3OG+c3Mcj/E0w22j80fZXCKwTmxZWsbLDSvYpHeKXtu\nq0QuZUU3sw4HvoWXwu4BTsvAW6kG1jCd6/b8XcqkEkkxGb+p+H1gOTAZ/76WmddLrwKOBf5mflN0\nncp2WZzBl/F68S2iq7R+ePL/G14qzzkZuEBXbfWlRC4lRaXuU4D9o0WXAxdlYDtg3wJvea9RsSVh\nV2940RIy/tfmUoNlwPV4gn3A4P+AXwBT8AR/Vi3HMfgAMBMY0H0xX8vAzQbtBt/Ex4R5PgOX1HI8\nKU+JXIoyb1L2A7y98FvAjIw/8wBYezKM+TqMzWtu+HvgPw2NU9qAzjWXDFsCjwH3Qr+DvWTdgbcc\nOSyTQBVnBuYDa5VYf2atx5DmpTryQBgMNjjZIBvVuV7Zs8507Qt8mI38+toxrxBQHXmF+2hwXXht\nP6ujuMJu4BAb7AvOi77DuQY/s+6deIp92KNUp91Uyp7ban4oa5jfMDsYL1YfjleTXA4ck6HZHxyZ\nG/YjPjV2DJJmsT1PcwOH5pqcfBsfynwlsDtwncFXzJsJFpSB36hOW4pRibxJmQ+CNMHgd7HS2y8N\ntin+rmYskddSwm6dEnmhz2OwkcHZse/3VoNPG6xd+ZkiKSmbO1VH3odFv8QHAkfiPTTBWz1cDNyS\n8VYJ0gKiEvZUg2uAbwAfBL4DfDla9seMbm4ES4m8jzG/+TUGOADYj65R7l4CrgBuiJqr5bafC5Ah\nwAFJ8m4CFpIbZ8Vbr5TfPjT5318GHjP4IjAB+ALerf+reEK/F7gReDi0MXP6ukoS+US891Z/4DLg\n3ALbXIgnhjeBo4FHEopPEhCVvNuBPfBu25vHVj+FD6T059b75e2k8FVpsSbS1W4fpqjlyl0Gd+MJ\n/ki8+/4+0fSaeQ/Q2UBWwy80v3KJvD/eXvRjeNvULPC/wNOxbQ7E61K3BT4CXATslnikza8DP/FT\nFZW4N8FLWh8EPhS9Hhjb7CW8GeGtwDMVNknroAk+Xz38ldfSDqGubirSAzf63rN466T344NqHQhs\nhRfgJgKYd/N/BHgc74r/XJP90e+gRc/NSpVL5OPwL3FRNH8NMInuifwQ4NfR64fxsRuGAi8mFmUY\nOmjAyRQl6vfhyXrj6P+heOePUdFUqEXC34EHgTnAk5nqO+500KK/LK2eyO/qkcjbVnpv1S4Z2l6D\nzvWAK8ybmu6NX719CC+obYO3ZAKvf/onnheexwt5zwMv4PXsKxt8f6WDFj03K1UukQ8DlsTml+Kl\n7nLbDCelRG4whO5VB5nY/xm6XyeXW5fJ267QthmAnWDEo355Wuh9+ccZEE0DC7zO/b82MDia1o29\nXo/yLQ1eAeYBT+amaBQ6EYoMObAmsUdNTa/E+w+sBeyAj164A57Qh+M9e7crcoDV5tUxr0TTm3iH\nstz/uWkVXrIvNb1Hj2alEJ/fETZ7CnbG/3j0WE9lV5w5lWz774z/0Woa5RJ5pT+A/ErENG8YdQDT\nGn3QffyPx9gGHe4tvHpkBfAv/I/mYryE9FxjkvbqTrjobbjlne7LF6g5W9NoYy6d7VF78l79Tmbg\nHbxaZc19L/Mrwq2AEXgHo82jaSh+Rb4eXqAa0vvYK7e/H7u9EceKXEfhe4WpKXcXZzdgOlFdGTAV\n/6sX/xC/wC9rron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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[3,3])#create the figure\n", " \n", " #histogram of the untransformed phenotypes\n", " plot_normal(phenotype_vals.values[:,ip],alpha=0.8)\n", " pl.title(\"%s\" % p_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the phenotypes deviate from a normal distribution.\n", "One of the assumptions of the linear regression model we use for association testing is that the model residuals are normal distrbuted.\n", "Violation of this assumption leads to biases in the analysis.\n", "We only have access to the residuals after fitting the model.\n", "Under the assumption that the model eplains only a small portion of phenotypic variation we can assess the phenotype values instead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming phenotypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make the data look more normal distrbuted, we apply two different phenotype transformations, the Box-Cox transformation and a non-parametric rank-based transformation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Box-Cox transformation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Box-Cox transformation makes the data \"more normal\" by fitting a power transformation with one parameter to the observed phenotypic data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phenotype_vals_boxcox, maxlog = preprocess.boxcox(phenotype_vals.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Rank-based (non-parametric) transformation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rank-based transformation makes the data look absolutely normal by converting the data first to ranks and then transforming it to the corresponding quantiles of a normal distribution. As this transformatino does not have a parameter (or actually one parameter per sample, namely the normal quantile), it is called non-paramteric." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phenotype_vals_ranks = preprocess.rankStandardizeNormal(phenotype_vals.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the histograms after transformation:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NsHwbJGp9HTIS9RwS3T4qGSqRggzLDQw08IiBNgPf933e1upLax1WmOvlYKGB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\nN6tCJsgMPTpHo6A092pG9I6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Si3cYwjrfj68oZeAUy1sbZg01a7DtBgZqKSTv6M01IoyMAo6GYEaiNsvbGziV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\nKIqiKIqiKIqilMH/B9xVmnMLKlrjAAAAAElFTkSuQmCC\n", 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+kgU1oqJl7PMw6Rj4BR4C3uRaO8+NGlFKGZKXEeVl7Tw3y2z2o/OVSlH8ZwRI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\nJuAayjNwgpEhvmYDLQZ2Gvj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[10,3])#create the figure\n", " \n", " plt = pl.subplot(1,3,1)#the untransformed phenotypes\n", " #histogram of the untransformed phenotypes\n", " plot_normal(phenotype_vals.values[:,ip],alpha=0.8,figure=plt)\n", " pl.title(\"%s\" % p_ID)\n", " \n", " plt = pl.subplot(1,3,2)#the untransformed phenotypes\n", " #histogram of the untransformed phenotypes\n", " plot_normal(phenotype_vals_boxcox[:,ip],alpha=0.8,figure=plt)\n", " pl.title(\"%s, Box-Cox\" % p_ID)\n", " \n", " plt = pl.subplot(1,3,3)#the rank transformed phenotypes\n", " #histogram of the untransformed phenotypes\n", " plot_normal(phenotype_vals_ranks[:,ip],alpha=0.8,figure=plt)\n", " pl.title(\"%s, ranks\" % p_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GWAS testing using linear regression on the original phenotype:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#run linear regression on each SNP\n", "lm = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals.values)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_lm = pd.DataFrame(data=lm.pvalues.T,index=dataset.geno_ID,\n", " columns=phenotype_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A common way to visualize the results of a GWAS is a Manhatten plot, where the $-log_{10}$ P-values are plotted against the genomic position." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Gy7Gdp4hkn81AlbM+jCpqSILmkqrxv4A3EzvN+VVsEpR/SnajRESSLa53OQfrYeoE5TSK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\nz7CXRO8A3oTl/7ViM4Iew7Zfbdj7T8P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WEjj/HPBjwA7gs8A/Ac6sR6dEREREtqvozJeOeGqZboD41GdP4PI6WBY4NuneB1Vho41W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\nqGmqZaPQ5OSi98yZxe4LF+IdkUjWZbdbLFYr+Vp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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Genome-wide manhatton plots for one phenotype:\n", "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[12,4])\n", " plot_manhattan(posCum=position['pos_cum'],pv=pvalues_lm[p_ID].values,chromBounds=chromBounds,thr_plotting=0.05)\n", " pl.title(p_ID)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Observations:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Several peaks are visible.\n", "\n", "* The peaks entail multiple neighboring SNPs due to linkage disequilibrium to the causal variant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### GWAS testing using linear regression on the transformed phenotypes:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we analyze the Box-Cox transformed phenotypes." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#run linear regression on each SNP\n", "lm_boxcox = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals_boxcox)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_lm_boxcox = pd.DataFrame(data=lm_boxcox.pvalues.T,index=dataset.geno_ID,\n", " columns=phenotype_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we analyze the phenotypes transformed by the rank-based transformation." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "#run linear regression on each SNP\n", "lm_ranks = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals_ranks)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_lm_ranks = pd.DataFrame(data=lm_ranks.pvalues.T,index=dataset.geno_ID,\n", " columns=phenotype_ID)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare the results on the various transformations and the orignial data, we create correlation plots between the P-values." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3a9+o92/nvKlGSs3rdxNac6/TdL0KLG71tB1wE/BDYHZ5bztq6UMDGs/ZLrmS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\nRGR0R5fPWgAAAABJRU5ErkJggg==\n", 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i/m0AfB33zd5YjUJEtZNIlgaAV4G9gVtyHktpzYl+Horr7nQM8IeMx6CiVyQ8PuL6UeAr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\nvcxwkH+fcvwt1sclrGuBX0bPle1vUZ3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Je+5JgyOqsLPBzbX3TTYfyZKc9NO3a4sgjAJV4GHcRY2r0XPd6DWbQPkkXZms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\n92vVTqXVqnZaaTARr5tuV90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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[15,3])#create the figure\n", " pl.subplot(1,3,1)\n", " pl.plot(-sp.log10(pvalues_lm[p_ID].values),-sp.log10(pvalues_lm_ranks[p_ID].values),'.')\n", " pl.xlabel('$-log_{10}(pv)$ lm')\n", " pl.ylabel('$-log_{10}(pv)$ lm ranks')\n", " pl.title(p_ID)\n", " max_range = max(pl.xlim()[1],pl.ylim()[1])\n", " pl.plot([0,max_range],[0,max_range],'k--')\n", " pl.subplot(1,3,2)\n", " pl.plot(-sp.log10(pvalues_lm[p_ID].values),-sp.log10(pvalues_lm_boxcox[p_ID].values),'.')\n", " pl.xlabel('$-log_{10}(pv)$ lm')\n", " pl.ylabel('$-log_{10}(pv)$ lm boxcox')\n", " max_range = max(pl.xlim()[1],pl.ylim()[1])\n", " pl.plot([0,max_range],[0,max_range],'k--')\n", " pl.title(p_ID)\n", " pl.subplot(1,3,3)\n", " pl.plot(-sp.log10(pvalues_lm_boxcox[p_ID].values),-sp.log10(pvalues_lm_ranks[p_ID].values),'.')\n", " pl.xlabel('$-log_{10}(pv)$ lm boxcox')\n", " pl.ylabel('$-log_{10}(pv)$ lm ranks')\n", " pl.title(p_ID)\n", "\n", " max_range = max(pl.xlim()[1],pl.ylim()[1])\n", " pl.plot([0,max_range],[0,max_range],'k--')\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we look at the residuals at the strongest associated SNP by plotting the phenotype values against the SNP." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uwE6nB83xPdFGZAblFmhvRRhPLBu+1IGXo3vJ0aI0Qi5DbbxeAg7JeCxGUyn0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\nV6PO7zfk2mbKuFGGKWyNUylwM5A7dBlyh/qK1V3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OwGRhBz52eG/8mADcjea/y6jciNa/aUiJmIky7+N4sqhHVqBoUez6zxkD/n+3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\ns6EAAAAASUVORK5CYII=\n", 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oQHt1FDoMAYplN+Kalgbo0KwAroHczajKYyLqCpx14irHLLSX1jT3IGJg56E8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\nonc956nWgYzwGCIq8YECOgPbUf7sXFTYcgEK4adeEYN4PWM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AAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[10,3])#create the figure\n", " \n", " plt = pl.subplot(1,3,1)#the untransformed phenotypes\n", " #find maximum squared beta value\n", " pheno_vals, s_idx = dataset.getPhenotypes([p_ID])\n", " \n", " imax = lm.pvalues[ip].argmin()\n", " i_0 = snps[s_idx,imax]==0\n", "\n", " #plot SNP vs. phenotype for max beta\n", " \n", " pl.plot(snps[s_idx,imax]+0.05*np.random.randn(snps[s_idx,imax].shape[0]),pheno_vals.values,'.',alpha=0.5)\n", " \n", " pl.xlabel(\"SNP\")\n", " pl.ylabel(\"phenotype\")\n", " pl.xlim([-0.5,1.5])\n", " pl.title(\"%s\" % p_ID)\n", "\n", " plt = pl.subplot(1,3,2)#the Box-Cox transformed phenotypes\n", " pl.plot(snps[s_idx,imax]+0.05*np.random.randn(snps[s_idx,imax].shape[0]),phenotype_vals_boxcox[s_idx[sample_idx],ip],'.',alpha=0.5)\n", " \n", " pl.xlabel(\"SNP\")\n", " pl.ylabel(\"phenotype\")\n", " pl.xlim([-0.5,1.5])\n", " pl.title(\"%s, Box-Cox\" % p_ID)\n", " \n", " plt = pl.subplot(1,3,3)#the rank transformed phenotypes\n", " pl.plot(snps[s_idx,imax]+0.05*np.random.randn(snps[s_idx,imax].shape[0]),phenotype_vals_ranks[s_idx[sample_idx],ip],'.',alpha=0.5)\n", " \n", " #pl.plot([0,1],[pheno_vals.values[i_0].mean(),pheno_vals.values[~i_0].mean()])\n", " pl.xlabel(\"SNP\")\n", " pl.ylabel(\"phenotype\")\n", " pl.xlim([-0.5,1.5])\n", " pl.title(\"%s, ranks\" % p_ID)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is an interesting artifact visible from the rank-transformation. On the original data there are two peaks visible. By making the data look normal distributed, the two peaks are merged together. Once conditioning on the strongest SNP, which seems to have a Mendelian effect on the phenotype, the residuals become less normal distributed than for the original data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Assessing the P-value distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For SNPs that are truely not associated with the phenotype (the null hypotheses), P-values should be uniformly distributed between 0 and 1.\n", "As in GWAS we test a large number of SNPs, most of which are probably not associated, we should largely get normal distributed P-values." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# P-value histgrams\n", "\n", "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[10,3])\n", " \n", " plt = pl.subplot(1,3,1)\n", " pl.hist(pvalues_lm[p_ID].values,20,normed=True)\n", " pl.plot([0,1],[1,1],\"r\")\n", " pl.title(\"%s\" % p_ID)\n", " pl.xlabel(\"P-value\")\n", " pl.ylabel(\"Frequency\")\n", " \n", " plt = pl.subplot(1,3,2)\n", " pl.hist(pvalues_lm_boxcox[p_ID].values,20,normed=True)\n", " pl.plot([0,1],[1,1],\"r\")\n", " pl.title(\"%s, Box-Cox\" % p_ID)\n", " pl.xlabel(\"P-value\")\n", " \n", " plt = pl.subplot(1,3,3)\n", " pl.hist(pvalues_lm_ranks[p_ID].values,20,normed=True)\n", " pl.plot([0,1],[1,1],\"r\")\n", " pl.title(\"%s, ranks\" % p_ID)\n", " pl.xlabel(\"P-value\")\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the histogram of P-values we observe that there are a large number of uniformly distributed P-values, but also a portion of small P-values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### P-value Quantile-Quantile plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference of the observed P-value distrbution and the distributions lies mostly in the range of small P-values. By comparing the quantiles of the negative log P-values to the negative log uniform quantiles, this difference betcomes even more visible.\n", "\n", "Question:\n", "\n", "* How can non-uniform p-values be explained in this dataset? " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ax6SJttgMK5S8GvP7I7D+mOcDOwGPYoOS+vm7FTmmWQZtEPhbrAp7Oxf3JHAJ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\nrhwUi2GkibLtmybspRjMWr/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WTVKR7bqKD1f5o6seJjPLxHXGaYsES5z2SafzLyDLlmsRTRQnTvuk0/kXkCQ9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\nAElFTkSuQmCC\n", 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P8ZaoeB/wAvCXqD3ADgEBlNB+DtxH+rNf/gTYjxLqfwHlaabjAEynG9+TC0uE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\n3Lf+2LG6+XXr/ON1df19bW3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Quantile-Quantile plots\n", "\n", "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[10,3])\n", " \n", " plt = pl.subplot(1,3,1)\n", " qqplot(pvalues_lm[p_ID].values)\n", " pl.title(\"%s\" % p_ID)\n", " \n", " plt = pl.subplot(1,3,2)\n", " qqplot(pvalues_lm_boxcox[p_ID].values)\n", " pl.title(\"%s, Box-Cox\" % p_ID)\n", " \n", " plt = pl.subplot(1,3,3)\n", " qqplot(pvalues_lm_ranks[p_ID].values)\n", " pl.title(\"%s, ranks\" % p_ID)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The grey area in the plots marks the $\\alpha=0.01$ confidence interval for the hypothesis that all or P-values come from the null distribution (are uniform) " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HDgJuiMr2EgYzlm8Arcx9aSBktwW0Ydu2JEmSKqga81DfGS25FuSsfzBaJEmS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\nSZIkSZIkSZIkSZIkSZIkSZI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VmdYqqONarp87Z6oFtSTlpWsHhiVtuXZ2RUn1o7UKaklSNSwCjgaeB/ZOfbQ2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\nLJ4i5EAILdUH93n+BuD46PG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SpAwsqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJkqQM\nLKglSWnsCNxd8XgJ+FypEUlSybxToiQpjUeA3aPpkcAi4GflhSNJ5bOFWpJUqwOBx4GFZQciSWWy\noJYk1epDwOVlByFJZbOgliTVYj3gfcBPyw5EkspmH2pJUi0OBu4CllRffURn3/SB3TB9afEhSVJN\nOqNHzSyoJUm1OAq4In71VV395xdMLDIYScqgK3r0mpN2B3b5kCSlNY5wQeK1ZQciSY3AFmpJUlqv\nAJPKDkKSGoUt1JIkSVIGFtSSJElSBhbUkiRJUgYW1JIkSVIGFtSSJElSBhbUkiRJUgYW1JIkSVIG\nFtSSJElSBhbUkiRJUgZFF9QXAc8B9w+xzXeAR4F7gd0LjkeSJEnKVdEF9cXAzCHWHwJsD+wAfBI4\nr+B4JEmSpFwVXVDfDiwbYv1hwKXR9HxgY2DzgmOSJEmSclN2H+opwMKK+aeBqSXFIkmSJKVWdkEN\nMGLAfE8pUUiSJEk1GF3y6y8CplXMT42WDXYVnW9Mb0s3o1lbZGCSVKPO6CFJahNlF9TXAccDVwL7\nAMsJo4IMdgRd/ebv6VeIS1Kj6IoeveaUE4YkqV6KLqivAPYHJhH6Ss8BxkTr5gE3Ekb6eAx4Bfho\nwfFIkiRJuSq6oD4qwTbHFxyDJEmSVJhGuChRkiRJaloW1JIkSVIGFtSSJElSBhbUkiRJUgYW1JKk\ntDYGrgYeAh4kDHsqSW2r7HGoJUnN578Iw57+PeF7ZFy54UhSuSyoJUlpbAS8E/hINL8GeKm8cCSp\nfHb5kCSlsQ2wBLgY+ANwAbBhqRFJUsksqCVJaYwGpgPfi/6+ApxaakSSVDK7fEiS0ng6etwZzV9N\n1YL6iM6+6QO7YfrSogOTpBp1Ro+aWVBLktJYDCwE3gL8CTgQ+OPgza7q6j+/YGLRgUlSjbqiR685\naXdgQS1JSusE4DJgPeBx4KPlhiNJ5bKgliSldS+wZ9lBSFKj8KJESZIkKQMLakmSJCkDC2pJkiQp\nAwtqSZIkKQMLakmSJCkDC2pJkiQpAwtqSZIkKQMLakmSJCkDC2pJkiQpAwtqSZIkKQMLakmSJCkD\nC2pJkiQpAwtqSZIkKQMLakmSJCkDC2pJkiQpAwtqSZIkKQMLakmSJCkDC2pJkiQpg9EF738mcDYw\nCvgBcOaA9RsBPwamRbF8E7ik6p66xu/cb/6FdZuxduXmuUYrSZIkpVRkQT0KOBc4EFgE3AlcBzxU\nsc1ngQeA9wGTgEcIBfaaQXt78qTt+s2v+POm9Fw5FV4rIHRJkiQpmSIL6r2Ax4DuaP5K4HD6F9Tr\ngInR9ETgRaoV0wDj/npFv/mVr4zPLVJJkiSpRkX2oZ4CLKyYfzpaVulcYGfgGeBe4MQC45EkSZJy\nV2QLdU+CbWYCfwAOALYDbgV2A1YM2nLxRR1vTI/bZXkeAUpSATqjRyvrBl4G1gKrCWckJaltFVlQ\nLyJcbNhrGqGVutIs4OvR9OPAk8COwIJBe5t8XHe/+Vfnj80lSknKV1f06DWnnDAK1UP40bC05Dgk\nqSEU2eVjAbAD0AGsBxxJuCix0lOEixYBNicU008UGJMkKR8jyg5AkhpFkS3Ua4DjgZsJI35cSLgg\ncXa0fh5wOmGYvPsIyfkL2OIhSY2uB7gl+jsPuKDccCSpXEkK6sOAGwgjcqR1U/SoNK9i+lngPTXs\nV5KUTZbcvi8hf7+JcO3Lw8Dt+YUmSc0lSUF9JOHmLFcDFxESpySpuWXJ7c9Gf5cAPyNclDigoD6i\ns2/6wG6Y7tlHSY2qk4wXkycpqI8h3NHwKEL3jB7gYuAKqo3GIUlqBrXm9g0J3fhWAOOAg4AvD97s\nqq7+8wsmDt5GkhpCFxkvJk96UeJLhFaMnwBbAn8H3A18Lu0LSpIaRi25fXNCa/Q9wHxCt5Fbig1T\nkhpbkhbqwwnD2+0A/BDYE3ie0ErxIPCdooKTJBWm1tz+JPC2OsQnSU0jSUH9AeAs4DcDlq8EPp57\nRJKkejC3S1JOknT5eI7BCffM6O8v8w1HklQn5nZJykmSgvrdVZYdkncgkqS6MrdLUk6G6vLxaeAz\nwHbA/RXLJwC/LTIoSVJhzO2SlLOhCurLCTdlOQM4hb7bzK4AXiw4LklSMcztkpSzoQrqHqAb+Gw0\nXWlTvEW4JDUjc7sk5WyogvoK4L3AXQxOugDbFBKRJKlI5nZJytlQBfV7o78ddYhDklQf5nZJylmS\nUT72BcZH08cC3wa2LiwiSVI9mNslKSdJCurvEwb63w04CXiCcFctSVLzMrdLUk6SFNRrgHXA+4Hv\nAucShleSJDUvc7sk5STJrcdXAKcBHwbeCYwCxhQZlCSpcOZ2ScpJkhbqI4HXgOOAxcAU4JtFBiVJ\nKpy5XZJykqSF+lngWxXzTwGXFhOOJKlOzO2SlJMkLdQfBB4FXiacIlwRTUuSmpe5XZJykqSF+hvA\nocBDBcciSaofc7sk5SRJC/ViTLiS1GrM7ZKUkyQt1AuAnwA/B16PlvUA1xYVlCSpcOZ2ScpJkoJ6\nI2AVcNCA5SZdSWpe5nZJykmSgnpW0UFIkupuVtkBSFKrSNKHekfgf4A/RvO7Av9eWESSpHowt0tS\nTpIU1BcQ7qbV28fufuCowiKSJNWDuV2ScpKkoN4QmF8x3wOsLiYcSVKdZM3to4C7gevzDEqSmlGS\ngnoJsH3F/N8T7rAlSWpeWXP7icCDhEJcktpakosSjwfmEfrbPQM8CRxTZFCSpMJlye1TgUOArwEn\nFRKdJDWRoQrqz1dM3wTcRmjRXgl8APh2gXFJkoqRR24/C/gXYGLu0UlSExqqoJ5AOJW3I7AncF20\n/Fjg9wXHJUkqRtbcfijwPKH/dGcB8UlS0xmqoJ4b/b0dmA6sqFh+Y8L9zwTOJly88gPgzCrbdBJa\nO8YAL2CClqQizY3+1prb3wEcRujysQGhlfqHwD/23+yIzr7pA7th+tLawpWkwnWSsf5M0of6zfS/\n8nt1tGw4o4BzgQOBRcCdhJaQhyq22Rj4LvAe4GlgUoL9SpKyqzW3nxY9APYHTmZQMQ1wVVf/+QV2\nD5HUqLqiR685aXeQpKD+IeE04LXACOD9wKUJnrcX8BjQHc1fCRxO/4L6aOAaQjENoYVaklS8WnP7\nQI7yIantJSmovwb8AngnIXHOIvSdG84UYGHF/NPA3gO22YHQ1eM2Qr++/wJ+lGDfkqRsas3tlX4d\nPSSprSUpqAHuih5pJGm1GEPow/cuwk0G7gB+Bzw6aMvFF3W8MT1ul+UpY5Gkeumkea4FqSW3S5IG\nSFpQ12IRMK1ifhp9XTt6LSR081gVPX4D7Ea1gnrycd395l+dPza3SCUpP11k7IsnSWouSe6UWKsF\nhC4dHcB6wJH0Dc/U67+B/QgXMG5I6BLyYIExSZIkSbkqsoV6DeFOXDcTCuYLCRckzo7WzwMeJvTh\nuw9YB1yABbUkSZKaSJEFNYS7cN00YNm8AfPfjB6SJElS0ymyy4ckSZLU8iyoJUmSpAwsqCVJkqQM\nLKglSZKkDCyoJUmSpAwsqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJkqQMLKglSZKkDCyoJUmSpAws\nqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJaWwAzAfuAR4A5pYa\njSQ1gNFlByBJaiqvAgcAKwnfIf8L3EQosiWpLdlCLUlKa2X0dz1gDLCuxFgkqXQW1JKktEYSunw8\nB9wC3FluOJJULgtqSVJa64C3AVOBvYG/KjccSSqXfaglSbV6CbgNmAn8sf+qIzr7pg/shulL6xaV\nJKXTGT1qZkEtSUpjErAGWA6MBd4NnDF4s6u6+s8vmFh0YJJUo67o0WtO2h1YUEuS0tgCuBQYReg2\n+BPgxlIjkqSSWVBLktK4H5hedhCS1Ei8KFGSJEnKwIJakiRJysCCWpIkScrAglqSJEnKoOiCeibw\nMPAocMoQ2+1JGIbpAwXHI0mSJOWqyIJ6FHAuoajeGTgK2ClmuzOBXwAjCoxHkiRJyl2RBfVewGNA\nN7AauBI4vMp2JwBXA0sKjEWSJEkqRJEF9RRgYcX809GygdscDpwXzfcUGI8kSZKUuyIL6iTF8dnA\nqdG2I7DLhyRJkppMkXdKXARMq5ifRmilrjSD0BUEYBJwMKF7yHWD9rb4oo43psftsjy/MCUpV53R\nQ5LUJoosqBcAOwAdwDPAkYQLEyttWzF9MXA91YppgMnHdfebf3X+2FyilKR8dUWPXnPKCUOSVC9F\nFtRrgOOBmwkjeVwIPATMjtbPy/wK6609iE3YYdDyVSxmOadm3r8kSZI0jCILaoCbokeluEL6o6n3\nPqZnArPpHrR8Hh3YKUSSJEl14J0SJUmSpAwsqCVJkqQMLKglSZKkDCyoJUmSpAwsqCVJkqQMLKgl\nSZKkDCyoJUmSpAwsqCVJkqQMLKglSZKkDCyoJUlpTQNuA/4IPAB8rtxwJKlcRd96XJLUelYD/wzc\nA4wH7gJuBR4qMyhJKost1JKktBYTimmAvxAK6S3LC0eSymVBLUnKogPYHZhfchySVBoLaklSrcYD\nVwMnElqqJakt2YdaklSLMcA1wI+Bnw9efURn3/SB3TB9aV2ikqT0OqNHzSyoJUlpjQAuBB4Ezq6+\nyVVd/ecXTCw2JEmqWVf06DUn7Q7s8iFJSmtf4MPAAcDd0WNmqRFJUolsoZYkpfW/2CAjSW8wIUqS\nJEkZWFBLkiRJGVhQS5IkSRlYUEuSJEkZNPdFia9uvCUXTnr/oOXLXhwFL5QQkCRJktpNcxfUPRuN\nYcLpywctf+60qRb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "computing test statistics on permuted data\n", "\"\"\"\n", "phenotype_vals_perm = phenotype_vals.copy()\n", "\n", "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " perm = np.random.permutation(phenotype_vals[p_ID].values)\n", " phenotype_vals_perm[p_ID] = perm\n", " \n", " #run linear regression on each SNP on permutated and non-permuted data\n", " lm = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals.values)\n", " lm_perm = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals_perm.values)\n", " \n", " #plot\n", " fig = pl.figure(figsize=[12,5])\n", " pl.subplot(1,2,1)\n", " pl.hist(lm_perm.test_statistics.flatten(),normed=True,color='g',alpha=0.5,bins=50,range=(0,20),label='perm')\n", " pl.hist(lm.test_statistics[1],normed=True,color='b',alpha=0.5,bins=50,range=(0,20),label='observed')\n", " pl.xlabel('test statistics')\n", " pl.ylabel('density')\n", " pl.title(p_ID)\n", "\n", " pl.legend()\n", " pl.subplot(1,2,2)\n", " p=pl.hist(lm_perm.pvalues.flatten(),normed=True,color='g',alpha=0.5,bins=50,range=(0,1),label='perm')\n", " p=pl.hist(lm.pvalues.flatten(),normed=True,color='b',alpha=0.5,bins=50,range=(0,1),label='observed')\n", " pl.title(p_ID)\n", " pl.xlabel('p-value')\n", " pl.ylabel('density')\n", " pl.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## False discovery rates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "False discovery rates (FDR) give an idea on the expected type-1 error rate at a given P-value threshold. If we are testing millions of hypotheses, then we might be willing to accept type-1 errors at a given rate, if in return we get more discoveries.\n", "\n", "This measure gives a useful alternative to traditional Bonferroni correction, which bounds the so-called family-wise error rate (FWER), namely the probability of having at least a single type 1 error.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### $q$-values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition: minimum false discovery rate threshold that would allow the variable to be significant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following we are computing $q$-values for each SNP first on the original data and then on permuted data. By permuting the data we break any dependence between the SNP and the phenotype, making the data come from the null distribution (no association)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:got pi0 > 1 (1.031) while estimating qvalues, setting it to 1\n" ] }, { "data": { "text/plain": [ "(0, 10)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#fitting q-values using the Benjamini-Hochberg step-down procedure\n", "\n", "import limix.stats.fdr as fdr\n", "fig = pl.figure(3,figsize=[15,5])\n", "\n", "#1. on the real tests\n", "pl.subplot(1,2,1)\n", "qv, pi0 = fdr.qvalues(lm.pvalues[1], return_pi0=True)\n", "#plot distribution of p-values and fitted \\pi parameter:\n", "fig = pl.figure(3,figsize=[8,5])\n", "pl.hist(lm.pvalues[1],bins=100,normed=True,color='b',label='observed',alpha=0.5)\n", "pl.axhline(pi0,color='g',linewidth=2,label='$\\pi$')\n", "pl.axhline(1,color='r',linestyle='--',linewidth=2,label='null')\n", "pl.legend()\n", "pl.xlabel('p-value')\n", "pl.ylabel('density')\n", "pl.ylim((0,10))\n", "\n", "#2. on permuted p-values\n", "pl.subplot(1,2,2)\n", "qv_perm, pi0 = fdr.qvalues(lm_perm.pvalues[1], return_pi0=True)\n", "#plot distribution of p-values and fitted \\pi parameter:\n", "pl.hist(lm_perm.pvalues[1],bins=100,normed=True,color='b',label='observed',alpha=0.5)\n", "pl.axhline(pi0,color='g',linewidth=2,label='$\\pi$')\n", "pl.axhline(1,color='r',linestyle='--',linewidth=2,label='null')\n", "pl.legend()\n", "pl.xlabel('p-value')\n", "pl.ylabel('density')\n", "pl.ylim((0,10))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare the differences and similarities between multiple hypothesis correctin via the false discovery rate and the family-wise error rate, we plot q-values (estimated by the Benjamini and Hochberg procedure) and the family-wise error rate (upper bounded by the Bonferoni method)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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A8/OBLwBjgcuwjQFaHYUFfaG7WERENrCW+3RgFXCS/yfACmAmNZ52VVr8vEEgqut9ObA3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\nwHNuyxERkSKJd8W7RNeg94bA81pufwJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare q-values against p-values and Bonferroni adjustement:\n", "pl.figure(figsize=[8,8])\n", "pl.plot((lm.pvalues[1]),(qv),'.',label = 'qv')\n", "pl.plot((lm.pvalues[1]),(lm.pvalues[1]*snps.shape[1]),'.',label = 'Bonferroni')\n", "pl.plot(lm.pvalues[1],lm.pvalues[1],'k--')\n", "pl.yscale(\"log\")\n", "pl.xscale(\"log\")\n", "pl.xlabel('p-value')\n", "pl.ylabel('adjuste p-value')\n", "pl.legend(loc='lower right')\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An interesting observation is that for a single significant SNP, the false discovery rate and the family-wise error rate are the same, whereas they start to differ when loweringt the significance threshold." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conditional analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the phenotype Cadmium Chloride there was one very large effect visisble. In th efollowing we perform a conditional analysis to find additional secondary associations.\n", "\n", "Note that all the phenotypes condition on the same SNP, that has an association to the first phenotype only." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create a matrix of covariates, one being the SNP to condition on \n", "#and a constant vector of ones representing the bias term.\n", "imax = lm.pvalues[0].argmin()\n", "\n", "covars_conditional=np.concatenate((snps[sample_idx,imax:imax+1],np.ones((phenotype_vals.values.shape[0],1))),1)\n", " \n", "\n", "#run linear regression on each SNP\n", "lm_conditional = qtl.test_lm(snps=snps[sample_idx],pheno=phenotype_vals.values,covs=covars_conditional)\n", "\n", "#convert P-values to a DataFrame for nice output writing:\n", "pvalues_lm_conditional = pd.DataFrame(data=lm_conditional.pvalues.T,index=dataset.geno_ID,\n", " columns=phenotype_ID)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YIK+sTJsMDUXKR44ksu6y3+3Zb+bmlsPT08V+gP5+lpeXuYdGT+h1OazD30kb\n239y2t/myP3SSewzOoCNnV2GdA7rFP9mIAvpT7by2U1NZeLPPTc/7tydPXNmKD8zUwyDlWV1pv2y\nngJmT9Uym3ecnz1We1abmIXdXyas1SzjHBAaX69xqKWyPXbL9SWwDonqJNMGjTvr4eFIORCoriwv\nrwT9btd+MzNTDM/OLvcDPPjg4NzKSmBpYSH7J8CzPjdNZB+rlV6GgSKw4iwngKPO3xawgHrLgHlh\noRTOZMpRd9kdHcf9u4Jmbyhg9kxjAOiWKvRS7VknJyq5bx2fh/QYMABcbvF5h7AdVYHeLIXxTOPO\nem5uOfziiwtDN28WBsvl1fBLLy0Mnz49kNcO3Btzc8vh11/PJzOZcmx0NLb8jndErr/wQvYT9NzJ\nuKw1Od2QnFB2ufskgDc5y4usPebcA2adn/lWEj6nTw/k791bXnKXO9BeaYECZm+59Uc9NjV202zw\nDq3PVq9ZRwLL0g8Ch2DyRgsvOIq9/yU6Okb0/pHJlMJA+OWXM+Nzc8VBCPTdvl1UfbiHxsai5VCI\n1UAgUF1drfYlEuEydjCWnqdAuYslgGNAFLiGZZhds1j9+SLWQX5kq+NmKhUrv/3t43PuMtjJcuN9\n6TwFzN7aD4Faor1B82br4Iux0QCuQnrW6QjTzD1spIx7qKPMrrg76HA4ULl6dWmwWiUQjQYr1SoM\nD4dV8uKhVCpWfutbR+Zgobq6Wu0LBgNl2j7+uXQnL67qyQ7FsQRPxLkfdm454AEs9ioD41h54fwG\nr7FGY2A8M1MMl8vVkLusoNkbGiXDOwlqg9D3WknAmpn+IrRtsPbG8arXjJJxDOt5fAjLMrdiFpgC\nnldmZvfm5pbD169n49PTy7FotG9lbCyaPXQonnnTmwaW/G7bfnPmzFD+rW8dvnf0aDx38mQyi8ox\n9gFNjLEHrGL1ywPYCE1DwAkscB7A6pg1os0eooDZO+7kGSW/G7I3bHRAmMxiPY7f4vwtgF3WanVW\ns9ltPFY2MTWViT/zzHzq2WfnD01PF5OBQKA6Ph7LHj+eyIyORorKdnjvzJmh/MMPDy4eOdKfV8Zx\n34izP65a7kV5rGxwHvuMxrGywH4suxyinnV2tD6hWSoVK+dy5UAuVw5of+sdBcyemcxiAXOe+6c6\n3eNqwW0EOyHodI32MvZeXgVea/E5bkeJHsvu+ycWC1YSiWApHg+VDh+O51KpWNHvNu1nc3PL4bt3\ni5GtHynIAgLBAAAgAElEQVQiHXYXOzbdxeqXk1hwXMFGxngQmKA+X0ECyzg3vWIwM1MMz8wUw1NT\nmfjdu8vJu3eXkxqb2TsKmL2Vox649apcvZPebk4KNp3l6i525n4dC5hbeD8ns9jOKk7Pnax478yZ\nofypU4mFY8fiC0ND4cL4eLQwOBgqF4tlKpVqyB1yTrwzNZWJX768NPLyy4tjmlVs32gcXlO6j/vZ\nDGMlGP1YGeHDWEnhY1jQnHMeM0yTKwbu6ER37hT7FxZK2sf6QAGzp5pOdbqHTWaxzHJp3YgWu6yv\n22iWKw5Qv6x1FDjJllnjtHt2H0GXMHdtZqYYfv313OCVK0ujt28Xh+7cKQ7kcivBXG4l+vLLi8Nu\n720R6ZRePZb0DLfPUgXLMIewY88KNnFZGDuOjWPHNLBM80Irn+fp0wP5Awei2QMHotkzZ4Z00uQR\njZLhuV7cuaWT1HoDpyewnUWYzmQ/cth02M7lK5ZorczCbUtLOyTZ3JUrS/HXXssNXb2aHYpG+1bG\nx6P5lZXVlWCwry+fr/Qp++E996BZLK7MqVOriO/cPksLQAx4K1bDnMCujg5iSaYVbEKtAnbMXNjs\nBVOpWLkxGXHvnk1YNDNT1Lj3HlGG2XO7LVXoSgnqHVAOAw9hI1l0YHiryWlsspJZe/1WvsO1bMxN\nBRPt8cYb2eTSUimZzVZChcJqKBzuCwQCrExMxPLDw5oe2w9nzgzlH3tsVKOU7AsaJaO7TWaxYePK\nWDDch5VlHMeyzp8FXsBG0RjErpQeB85sVlLlDiVXLldDzzwzN3T5cnbk8uXsyJUrS7pq6hFlmD3V\niQk+/FbLLsexnUMc++cvY7XGbQxQ1+xI3EzzEi0No9UL73V3yGRK4VyuEs7lSuFYLMTQUKSUSsWK\nIyPh/IkTybwuEYrI/paewDr3uWaxY+McdrVzzrm5x8wjWH1zGBv+9D5XrizFX389P3D4cH8BIJMp\nRZaXV4LOBFLiAWWYpR3GsbPkcsMtAUy0L/uRnsAC8UewHUsRuAG8QsvjzvZkdt9z2WwlXKkQhWAw\nGu2rHD4cyzz22MjsE0+MZcbGosoue8ztOe93O8RLqmHeQ97AOqhfwybPchMK7rEyg9UzvxkbPeO+\nEsOpqUz80qXM+IsvZsYvX84kAapVAsXiSuTmzUJC///eUMDsqfWd43pKP3amfJ36UDphtu6QN7F5\nr/6mAe4d4Hng1dbeS13CbJdkMlQOhSiFw4Hy8HC0FIuFqgsLpfDTT8+OPf307JgbwGkn3nmNPef1\nfu83G3aKlq4wOY0FyO6ssnksOI5hV0WL2PEyDJzCZq49ik1s8tD6V1tYKIXn5orxxcVSfHZ2OQ7Q\n11ddBVhdJaiO1t5QSYan1nSOS/bYzq6f+qxFBWyHsIX0KWznUHBKVBrKN+4rX5m2n6Sodyjc7oxm\n487PLachlc0NDUXKExPxbChULI+NRfKVSjX4uc/NHchmV6LRaN9KMBioHD6cqE3Qow4pIrJPhbEZ\n/txjYxbLKIMdw8LY1dk+LDYoYhnpNU6fHsi//PLi0tWrS8m7d6uJbHZgIRAIsLKyGujrq67oyp43\nvM4wHwM+BrwIfB74Xo/X7ze3c1yvOQKMYf/8KaxTw6Jz2ySoTSex0oqT2Pdio0z0+pmscthZulsz\nPUxLGeN0Ehu6Z8i5afKSXapWCYyOxkqJRGg5n68EKxX6isWVYCAQqILVOau2rvNSqVj54MFYIRwO\nVK5cWYo/99y9Ab/bJCLpJHZ8OoR19ithgfIS9Suv7kx/QezYNoNdOb2vD0gqFSsHg6xks5WhmzeL\nqU9/euZQLrcSCQT6yOcrSnx6xOs3ugz8A+A5bOabzwEfAV72uB0+aMwu90JJRi1IdcdFjmM7hinq\ndVm3ttjOPHbGnWPjwHqzkwt359PCWXU6iQX049j4l5qNbpcymVI4Gg2uLi2VwrlcJXr0aDQ/OBgu\nDQyEislkeOXEiWT+6tWsAjePzM0th194YX5sfr4c6+sLlK3ESaPBiPgsjx2nQtQ7+GWwIPkwFgPd\nwxJMReoB9X1mZorhN97IJ4vFSsTynIFqKMRKMEjfgQPx5c5vioD3AfMd5wYWKL2MfXH2QcBcs5NS\ngi7TWC4BWL1yAfvnd7dtoYWDdgb7PhS4/z1xB353l93A+5jz89X6Q5sG5Qng0Ybn7OD9V81zoxMn\nkvmPfOR2Xz5fCUUiwdVCoRI8fLg/f/hwfxWs3q5SqYbAgjmVZHTOzEwxfPXq0uD167mBlZVqcGws\n5neTRMQm8EpQHy1qCDv2zAAPUL/K6SaFKlgAHV3/SjMzxcgrrywOrqxUw6VShUBgdXV4eLQYCgX6\nCoWVcDZbDnZ8cwTwt9PfSeBx4NM+tsFDPdmr2f1nn8ZGrJjDdgTONJ+bBZq1rO8hrKPDA9RnO3Ll\nqHWQrAW4J7GA+Zgtt9TpJYX1PH6Q+tB32+j4V+8s+JnPzPZiOc22vfjiwtD8fCmZy1Xi169nB2Zm\nlmOZTLkfrL55eDhSLhYrfcVipU+1dZ01N7ccXlioRFdWCJbLK6ujo+Gssssim/FqpKR0EksGPoId\ne0ao1zG7mWewcowD2LFtFJsqe5x1FhdL4fn55Vg2uxLLZKrJV15ZGLtzp9gfDAYD2Wwlok5/3vAr\nYE4CvwG8j3rmsNGF8+fPnzt//vy5D33oQyc9bVlH9Uqv5tpoH1DvgDdNfZaig85ts1rhBLYjeRc2\nA9LDwKMb7Mjy3F/PFcSC3lbH+o1jPZNj1HdYskMzM8Xwa69lh1dWiC4vr4QWF8uxXK4Su3On2B8K\nBSoHD8YKAEtLlfDSUiWsHXlnjY1Fy6EQq8vLK33hcLBvZYVge4dy3GwEG/GGPoP28XykpDgWBLsJ\nm1NYAucW8Bp2xXMBCGDlgiE2OEalUrFSMBio3LtXDJXLhJeXiV+9mjv82msLo+FwoPTAAwOLSkx4\nw4+AOQz8N+CXgd/a5DEXLl68+EcXL178o6eeeuqaZy2T7cixNmjdTqlDCssqH8F2Kpt9D91Ofwnn\noOEGyhls6LpWzFIfJP4G287y168MvOMd45qQA3j00aH50dHwwsRE/8KBA7F8LleJBAIwPBwpp1Kx\nslOSEaxUqkFNk91ZqVSsfPx4YjEeD5btuNsu6QnspPYRBWx+qY09f1KfwV4zmQVuA8tYsmaEegZ5\nGDuGzWCJp1lsZIwcdiw83Ph5T01lEsvL1Wg0GlqNxymWy6yUSiQymcro9etLI6OjkaLK3rzhdQ1z\nAPg54CXg33u8bmk/t2QCaoEtOWx0DGgeQLsd9yrUx6VslMCG2xnFBnQvYmfjjROjtBL05oAr9eWd\nZPjd57x7+0/tMalUrHzq1MDiO9+5cuP27Xx8fr6cqFYDlWqVqvuY06cH8tev5/Lusn+t3R+GhyPl\nY8cS2ZmZYiwYZKVNV7ESWCded1lkj5vMOkOT4tGV3hwWMOexINkdNs6VwPr/zGL9f+adv9/XEeH6\n9WwiHO4jkQhlV1Yqq4uLDC4uEh8dXe2/eTMfp17iIR3kdYb5ncC3A18BPOvc9lkU0guzzdUubbmd\n8pyaYoaxnYBbSpHYZFuvYSdNX8DOwgNYCcf6OmZ39qMvB74MeAtWklFY25am72cOu/z1mi33wvvv\nn5mZYnh2drl/ZaUaqlYDwZUV+iKRvkoiEVppfNzAQGhlYGDt76Qzxsai5cHBUCkeD1VWVwm16ft9\nl/r/catXc6StJqepjSC0UV269mXb53lZZBE7jgWw4+UIlviZx/6vFqhnm29gx6mrjZ/32Fi0fPdu\nIX7vXmk4HA4yOhotBIMsl8v0lcsVDzdFvA6YP+Gs8zGsw9/jwP/2uA0+6snZ5lLY7ETHsLGYXU3G\nSZ7MApewGq5F6lnqRjks+zxAffzkKHbG7ozvXOs8eGTz93Myi01HetP5xRaPl2bm5pbDb7yRT0xP\nF/v7+0OVI0fiS+Pj4eyhQ7G8W0fndkRbWKhEVcPcealUrDw6Gi0kEqFKpVIN0p6McAI47tyUYfZF\negJ77xP3l2T05LGkh6ST2GfnBssrzs25ql8L3N2rpktY8ugSlkyquXJlKV6pVIOl0kowFApSqVQq\nkQjVcJhqNlvWsDge0oDXsgOTWUifbPiFGxRVsED3MGvHUN5oZr0cdimqH9tZ3OT+TFbB+X0A27E8\nj3UudMZ3Tk9gI23g/H2TzIGbUWj18Y3cg1EvdNZsj6WlSjgWC60cO9a/ODQUKbtDyDXq66uXaEjn\nnT49kK9UqqF8fmWjIRp34gx21chd1sgb3Uczl26be+LRyZFkaiczh7Djm3ulYAFLEuUaHhfBjp/9\n1DrPrz3W2ARQgWokEsgHAgRGR+Ol2dmlSl8f1Uikr3r9ek4nTB5RwOwpz2uoOiR9CniTc2caK7EA\nuI6dVZ/E6rBauZS7hAXGmbXvyWQW0reBy9jlqnmsY8T6zoXbDcy28fhaBjsO6VvbXE/PKhTKIYAT\nJ1L5sbFo+ZVXFgfBMstuRnlurhgBu5zoX0v3D6fTz2K5vLrQpn3LHHY1x10Wz01ONxwv1gd4CawD\nGdioCx0/nszMFMOwl6e6T5/CroQW7X3t+PCLeaw2GSzZ4xy37vv/dEdyqgBDayceSidv3SoMzM8v\n94fDoUAwGChXKqvBcBiqVXKJRCQXiQRWZ2aKGu/eA36Ow7wPpU9Rz9rsde7QcbPU6tEnp7ESjQks\n0ExhZ9AbXNKtXY4qYTuK8AalG1exHU4VK814EHjIfqaT2A7InQynlazaNh5fm077EFY7fVbjMNuk\nJIXCarhQWA03joCxuFgKX726NPjcc/Pjv/u7N49PTS2mrlzJjj/zzNyQn+2VHXN78JecZfHF5PQm\ngV3j1ModL5mZmSmGn356bvTpp+dG3cB5L/noR6fjWFnfMewKaAfVRla6hSWTrmHHm4jd1ly1dMsR\ni9RLEA83zqQ7PV1MrKxU+6LRYGl4OFKsVFZIJELVRILSyEi0cPz4QFGlb95QwOyZ9Cmss+OfA966\nx+vO3GmwnX/SNTv1PPXh32awoHiT4HTSKa/YsIYZ5/JZGKv9imJn4Uls9IzE2vrkVrJqrT6+dkkt\n7GznBPCmP/3T2dGt19H7qtVqoFqt1sYwGxqKlAcHI+XXXsslX3hh/sCVK5nhO3cKw3fvFpPT08U9\nPvZ1+pTzv9vV3IDmE5+YSbVp33IGG6Fm1FmW7nKX+nCZHe+U+cd/PD3+8Y/fPWL/30t7NXHwADbm\nvwcn8ZNZ6kOvbjVSUIj6TLkF1iaZcn19VPv7g5Xjx+ML73xn6o1HHx2dGxnpzx46NLB04kRybnQ0\nUtSVPG+oJMM749iZbT92ENoj1td8pZNYZjmMnRGPQ/puQwCaa7g5k5msD07XnGHnqB+Yb657zGHs\nOzqPdfSbxTK+DWUZ2738vK3H56l3Suy7d6+0B4O/9tZgDw9HyvF4sOQup1Kx8tzccjiXK0fy+Upk\nerowsLS0XF1eXg0Hg4GVRCK0h7txp08BTzjLOFc8utKVK0vxGzfyg9lspYJdGdnt513ATlTdZdm1\nttfPevK5TE1l4i+9lEm98srC4WQynHv88ZHbXqy3nX77t984io24dBobaemzeFuX7x4T2WBf7B5X\nblAP5mtJpsXFciSXq0Sj0WDmzW8ezhw5Es8vL6/0FQqV8MhIuBQKBSoqx/CGMszemcWyqW8A1/ZG\nDfOmA+cXsCzyMhbQNo46kXB+546jHFmb8bpvZIuT2Jn/A6wtV3Frod+EBa63qXcU3OHYvq0Ow9Q4\njTlTWKZ88dFHB+81fVrXaWUUke0bH+8vjo/3F6E+zFwmU+7PZErRO3fyA3Nzy8OBQKAaj+/lYBmw\nKynjzq2rs2rDw5FyKBRYCQYDbsfb3boOXHVu19vwevtc2ychcWuYT1IbjrMzswIuLJTC09OFeKGw\nEsvlVrrmqtHUVCY+NZXZzv+lO5teh8sX1hxnGjLMmw5pF6A+69D6jHTq1q18cn6+NHDnTiF5/Xo2\n/tJLmeFMZjk2P18euHIlN3r9ei65F8tkWtc9wycqYPbOXeAZrBPbXp7MIYFlgzNY9nWQ+vjLrT5/\neN1zGncYrhPAUef1G3fQrVzi2kArwzA1HnB6Yhrzjd7rXRkbi5aDQSrBIJWxsWjZHWbOpsEuhsvl\n1fDqKpFqdTUwMhJZOnIkvpe/6zPY99wdJ7VrjY1Fy4cOxfJHjsSWaE/ADPWAWbpPAusjcgx4U0P/\nmJPtDppPnx7Ij4xEitFoXymRCBa64arR1FQmfvny0sjly0sjrQTNX//1R9/ArmAuYcetDu2XGo8z\nQK0fwGS2SeDXeExbf3w7e+dObiSTKUbz+Urf9HSx//LlxdTMzPLI7Gx+cH5+OZ7LlXu4UqD2fh7p\nhtkuFTB7ZjKLHXjLQKobPvytTbojYFxzemy7ZRIx51amVnZRO0jnsCz6FWqd+dYEnu7wOu5zrmET\nmHyB2mgb6aQ9jwC1oXaYd9rTsANqxZqd1Dibjiu7PgNUC55TzvPGX3ppcQ+V0gD3v9e7Nje3HM5m\nK9Fs9v4xlsfH+5f7+4OVvj5W4/FI/uDBeG54OLLXLxXecm5dzT6XlVAbp8d2Z+x0xjyX3Zmcxk66\nZtpUkpHDym6WsX2lGzQewZINbXXwYDw7MhJdPHCgf0+eBL/66lKc+twAR6gPydeiHWU5E9Q7+rkd\n4ddf7WvsiH6X2pXNySyk3wK8Y3Z2dbhYpB/6QgCBANVwOLA8NtafGR2NZIeGIqUeL8k4jl1pHvY7\n06yA2VvugaelwcZnZorhLrjU0lB7BdjkJIewHcEKdhLQ0Iluchp4GSudCHDfyUFtdIzG2uYbzq2R\n22nwGrUSltq4lQ1lHs12ZOkkNrLGg9hlS3cc5sP15615rnsikAIecW5HsQ6HbYtEvDOZpTaVeHuy\n5QsLpfDMTDExM1NMLCyUwmNj0fLRo/Hcww8PZh57bORuIhFaSCTChfHxWC4W6/M9E7VL2x2FxRcz\nM8XwCy/Mj7300sL45ctLI7Rv1IQdXs2R+6WT1P4X23HQn5zGhiq7hM0Odw3LxB0EJtrZUXVubjkc\niQSrExPx/MRE/1I3nASfOTOUf+ihgfmHHhqYP3NmaMvv6J07hX5s3z/g3LZRppaewI4hLTynNvJF\nibX7jE2u9q3viL7myuYYcLRUIlqtQjQaKE9MxAojI5HisWOJzCOPDN155JGhexMT/aUuiBM6JYEd\nu9dPjOaLHk7ldyV39jp3eVMzM8XwnTv1WjF/ziBrNbBA+ib1YdYmsJ3/KDB+f2eo2vihG2Vy3aAX\n1tZ5beQWFqw0a59z6SvNBkHhAWxIuCg2YHycenmHe/bvPLeWUXMNYMGz2/mptAdrmN0MOZDeZHrd\n3XE6/RUAcrly4PjxgdzduwWi0b5SMhkqNw49t/dMZiG9UF/uTleuLMWvX88NzM4uDwYCgXYGzNLV\nJq/a/zVgn/kI9ZlR47Z/3P33dmwsWj5yJJYrFMqhgwfjuW4ZkaGVQHmdHFaScRfb1x+x/+/N9ou1\noUVPYVn7ezSd8Ko2u98wdqy5xdpJZdx94bpj/6afUQHo6+ujEotRGBwMFwCGhqKloaFoaXw8kp2Y\n6C8NDfl/AtNBbp+oIGtLM32xDzLM3VMwjn34Fee2Fw5qG50VR4BVLAgdBEY3zmasL+fYTGMHuzU7\nDneEioZM9KaP3Uwc6xX9KHaG6mQWmGDjExY3ozaD7RyLWNAcBMqNJzD7lTMyRi6ViuWGhyNlt9Pf\n7Oxy/9BQpDw6GilEIn0roVBgdWmpEr56NTe8zY45XSQ9gX33I120D1ljZqYYvndvub9QqISgWo7F\ngns9q++Lzl/Nm8xSGze5XSdf6VNYiVwEC+iCWEA4i135a8u02alUrBwMBioTE/3ZRx4ZWtz6Gd7Y\nwWeWx7Lxn8P2786xbaP3KJ0Evgj4s8CXAW/HkkWbHLdryZvD2PHmNLXxnmtZ420MgQpYgLgYjVKK\nxULl8fFYYWgoUj52rH/p2LH+pS/7sonZhx8eXDx4MFbo4ZIMt0xlGiv19FWPZ5i3zEB6LYft1Nzl\nTaVSsbI73mUqNezXP4NbA+su57Av8DhWolB2lpfWDS3n2ChQnsxCOlFfbvx5n/Wz+q177JYzJ7oZ\n5QQWMI9ilyxj2D/fTMPrJKnXAS44634Q2+n1UZ/1bA+ZnG54r9uSXbbOZf05d3lubjk8NbU4BPCW\ntwzN9fUFVkOhYF8uV4nevl0IRKPBqldZ5vbORJZOUj9ZhC6dfnhubjlcqRAcGoouLy+v9CUS4fUl\nVLsVx/5//N53dszMTDHszlYJLKZSLVXMbVO7r/akJ7BJnMaxq3AF7LuaxfZtx7F92K6/t1NTmfj8\nfDmey1VCuVw56+PxqGa7V2Dn50v92HsVwsr/btF8ApMDzt/f4twCWLDdqhj3ZUS3HX/MAbcHB4O5\nkZFYPpkMlU6fHsifPj2Qd/uP9HCg3Og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Gy7Gdp4hkn81AlbM+jCpqSILmkqrxv4A3EzvN+VVsEpR/SnajRESSLa53OQfrYeoE5TSK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\nz7CXRO8A3oTl/7ViM4Iew7Zfbdj7T8P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8T5fbkqUCfuAa4DeBrVjeZzQvrhO4DhsNuMi5VAEjcTugGmJpHFqFTTJEwI+N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\nNGd0dCzHCZLHA+TOziFvV9dwobM9nuc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WEjj/HPBjwA7gs8A/Ac6sR6dEREREtqvozJeOeGqZboD41GdP4PI6WBY4NuneB1Vho41W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\nRu3yMPK8QfrJJtjJhvZS8qpBDcKzF6l3TuAXyvI0h6wSFpF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1A9txQuiqKFVDRERk9T4J/Hvmq2lYQJz1WBhjI1ge0h33jASKFwewLGfVI5ub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\nf1dicLAj9P77c7c5HAXD53OmK4Wmm5lJGfl80eF22/M+nyP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for ip, p_ID in enumerate(dataset.phenotype_ID[0:P_max]):\n", " pl.figure(figsize=[12,4])\n", " plot_manhattan(posCum=position['pos_cum'],pv=pvalues_lm[p_ID].values,chromBounds=chromBounds,thr_plotting=0.05)\n", " pl.title(p_ID)\n", " pl.figure(figsize=[12,4])\n", " plot_manhattan(posCum=position['pos_cum'],pv=pvalues_lm_conditional[p_ID].values,chromBounds=chromBounds,thr_plotting=0.05)\n", " pl.title(p_ID+\" condtional\")\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observation: For all but the associated phenotype the conditional analysis looks almost identical to the original analysis." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }