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Last active August 25, 2023 14:43
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Prediction of selection in euploid embryos due to lethal mutations
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8c9573e7-39de-4b09-bd33-824c61cc3136",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import binom, geom\n",
"from arjun_plot.utils import *\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "50c57791-3889-471d-b495-e33481dabe70",
"metadata": {},
"outputs": [],
"source": [
"# Taking implantation rates from Reig et al 2020 Table S2\n",
"# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125286/\n",
"# NOTE: assumed that these are simply single transfers (no repeated measurements)\n",
"n_trials_35, n_success_35 = 3789, 3105\n",
"n_trials_37, n_success_37 = 2200, 1731\n",
"n_trials_40, n_success_40 = 1624, 1260\n",
"n_trials_41, n_success_41 = 319, 234\n",
"n_trials_42, n_success_42 = 243, 178\n",
"\n",
"#1. Deriving the probability of implantation success\n",
"p_success_35 = n_success_35/n_trials_35\n",
"se_success_35= np.sqrt(p_success_35*(1-p_success_35)/n_trials_35)\n",
"\n",
"p_success_37 = n_success_37/n_trials_37\n",
"se_success_37= np.sqrt(p_success_37*(1-p_success_37)/n_trials_37)\n",
"\n",
"p_success_40 = n_success_40/n_trials_40\n",
"se_success_40= np.sqrt(p_success_40*(1-p_success_40)/n_trials_40)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f2f58d3-9073-4680-9c8d-c2892fc3a251",
"metadata": {},
"outputs": [],
"source": [
"# This is the same data, but using the live birth numbers. \n",
"n_trials_35, n_success_35_live = 3789, 2301\n",
"n_trials_37, n_success_37_live = 2200, 1249\n",
"n_trials_40, n_success_40_live = 1624, 881\n",
"n_trials_41, n_success_41_live = 319, 163\n",
"n_trials_42, n_success_42_live = 243, 127\n",
"\n",
"#1. Deriving the probability of implantation success\n",
"p_success_35_live = n_success_35_live/n_trials_35\n",
"se_success_35_live = np.sqrt(p_success_35_live*(1-p_success_35_live)/n_trials_35)\n",
"\n",
"p_success_37_live = n_success_37_live/n_trials_37\n",
"se_success_37_live = np.sqrt(p_success_37_live*(1-p_success_37_live)/n_trials_37)\n",
"\n",
"p_success_40_live = n_success_40_live/n_trials_40\n",
"se_success_40_live = np.sqrt(p_success_40_live*(1-p_success_40_live)/n_trials_40)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "736c5cfa-1cda-4254-b1ca-d16222feceb8",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 600x300 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Upper and lower bounds on lethal point mutations per-meiosis from \n",
"# https://pubmed.ncbi.nlm.nih.gov/35879308/\n",
"min_prop=0.26\n",
"max_prop=0.51\n",
"\n",
"# number of euploid embryos ... \n",
"ns = np.arange(1,100)\n",
"fig, ax = plt.subplots(1,2,figsize=(6,3))\n",
"ax[0].plot(ns, ns*min_prop, color='blue', label=r'Paternal Age: 30 years')\n",
"ax[0].plot(ns, ns*max_prop, color='blue')\n",
"ax[0].fill_between(ns, ns*min_prop, ns*max_prop, alpha=0.1)\n",
"\n",
"# NOTE: this factor of ~3 for a 60 year old father is crudely from Fig 2 of https://doi.org/10.1016/j.tig.2019.08.005\n",
"ax[0].plot(ns, 1.5*ns*min_prop, color='orange', label=r'Paternal Age: 50 years')\n",
"ax[0].plot(ns, 1.5*ns*max_prop, color='orange')\n",
"ax[0].fill_between(ns, 1.5*ns*min_prop, 1.5*ns*max_prop, alpha=0.1)\n",
"ax[0].legend(frameon=False, fontsize=8)\n",
"\n",
"\n",
"ax[0].set_ylabel('# mutations not\\n in GnomAD', fontsize=10)\n",
"ax[0].set_xlabel(r'# Euploid embryos', fontsize=10)\n",
"\n",
"# Modeling the probability of successful implantation success / live-birth as a geometric random variable\n",
"prob_success = lambda k,p: geom.cdf(k,p)\n",
"n_embryo_euploid = np.arange(1,9)\n",
"ax[1].plot(n_embryo_euploid, [prob_success(n, 1.0 - min_prop) for n in n_embryo_euploid], color='blue')\n",
"ax[1].plot(n_embryo_euploid, [prob_success(n, 1.0 - max_prop) for n in n_embryo_euploid], color='blue')\n",
"ax[1].fill_between(n_embryo_euploid, [prob_success(n, 1.0 - min_prop) for n in n_embryo_euploid], [prob_success(n, 1.0 - max_prop) for n in n_embryo_euploid], alpha=0.1, color='blue')\n",
"\n",
"# Fill in some of the data from Reig et al 2020\n",
"ax[1].errorbar([1], \n",
" p_success_35_live, \n",
" yerr=se_success_35_live, capsize=2, color='orange', label=r'Age $\\leq$ 35')\n",
"ax[1].errorbar([1], \n",
" p_success_37_live, \n",
" yerr=se_success_37_live, capsize=2, color='purple', label=r'37 $\\geq$ Age $>$ 35')\n",
"ax[1].errorbar([1], \n",
" p_success_40_live, \n",
" yerr=se_success_40_live, capsize=2, color='red', label=r'Age $\\geq$ 40')\n",
"\n",
"\n",
"ax[1].set_xlabel(r'# Euploid embryos', fontsize=10)\n",
"ax[1].set_ylabel(r'$\\mathbb{P}($ success live birth $)$', fontsize=10)\n",
"ax[1].legend(frameon=False, fontsize=8)\n",
"ax[1].set_xticks(np.arange(1, np.max(n_embryo_euploid)))\n",
"debox(ax[0]); debox(ax[1]);\n",
"label_multipanel(ax, labels=[\"A\", \"B\"], xoff=-0.3, yoff=1.05, fontweight='bold')\n",
"fig.patch.set_facecolor('white')\n",
"plt.tight_layout()\n",
"plt.savefig('lethal_vars.020923.png', dpi=300, bbox_inches='tight')"
]
},
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"source": [
"### Discussion + Brainstorming of use of DNM mutations in embryos \n",
"\n",
"1. One suggestion of this is that some proportion of live-birth success is determined by paternal age due to putative lethal mutation burden. Can we verify this in some empirical IVF dataset that it takes older fathers more euploid embryos to have a live birth for mothers of the same age?\n",
" a. Currently our number of euploid embryos for successful live birth assumes that conditional on euploidy that only lethal mutations (either maternal or paternal in origin) can affect the live-birth probability. I'm sure that this assumption breaks, but thinking of specific exceptions to this would be fruitful.\n",
" \n",
"2. Its possible that we are not observing the \"most lethal\" point mutations, that would create developmental arrest of the embryo prior to Day 3 or Day 5 biopsies. \n",
"\n",
"3. We have not made any strong assumptions about the \"spectrum\" of lethal mutations and its relationship to paternal age. It would be useful to add some additional detail to these calculations using the Roulette model for basepair level mutation rates: (see https://www.biorxiv.org/content/10.1101/2022.08.20.504670v2). Whether lethal mutations occur in \"clusters\" would also be quite interesting to understand their spatial scale in the genome - if originating mechanism is localized...\n",
"\n",
"4. We currently don't account for the power to detect individual mutations in the embryos (which should influence the y-axis of plot A above on a per-embryo basis due to variable coverage per-embryo and genomic location). It seems from PTA applications that they are fairly high-fidelity but still might be a useful point for modeling and staying closer to anticipated experimental noise.\n",
"\n",
"5. We've limited ourselves to SNVs unobserved in GnomAD as a putative basis for lethality (can't be in gnomAD if lethal generally) but have not considered other forms of variation necessarily. Would the PTA methods allow for detection of CNVs or indels that we could also compare to large-scale catalogs?"
]
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