Distribution of Genotype network properties in a set of coalescent simulations, varying the number of samples used to construct the networks.

<p>We first simulated 15,000 haploid sequences (5,000 for each of an African, Asian and European population). Then, we randomly sampled a given number of haplotypes, from 5,000 to 100, and calculated the distribution of properties of genotype networks built using only the randomly sampled individuals. Networks are based on 11 SNVs, and each data point represents the average of five random subsamplings of the same size. Bars are too short to be visible for most data points, and show 6 times the standard deviation of the mean.</p

Examples of genotype networks and their properties.

<p><b>A.</b> Representation of the Genotype Space for a region including 5 loci or Single Nucleotide Variants (SNVs). The space of all possible genotypes is represented as a Hamming graph (whole network). Each node represents one possible genotype, and each edge represents that the two nodes connected have only one difference. <b>B.</b> Example of genotype network. On top of the Genotype Space, we mark the genotypes observed in a population, and define it as the genotype network of that population (green nodes). <b>C.</b> Genotype networks of two populations (yellow and blue). The green population has a large average path length and diameter, while the blue population has a short average path length and diameter. <b>D.</b> Genotype network of a population having a high average degree and only one single component. <b>E.</b> Genotype network of a population having low average degree and many fragmented components.</p

Distribution of network properties, comparing a dataset of neutral demography simulations against a scenario of selective sweep.

<p>Selection scenario simulates a recent selective sweep with a selection coefficient of 0.015, and a final frequency of 0.99. The networks included in this graph are calculated by merging the 5,000 haplotypes of the three populations simulated (African + European + Asians) into a global population, and calculating the genotype networks on all the 15,000 haplotypes together.</p

Regions showing top scores in the genome.

<p>Regions showing top scores in the genome.</p

Distribution of network properties, comparing SNV functional annotations.

<p>Each point shows the mean +/−2 standard deviations of the mean for a class of networks.</p

data_sheet_1_Evaluating the Genetics of Common Variable Immunodeficiency: Monogenetic Model and Beyond.PDF

<p>Common variable immunodeficiency (CVID) is the most frequent symptomatic primary immunodeficiency characterized by recurrent infections, hypogammaglobulinemia and poor response to vaccines. Its diagnosis is made based on clinical and immunological criteria, after exclusion of other diseases that can cause similar phenotypes. Currently, less than 20% of cases of CVID have a known underlying genetic cause. We have analyzed whole-exome sequencing and copy number variants data of 36 children and adolescents diagnosed with CVID and healthy relatives to estimate the proportion of monogenic cases. We have replicated an association of CVID to p.C104R in TNFRSF13B and reported the second case of homozygous patient to date. Our results also identify five causative genetic variants in LRBA, CTLA4, NFKB1, and PIK3R1, as well as other very likely causative variants in PRKCD, MAPK8, or DOCK8 among others. We experimentally validate the effect of the LRBA stop-gain mutation which abolishes protein production and downregulates the expression of CTLA4, and of the frameshift indel in CTLA4 producing expression downregulation of the protein. Our results indicate a monogenic origin of at least 15–24% of the CVID cases included in the study. The proportion of monogenic patients seems to be lower in CVID than in other PID that have also been analyzed by whole exome or targeted gene panels sequencing. Regardless of the exact proportion of CVID monogenic cases, other genetic models have to be considered for CVID. We propose that because of its prevalence and other features as intermediate penetrancies and phenotypic variation within families, CVID could fit with other more complex genetic scenarios. In particular, in this work, we explore the possibility of CVID being originated by an oligogenic model with the presence of heterozygous mutations in interacting proteins or by the accumulation of detrimental variants in particular immunological pathways, as well as perform association tests to detect association with rare genetic functional variation in the CVID cohort compared to healthy controls.</p