Additional file 1: Table S1. of An assessment of sex bias in neurodevelopmental disorders

Keywords used to bin phenotypic indications from clinical referrals into phenotypic categories. Table S2. A list of CNV artifacts used for removing false positive calls is provided as an Excel file. Table S3. A list of all rare CNV calls is provided as an Excel file. Table S4. Frequency of comorbid features among boys and girls with autism in the (clinical and rare CNV) study cohort. Table S5. Frequency of comorbid features among boys and girls with ID/DD in the (clinical and rare CNV) study cohort. Table S6. Frequency of comorbid specific OHI features among boys and girls with autism in the (clinical and rare CNV) study cohort. Table S7. Frequency of comorbid specific OHI features among boys and girls with ID/DD in the (clinical and rare CNV) study cohort. Table S8. Frequency of comorbid features in boys and girls carrying specific CNVs associated with genomic disorders. Table S9. Ratio of boys to girls in individuals with autism and ID/DD and showing specific comorbid features. Table S10. Ratio of boys to girls in individuals with autism or ID/DD and carrying specific CNVs. Table S11. Comparison of rare CNV load for specific combination of comorbid features in autism and ID/DD. Table S12. Comparison of CNV burden between boys and girls ascertained for autism or controls in two independent cohort studies. Figure S1. Age specific prevalence of ID/DD/MCA and neuropsychiatric/behavioral features within the clinical dataset. Figure S2. Frequency of comorbid features within the OHI category for clinical and rare CNV cohorts. Figure S3. Replication of CNV burden results in two independent cohort studies. Figure S4. Explanation of the comparisons made in the family history matrices. (ZIP 1274 kb

Current and suggested GWAS approaches.

<p>(A) Current approach. GWAS identify variants that are overrepresented in cases. Rare variants of large effect (red square, blue star) may escape detection, thereby contributing to missing heritability. Common variants that are overrepresented in cases (small yellow bar, 6 versus 2) do not contribute strongly to disease risk. A cryptic disease-related variant does not show significant overrepresentation in cases (open circle). (B) Suggested approach. Individuals are first analyzed for phenotypic robustness (bold box) and then for variants associated with disease. Rare variants of large effect will be enriched in robust cases, although they may also be present in nonrobust cases. Variants that are overrepresented in all cases (robust, nonrobust) will show higher penetrance in nonrobust individuals (large yellow bars). The formerly cryptic, disease-related variant (open circle) is significantly enriched in nonrobust cases versus nonrobust controls (and robust cases) and can therefore be identified. Together, heritability significantly increases. The formerly cryptic genetic variant and higher penetrance variant can be thought of as “disease-specifiers” as they determine the specific disease phenotype of individuals carrying them. Note symbols represent highly simplified frequencies of specific variant in indicated groups and not individuals carrying certain variants.</p

An illustrative example.

<p>In the top scenario, a VNA (shown in red) is present in the donor. In ref+, only concordant alignments (correct orientation and mapped distance) are present. As a result, the SV caller does not make a call in ref+, which is converted by TRANSLATE_CALLS to an insertion call in the GRC reference (hg18). In the GRC reference, however, the read pairs that originate from across the VNA junction map discordantly, with one read left unmapped or falsely mapping to a homologous region. These signals in the GRC reference are difficult to decipher for any SV algorithm. In the bottom scenario, where the VNA is absent in the donor, the pairs that span the VNA injection point in the donor align concordantly to the GRC reference. In ref+, they align discordantly with an enlarged mapped distance but bear the hallmark signature of a deletion. This is among the easiest signals that an SV caller can detect and most algorithms show good results with respect to this SV type</p

Improving the Power of Structural Variation Detection by Augmenting the Reference

<div><p>The uses of the Genome Reference Consortium’s human reference sequence can be roughly categorized into three related but distinct categories: as a representative species genome, as a coordinate system for identifying variants, and as an alignment reference for variation detection algorithms. However, the use of this reference sequence as simultaneously a representative species genome and as an alignment reference leads to unnecessary artifacts for structural variation detection algorithms and limits their accuracy. We show how decoupling these two references and developing a separate alignment reference can significantly improve the accuracy of structural variation detection, lead to improved genotyping of disease related genes, and decrease the cost of studying polymorphism in a population.</p></div

Method workflow.

<p><b>a)</b> In a traditional SV calling pipeline the reads are first aligned against the GRC reference and the alignments are passed to an SV caller, which annotates regions of the GRC reference as being inserted/deleted. <b>b)</b> Our approach is composed of two additional components. BUILD_REF takes a set of sequences to be inserted and modifies the GRC reference genome (e.g. hg18) by inserting the sequences into their prescribed locations, obtaining a new genome (ref+). We next align the reads to ref+ and run a SV caller. The TRANSLATE_CALLS component then modifies the resulting calls so that they become calls relative to the GRC reference, not ref+.</p

Analysis of ref+ pipeline accuracy.

<p>Each vertical line represents one individual, with the plus (+) point representing the ref+ pipeline and the square point representing the GRC pipeline.</p

WT mice (gray) display a greater increase in insulin resistance than <i>Dp(11)17/+</i> mice (red) after HF diet from 19 to 22 weeks as well as an increased weight gain after a long-term HF diet.

<p>(A) During IP-GTT, WT mice display more dramatically decreased glucose clearance rate (*p = 0.00011) after HF diet than <i>Dp(11)17/+</i> mice (*p = 0.03). (B) The insulin level of both genotypes are not impacted by HF diet. (C, D) During IP-ITT, <i>Dp(11)17/+</i> mice after HF diet demonstrate lower blood glucose concentration than WT mice, shown as both actual concentration in (C) (*p = 0.002, 0.007, 0.003 and 0.002) and percentage of the initial glucose concentration in (D) (*p = 0.00015, 0.00063, 0.0026, 0.041); whereas the differences are only partially significant under RC as shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002713#pgen-1002713-g003" target="_blank">Figure 3</a> (C, D). (E) Body weight of both genotypes after HF diet from 10 to 30 weeks. *: comparison for each genotype between HF and RC; $: comparison between the genotypes under the same diet condition. After HF feeding, both genotypes gain weight (WT: *p<0.001; <i>Dp(11)17/+</i>: *p = 0.048). <i>Dp(11)17/+</i> mice are still lighter than WT littermates after HF (^$p<0.0005), similar to those fed with RC (^$p<0.001). The curves for normal diet (data points without triangles) are the same as shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002713#pgen-1002713-g001" target="_blank">Figure 1B</a>. All comparisons were made with ANOVA with repeated measures (A, B, E) or two-tailed t-test (C, D); results are expressed as mean ± s.e.m. and obtained from measurements of n = 5 <i>Dp(11)17/+</i> and 7 WT after HF diet. <i>Dp</i>/WT mice after HF diet: red/gray solid line with triangle markers; <i>Dp</i>/WT mice with RC: red/gray dashed line without marker.</p

<i>Dp(11)17/+</i> mice have reduced and <i>Df(11)17/+</i> mice have increased body weight.

<p>(A) A <i>Dp(11)17/+</i> male (23 weeks old) and its WT littermate are shown, both on isogenic C57BL/6<i>^Tyr</i>^c<i>-Brd</i> background. <i>Dp(11)17/+</i> mice appear gray because of a coat color marker in the construct used to chromosome engineer this strain <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002713#pgen.1002713-Walz1" target="_blank">[20]</a>. (B) Growth curve of <i>Dp(11)17/+</i> (red) and WT littermates (gray) reveal decreased weights for the duplication CNV mutants throughout their life span (*p<0.001 for by ANOVA with repeated measures). (C) A <i>Df(11)17/+</i> male (32 weeks old) and its WT littermate on pure 129S5 background (D) Growth curve of <i>Df(11)17/+</i> (green) and WT littermates (gray) reveal increased weights for the deletion CNV mutants (*p<0.05 by ANOVA). (B, D): n = 10–25 mice for each data point, results are expressed as mean ± s.e.m.</p

<i>Dp(11)17/+</i> mice (red) have similar food intake and activity levels, but higher energy expenditure than WT mice (gray), which may be partially accounted for by the difference in expression levels of UCP1 in the BAT tissue.

<p>(A) <i>Dp(11)17/+</i> mice have similar amount daily food intake to WT mice after 4 wks of age, although they consume less food at 3 wks (*p = 0.001) and 4 wks (*p = 0.048). (B) VersaMax system (Accuscan Inc., Ohio) using the beam block technique implemented in home cages revealed no difference in horizontal activity level between <i>Dp(11)17/+</i> and WT animals. (C, D) Oxygen consumption measured using the CLAMS system (Columbus Ins., Ohio) for over three days documented higher energy expenditure of <i>Dp(11)17/+</i> mice in the light phases alone (*p = 0.0095) and during the entire day (*p = 0.044). (E, F) Respiratory exchange ratio (RER) measured using the CLAMS system for over three days again confirmed higher metabolic activity of <i>Dp(11)17/+</i> mice (*p = 0.00151). (G) Western blot for UCP1 expression in BAT tissue of three <i>Dp(11)17/+</i> and three WT mice with antibody AB3036 (Millipore). The same blot was normalized to actin blotting using MAB1501 (Millipore). (H) Normalized intensity of UCP1 signals in <i>Dp(11)17/+</i> vs. WT mice (17.13±10.21 vs. 4.74±2.05, p = 0.35). The measurements are from (A) 5–13 <i>Dp(11)17</i>/+ and 5–11 WT at different time points (B) to (F) 12 <i>Dp(11)17</i>/+ and 7–10 WT at 25–32 wks (G) 3 <i>Dp(11)17</i>/+ and 3 WT at 30 wks.</p

<i>Dp(11)17/+</i> mice (red) display improved insulin sensitivity compared to WT mice (gray).

<p>During IP-GTT (6 hr fasting, 1.5 mg glucose/g body weight), <i>Dp(11)17/+</i> mice demonstrate (A) lower blood glucose (*p = 0.006 for 120 minutes post injection; # p = 0.052 for the area under curve (AUC)) and (B) lower blood insulin level (*p = 0.0037, 0.0026, 0.0051, 0.0031 and 0.0015 for the time points 0, 15, 30, 60 and 120 minutes; *p = 0.002 for AUC). During IP-ITT (4–6 hrs fasting, 1 mU insulin/g body weight), <i>Dp(11)17/+</i> mice also demonstrate lower blood glucose concentration, shown as both actual concentration (C) (*p = 0.011, 0.004 and 0.037 for 0, 15 and 30 mins post insulin injection) and percentage of the initial glucose concentration (D) (*p = 0.038 for 15 mins post insulin injection). All comparisons were made with two-tailed t-test; results are expressed as mean ± s.e.m. from measurements of (A, B) n = 5 <i>Dp(11)17/+</i> and 6 WT at 30 wks (C, D) 4 <i>Dp(11)17/+</i> and 4 WT mice at 20–22 wks. All AUCs are computed until 120 minutes, for the entire length of the time curves.</p