Research ethics approval
Ethics approval for use of human research subjects in this study was obtained from the University of British Columbia/Children’s and Women’s Health Centre of British Columbia Research Ethics board (H17-01545) and from the Hospital for Sick Children (1000038847) and Mount Sinai Hospital (05-0038-E) Research Ethics boards. Informed written consent was obtained from all study participants.
Sample collection and cohort characteristics
Vancouver Cohort
Placental samples for the Vancouver cohort were ascertained and processed as described19 and include cases used in previous studies (16, 21–24). Clinical information, including newborn sex and birth weight, gestational age at delivery, maternal age, and ethnicity were collected. Placental and maternal samples were processed and DNA was extracted as previously described(16).
This cohort (N = 207) included 136 controls from uncomplicated pregnancies (no SGA, hypertension/PE, or known abnormal maternal serum screen results) and 71 cases of SGA (Table 1). Exclusion criteria were a prenatally-diagnosed chromosome abnormality or congenital anomaly in the fetus. SGA was defined as birth weight < 10th percentile, adjusted for sex and gestational age at birth based on Canadian growth charts (25). The majority, 55/71 (77%) of SGA cases met criteria for FGR, defined as birth weight < 3rd percentile, or < 10th percentile with additional findings suggestive of placental insufficiency, including i) persistent uterine artery notching at 22–25 weeks, ii) absent or reversed end diastolic velocity on umbilical artery Doppler, and/or iii) oligohydramnios (amniotic fluid index < 50 mm). One FGR case had a birth weight > 10th percentile but was diagnosed as FGR from prenatal measurements and severe oligohydramnios. Preeclampsia (PE) was defined according to Canadian criteria(26) as previously described(24). Following aneuploidy assessment, euploid placentas from a subset of N = 24 control and N = 29 SGA cases, 90% of which fulfilled criteria for FGR, were selected for further CNV profiling (Table 1). These were randomly selected after excluding cases and controls associated with a twin pregnancy (N = 23), or known maternal smoking during pregnancy (N = 3). Figure 1 summarizes the study design and number of cases per cohort used at each analysis step.
Table 1
Study cohort clinical characteristics
Group
|
Gestational age at birth (w), mean (range)
|
Maternal age at birth (y), mean (range)
|
Sex, N male (%)
|
Birthweight (S.D.), mean (range)
|
Twins, N (%)
|
PE, N (%)
|
Vancouver cohort - Total samples
|
Control (N = 136)
|
39.2 (30.1–41.9)
|
34.3 (23.8–45.8)
|
68 (50)
|
0.1 (-1.2-2.7)
|
11 (8)
|
0 (0)
|
SGA (N = 71)
|
35.3 (23.6–41.7)*
|
35.2 (23.1–41.0)
|
34 (48)
|
-1.9 (-3.6- -1.2)*
|
12 (17)
|
31 (44)
|
Subset of samples for CNV profiling
|
|
Control (N = 24)
|
39.3 (38.0-41.4)
|
34.8 (30.2–40.5)
|
13 (54)
|
0.01 (-1.1-2.2)
|
0 (0)
|
0 (0)
|
SGA (N = 29)
|
34.9 (24.0-40.6)*
|
34.4 (23.9–42.9)
|
18 (62)
|
-1.9 (-3.0- -0.6)*
|
0 (0)
|
11 (38)
|
Toronto cohort – Total samples
|
Control (N = 37)
|
37.1 (27.3–41.0)
|
32.9 (21–43)
|
19 (51)
|
0.28 (-1.1-1.5)
|
0 (0)
|
N/A
|
SGA (N = 30)
|
34.0 (27.1–38.6)*
|
35.1 (25–44)
|
9 (30)
|
-2.2 (-3.5-1.2)*
|
5 (17)*
|
N/A
|
*p < 0.05, p-values calculated in comparison to respective control groups by Student’s t-test for maternal age and birth weight, Mann-Whitney U-test for gestational age, and Fisher’s exact test for all categorical variables. SGA, small-for-gestational age; PE, preeclampsia; N/A, not available. |
Toronto cohort
The Toronto cohort was ascertained and processed as part of a distinct study, and findings from the two cohorts were then subsequently compared. Placental samples were obtained as previously described (27). Clinical information including newborn sex, birth weight, and gestational age were collected for all cases. The original cohort included N = 99 samples, however following microarray quality filtering, N = 67 remained, including placentas from 37 control and 30 SGA pregnancies (Table 1, Fig. 1). Definitions for control and SGA followed the same criteria as the Vancouver cohort, described above. Exclusion criteria were a prenatally-diagnosed chromosome abnormality or congenital anomaly in the fetus, CMV or toxoplasmosis infection, or clinical amnionitis. Additionally, cases or controls were excluded if mothers were diagnosed with: i) preconceptional severe hypertension; ii) clinically significant thrombophilia; iii) advanced renal, heart or liver failure; iv) type I diabetes mellitus or gestational diabetes requiring treatment with insulin; or v) anemia and autoimmune disorders requiring therapy during pregnancy.
Aneuploidy screening and CPM follow-up
Aneuploidy was detected using several methods in this study. In the Vancouver cohort, samples were assessed by comparative genomic hybridization (CGH), which can detect aneuploidies greater than 15 Mb, or by multiplexed ligation-dependent probe amplification (MLPA) of subtelomeric probes (SALSA MLPA Subtelomeres Mix, MRC-Holland, NL), designed to detect aneuploidies that extend to the ends of the chromosome (Fig. 1). A subset of these samples (N = 85 control and N = 43 SGA), all screened by CGH, have been previously published (16); the current study is a retrospective re-assessment of aneuploidy in those cases, with additional samples collected. For more recent cases, MLPA was used to screen for aneuploidy because it is a reliable and cost-effective method to identify whole chromosome aneuploidies (monosomy and trisomy), as well as terminal duplications and deletions. In the Toronto cohort, aneuploidy was detected using CNV profiling by microarray (see below). All cases with an aneuploidy detected by any method was further assessed by microsatellite polymorphism genotyping of probes on the involved chromosome (Additional File 1: Supplementary Methods). Aneuploidies identified by MLPA were also confirmed using CNV profiling by microarray to determine the extent of the alteration, particularly in cases where results suggested abnormalities restricted to one chromosome arm (see below, Supplementary Methods).
Microarray processing and CNV detection
Placental DNA was assessed on the Infinium Omni2.5-8 BeadChip array (Illumina, USA) for the Vancouver cohort, and on the Affymetrix CytoScan HD array (ThermoFisher Scientific, USA) for the Toronto cohort (Fig. 1) at The Centre for Applied Genomics, Toronto, Canada (28, 29). In the Vancouver cohort, an additional DNA sample from a different location in each placenta was also run on the array to assess the possibility of detecting mosaicism of CNVs by high-density microarray (Supplementary Methods). Following sample quality checks unique to each array type, all 54 Vancouver cases and 67/99 Toronto cases were available for analysis (Fig. 1). CNVs were detected using in-house pipelines (28, 29) applying 3–4 CNV-calling algorithms specific to each array platform (Supplementary Methods). Following CNV quality checks, high-confidence CNVs called by at least two algorithms with a minimum 50% reciprocal overlap, ≥ 5 probes, and ≥ 10 kb were kept for analysis. CNV boundaries were compared to the Database of Genomic Variants and in-house databases of CNVs in controls, and rare CNVs were defined as those present in < 0.1% of controls and at least 50% unique. Given discordance in CNV calls between technical replicates of placental DNA (Supplementary Methods, Supplementary Fig. 1), mosaicism of CNVs was not investigated and the DNA sample with the higher microarray quality scores from each placenta was selected for CNV analysis for the Vancouver cohort. Ancestry was assessed using SNP genotypes by MDS clustering of identity-by-state distances in PLINK (30) (Supplementary Methods). The ancestry composition of both cohorts was comparable (Supplementary Table 1, Supplementary Fig. 2).
Candidate CNVs
CNVs with potential clinical relevance to SGA were prioritized based on: whether they were rare, ≥ 200 kb, overlap pathogenic or likely pathogenic CNVs in the DECIPHER or ClinVar databases, overlap genes with important roles in placental function or those that are reported to be differentially expressed or with variants associated with growth restriction. CNVs were categorized following American College of Medical Genetics guidelines (31). Candidate CNVs were confirmed and assessed for CPM using quantitative PCR (Supplementary Methods).
Placental-enhanced and imprinted genes
A list of 356 genes with elevated expression in the placenta was downloaded from the Human Protein Atlas (32), including 78 with placental-specific elevated expression. A database of imprinted regions was curated from the OTAGO Imprinted Genes (33) and GeneImprint (34) databases, and reported placental imprinted differentially methylated regions (DMRs)(35, 36) (Table S2). Outer genomic boundaries were used to generate a consensus region for those genes associated with a placental imprinted DMR.
Functional pathway enrichment
Enrichment of 2,191 GO and KEGG (37) pathways in genes with coding sequences impacted by rare CNVs in SGA was assessed using a generalized linear model with universal gene count correction in the cnvGSA R package. Sex and cohort (array) were included as covariates, and thresholds of 100-1,500 genes were used to limit pathways assessed. A false-discovery rate (FDR) of < 0.1 was used to define significantly enriched (coefficient > 0) or deficient (coefficient < 0) pathways in SGA CNVs.
Statistical analyses
Continuous variables were compared using the Student’s t-test or Mann-Whitney U test depending on whether the data was normally-distributed by the Shapiro-Wilk normality test. Categorical variables were compared by Fisher’s exact test. Bonferroni correction for multiple testing was used where applicable. Statistical power for comparing CNV load was assessed using the pwr package in R. Based on a previous report of a large effect size (d > 0.95) in the difference in CNV load in control vs. SGA placentas (19), we assumed a slightly lower but still large effect size (d) of 0.8. Based on the minimum sample size in each group per cohort (N = 24) and using an α = 0.05, our study had > 80% power to detect significant differences in each cohort individually. Analyses were performed in R version 3.5.1 (38), and plots were generated using the ggplot2, ggbio, and ggpubr packages.