Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies

Chelsea Lowther¹, Elise Valkanas², Jessica L Giordano³, Harold Z Wang⁴, Benjamin B Currall¹, Kathryn O'Keefe⁴, Emma Pierce-Hoffman⁵, Nehir E Kurtas¹, Christopher W Whelan⁵, Stephanie P Hao⁴, Ben Weisburd⁴, Vahid Jalili⁵, Jack Fu¹, Isaac Wong⁴, Ryan L Collins⁶, Xuefang Zhao¹, Christina A Austin-Tse⁷, Emily Evangelista⁵, Gabrielle Lemire⁵, Vimla S Aggarwal⁸, Diane Lucente⁹, Laura D Gauthier¹⁰, Charlotte Tolonen¹⁰, Nareh Sahakian¹⁰, Christine Stevens⁵, Joon-Yong An¹¹, Shan Dong¹², Mary E Norton¹³, Tippi C MacKenzie¹⁴, Bernie Devlin¹⁵, Kelly Gilmore¹⁶, Bradford C Powell¹⁷, Alicia Brandt¹⁷, Francesco Vetrini¹⁸, Michelle DiVito³, Stephan J Sanders¹², Daniel G MacArthur¹⁹, Jennelle C Hodge¹⁸, Anne O'Donnell-Luria²⁰, Heidi L Rehm⁴, Neeta L Vora¹⁶, Brynn Levy⁸, Harrison Brand¹, Ronald J Wapner³, Michael E Talkowski²¹

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA.
² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
³ Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA.
⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
⁷ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA.
⁸ Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁰ Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹¹ School of Biosystem and Biomedical Science, Korea University, Seoul, South Korea.
¹² Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
¹³ Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA.
¹⁴ Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹⁵ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹⁶ Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁷ Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁸ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
¹⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research, and University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia.
²⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
²¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA. Electronic address: mtalkowski@mgh.harvard.edu.

PMID: 37595579
PMCID: PMC10502737
DOI: 10.1016/j.ajhg.2023.07.010

Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies

Chelsea Lowther et al. Am J Hum Genet. 2023.

. 2023 Sep 7;110(9):1454-1469.

doi: 10.1016/j.ajhg.2023.07.010. Epub 2023 Aug 17.

Authors

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA.
² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
³ Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA.
⁴ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
⁷ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA.
⁸ Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁰ Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹¹ School of Biosystem and Biomedical Science, Korea University, Seoul, South Korea.
¹² Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
¹³ Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA.
¹⁴ Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA.
¹⁵ Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
¹⁶ Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁷ Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁸ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
¹⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research, and University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia.
²⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
²¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA. Electronic address: mtalkowski@mgh.harvard.edu.

PMID: 37595579
PMCID: PMC10502737
DOI: 10.1016/j.ajhg.2023.07.010

Abstract

Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.

Keywords: genome sequencing, karyotype, microarray, exome sequencing, structural variant, autism spectrum disorder, structural anomaly, prenatal, first-tier, diagnostic.

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Conflict of interest statement

Declaration of interests M.E.T. and H.R. receive research funding from Microsoft Inc and/or research reagents from Illumina Inc. M.E.T. also received research funding from Levo Therapeutics and research reagents from Ionis Therapeutics for unrelated research projects.

Figures

**Figure 1**
Overall study design We performed genome sequencing (GS) on 7,241 individuals from two phenotypically ascertained cohorts: autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). The ASD quartet families (n = 6,448 individuals) included one affected proband with ASD, one unaffected sibling, and two unaffected parents. The prenatal cohort included 249 trios (n = 747 individuals) comprising a fetus with an FSA detected by ultrasound and two unaffected parents as well as 46 singleton fetuses ascertained for a diagnostic procedure performed in pregnancy. Fetuses from the 249 trios were pre-screened with one or more standard-of-care diagnostic tests (karyotype, chromosomal microarray [CMA], and/or exome sequencing [ES]) and the 46 singleton fetuses were pre-selected on the basis of having a clinically reportable variant identified by one of the same three standard-of-care tests. For the 1,612 ASD quartet families, we had access to unfiltered data from CMA, ES, and GS available for analysis (see subjects and methods for more details). We performed multiple benchmarking analyses, including comparing the yield of diagnostic variants between ASD probands and their unaffected siblings, direct technology comparisons in the ASD probands, and comparisons against results from clinical diagnostic tests in the fetuses. We assessed the performance of GS by considering the overall, incremental, and sequential diagnostic yields provided by this technology. Plots are demonstrative only and are not drawn to scale nor reflective of real data.

**Figure 2**
Genome sequencing analytic framework The comprehensive framework we developed to identify diagnostic variants from GS data, which consists of four components: variant discovery, variant annotation, variant filtering, and manual variant classification. We identified nine different variant classes, including single-nucleotide variants (SNVs), small insertions and deletions (indels; below 50 base pairs), deletions (DELs) and duplications (DUPs) that ranged from over 50 base pairs to full chromosomal aneuploidies, insertions (INSs), translocations (TLOCs), inversions (INVs), complex rearrangements (CPXs), and short tandem repeats (STRs). The filtering strategy was designed to retain P/LP variants while limiting the number of variants requiring manual variant classification. The specific filtering criteria are described in the supplemental methods. All variants output by the filtering pipeline were manually curated by an expert variant review panel following existing clinical guidelines.^,^,^,^,^,^,^, All variants classified as P/LP in genes associated with the indication for testing were considered to represent the diagnostic yield of GS. ASD, autism spectrum disorder; GATK, Genome Analysis Toolkit; SV, structural variant; VUS, variant of uncertain significance; ACMG, American College of Medical Genetics and Genomics; AMP, Association for Molecular Pathology; SVI, Sequence Variant Interpretation Working Group; ClinGen, Clinical Genome Resource.

**Figure 3**
Benchmarking the performance of GS in ASD probands and unaffected siblings (A) The fraction of ASD probands and unaffected siblings identified to carry a P/LP variant by GS subset by inheritance category. The denominator used for all categories was 1,612 except for hemizygous variants where only males were considered (n = 1,440 male probands and 755 male siblings). p values were calculated by comparing the fraction of probands and siblings with a P/LP variant using Fisher's exact test. (B) The total number of P/LP variants (n = 128) detected by each technology (GS, CMA, and ES) in n = 126 ASD probands.

**Figure 4**
Overview of fetuses with structural anomalies and diagnostic yields across technologies (A) The phenotypic breakdown of 249 trio fetuses identified to have a structural anomaly detected by ultrasound included in this study. The fetuses were pre-screened with a combination of standard-of-care diagnostic tests (see subjects and methods for details). Fetuses with anomalies impacting more than one body system were counted as having multisystem abnormalities. The remaining categories represent fetuses with isolated structural anomalies. (B) The added diagnostic yield for each sequencing technology when applied serially to pre-screened fetuses. Each technology is assessed in a cohort that was depleted for diagnostic variants detected by the preceding technology. Yields for karyotype and CMA were taken from Wapner et al. and yields for ES from Petrovski et al. (C) The estimated overall diagnostic yield provided by each diagnostic test if they were applied to a cohort of unselected fetuses with structural anomalies. ^∗The yields were predicted on the basis of data from this study as well as previously published work.^, The dashed gray box surrounding the ES bar indicates the diagnostic yield that could be captured if ES-based CNV methods are applied.^, Each bar is colored on the basis of the fraction of diagnoses provided by each variant class. CMA, chromosomal microarray; ES, exome sequencing; GS, genome sequencing; SNV, single-nucleotide variant; indel, small insertion and deletion; CNV, copy-number variant; DEL, deletion; DUP, duplication; TLOC, translocation; INV, inversion.

**Figure 5**
Examples of diagnostic structural variants uniquely identified by genome sequencing (A) A 5,618 bp single exon in-frame deletion in *RERE* in an ASD proband. (B) A compound heterozygous missense variant *in trans* with an intragenic exonic duplication in *DYNC2H1* in a fetus with short-rib thoracic dysplasia. (C) An SVA retrotransposon insertion disrupting *DMD* in an ASD proband. (D) A balanced reciprocal translocation between chromosomes 12 and 13 in an ASD proband that directly disrupts *GRIN2B*. (E) Linear representation of a *de novo* complex SV impacting chromosome 1 in an ASD proband. Each rearranged segment of DNA in the derivative chromosome is depicted by a unique roman numeral (i–v), while the four deleted segments of DNA are outlined in purple and sequentially numbered DEL 1–4 (6.3 Mb total deleted). Arrows and chromosomes are not drawn to scale. Inverted segments are denoted by a reverse orientation of arrows. Genomic coordinates for this variant are provided in Table S11.

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References

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Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies

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Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies

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Conflict of interest statement

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