OSCR

Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder.

Code ↔ Paper

32 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 32 matches
  1. [1] § Methods › Variant annotation and prioritization ↔ Variants_filtering.py, lines 315 to 367 · score 1.00 · LOF flags, frameshift_variant, incomplete_terminal_codon_variant, inframe_deletion, inframe_insertion, missense_variant · Report a mismatch
  2. [2] § Methods › Quality control ↔ Variants_filtering.py, lines 11 to 77 · score 0.96 · allelic balance, multi allelic, variant filtering, Korean ASD, LAA, LPL · Report a mismatch
  3. [3] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ gen_phe_association.R, lines 119 to 160 · score 0.91 · SCQ lifetime, polygenic score, Clinical phenotypes, DNV carrier, constrained genes, Co occurring rare · Report a mismatch
  4. [4] § Methods › Curation of disease-association genes ↔ Figure2.R, lines 124 to 187 · score 0.90 · associated genes, ABM, ADHD, ALS, ANX, EP · Report a mismatch
  5. [5] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ pTDT_calculation.R, lines 1 to 52 · score 0.88 · polygenic transmission disequilibrium, parental PS, uni parental, pTDT, genetic factors, co occurring rare · Report a mismatch
  6. [6] § Methods › Statistical analyses ↔ Figure2.R, lines 1 to 77 · score 0.86 · compareCluster, enrichGO, pairwise_termsim, GO enrichment, MF, db · Report a mismatch
  7. [7] § Methods › Polygenic score (PS) calculation ↔ Variants_filtering.py, lines 244 to 310 · score 0.85 · chi square, GWAS summary statistics, common variants, Ambiguous, harmonize, GRCh37 · Report a mismatch
  8. [8] § Methods › Statistical analyses ↔ Figure3.R, lines 217 to 290 · score 0.84 · compareCluster, enrichGO, pairwise_termsim, GO enrichment, db, hs · Report a mismatch
  9. [9] § Methods › Variant annotation and prioritization ↔ ident_gene_pair.R, lines 59 to 85 · score 0.83 · CADD phred, BayesDel, FATHMM, MetaRNN, PROVEAN, PrimateAI · Report a mismatch
  10. [10] § Methods › Rare inherited variants ↔ Variants_filtering.py, lines 119 to 239 · score 0.83 · external AF, ultra rare inherited, internal AC, gnomAD, rare inherited variants, cohort · Report a mismatch
  11. [11] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ Figure4.R, lines 56 to 121 · score 0.80 · SCQ lifetime, DNV carrier, Co occurring rare, genetic factors, DCDQ, FSIQ · Report a mismatch
  12. [12] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ gen_phe_association.R, lines 119 to 160 · score 0.75 · BAPQ pragmatic, BAPQ rigidity, clinical phenotypes, co occurring rare, SRS, PS · Report a mismatch
  13. [13] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ Post_hoc_analysis.R, lines 1 to 57 · score 0.75 · post hoc, logistic regression, co occurrence, interaction, adjustment, covariates · Report a mismatch
  14. [14] § Methods › Polygenic score (PS) calculation ↔ run_PRScs.sh, lines 1 to 38 · score 0.72 · global shrinkage parameter, PRScs, phi, Polygenic, score · Report a mismatch
  15. [15] § Methods › De novo variants ↔ Variants_filtering.py, lines 82 to 114 · score 0.71 · de_novo, Korean ASD, DNV call, threshold, DP, GQ · Report a mismatch
  16. [16] § Methods › Identifying cell type-specific DEGs and calculating co-expression correlation of gene pairs ↔ Figure3.R, lines 217 to 290 · score 0.70 · ward.D2, hierarchical clustering, Manhattan, distance, correlation, disrupted · Report a mismatch
  17. [17] § Methods › Statistical analyses ↔ pTDT_calculation.R, lines 54 to 74 · score 0.69 · ANCOVA model, pTDT, residuals, fitting, covariance, child · Report a mismatch
  18. [18] § Methods › De novo variants ↔ Variants_filtering.py, lines 82 to 114 · score 0.69 · de_novo, gnomAD, allele frequency, Hail, AF, DNVs · Report a mismatch
  19. [19] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ Figure1.R, lines 258 to 333 · score 0.68 · CDS length, gene length, EAS pairs, EUR pairs, attributed, longer · Report a mismatch
  20. [20] § Methods › Identifying disrupted gene pair ↔ ident_gene_pair.R, lines 141 to 194 · score 0.67 · adj pval, RareComb, Python, itemsets, BH, gene pairs · Report a mismatch
  21. [21] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ Post_hoc_analysis.R, lines 1 to 57 · score 0.66 · Logistic regression, identify disrupted gene, EUR samples, co occurrence, disrupted gene pair, adjustments · Report a mismatch
  22. [22] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ pTDT_calculation.R, lines 1 to 52 · score 0.65 · uni parental, co occurring rare, phenotypic association, adjustment, children, male · Report a mismatch
  23. [23] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ Figure1.R, lines 258 to 333 · score 0.65 · median LOEUF, gene constraint, EAS pairs, EUR pairs, SFARI, Figure 1 · Report a mismatch
  24. [24] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ Figure4.R, lines 1 to 54 · score 0.63 · uni parental, co occurring rare, phenotypic association, stratifying, ADOS, male · Report a mismatch
  25. [25] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ Figure1.R, lines 337 to 408 · score 0.59 · LOEUF scores, EAS pairs, EUR pairs, median, SFARI, DNV · Report a mismatch
  26. [26] § Results › Association of co-occurring rare variants within disrupted gene pairs with ASD-related phenotypes in both affected and unaffected individuals ↔ Figure4.R, lines 56 to 121 · score 0.57 · BAPQ rigidity, co occurring rare, SRS, CI, child, PS · Report a mismatch
  27. [27] § Methods › Rare inherited variants ↔ Variants_filtering.py, lines 119 to 239 · score 0.57 · rare inherited variants, mendelian, trio, AB, family, cohort · Report a mismatch
  28. [28] § Results › Identification of disrupted gene pairs in ASD from East-Asian and European ancestries ↔ cross_evaluation.R, lines 489 to 550 · score 0.56 · Cross ancestry, EAS pairs, EUR pairs, Asian, East, European · Report a mismatch
  29. [29] § Methods › Identifying disrupted gene pair ↔ Figure1.R, lines 114 to 185 · score 0.53 · RareComb, co occurrence, itemsets, max, combo, gene pairs · Report a mismatch
  30. [30] § Methods › Cross-ancestry evaluation of disrupted gene pairs ↔ Post_hoc_analysis.R, lines 61 to 147 · score 0.52 · carrier frequencies, variance, error, model, binomial, regression · Report a mismatch
  31. [31] § Methods › Quality control ↔ Variants_filtering.py, lines 11 to 77 · score 0.52 · LCR, split, SNPs, quality, DP, GQ · Report a mismatch
  32. [32] § Results › Candidate genes in disrupted gene pairs implicated in cytoskeleton activity and neuronal cell types ↔ Figure2.R, lines 124 to 187 · score 0.51 · DDD, EA, SCZ, neuropsychiatric, CHD, neurodevelopmental · Report a mismatch

Paper

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The authors' code

Python · 438 lines · 19 KB · MIT · 8 matches

  1. #########################################################
  2. # Whole-genome variant processing pipeline
  3. #########################################################
  5. import hail as hl
  6. import time
  7. import os
  8. from functools import reduce
  11. def quality_control_wgs(vcf_path, output_dir, chr_name, lcr_bed_path):
  12. start = time.time()
  14. # 1) Import raw VCF as MatrixTable
  15. mt = hl.import_vcf(vcf_path, force_bgz=True, reference_genome='GRCh38', skip_invalid_loci=True)
  17. # 2) Filter out low-complexity regions (LCR) and very long alleles (length ≥ 50bp)
  18. lcr_bed = hl.import_bed(lcr_bed_path, reference_genome='GRCh38', min_partitions=100)
  19. mt = mt.filter_rows(~hl.is_defined(lcr_bed[mt.locus]))
  20. mt = mt.filter_rows((mt.alleles[0].length() < 50) & (mt.alleles[1].length() < 50))
  22. # 3) Recompute total depth (DP) from AD - For only Korean ASD WGS
  23. mt = mt.annotate_entries(DP = hl.sum(mt.AD))
  25. # 4) Normalize local likelihood fields (AD, LPL, LA) for multi-allelic handling - For only Korean ASD WGS
  26. mt = mt.annotate_entries(
  27. AD = hl.case()
  28. .when(mt.GT.is_hom_ref(), hl.vds.local_to_global(hl.array([mt.DP]), hl.array([0]), n_alleles=hl.len(mt.alleles), fill_value=0, number='R'))
  29. .default(mt.AD),
  30. LPL = hl.case()
  31. .when(mt.GT.is_hom_ref(), hl.missing('array<int32>'))
  32. .default(mt.LPL),
  33. LA = hl.case()
  34. .when(mt.GT.is_hom_ref(), hl.array([0]))
  35. .when(mt.GT.is_het_ref(), hl.array([hl.int32(0), hl.int32(mt.LAA[0])]))
  36. .when(mt.GT.is_hom_var(), hl.array([hl.int32(0), hl.int32(mt.LAA[0])]))
  37. .when(mt.GT.is_het_non_ref(), hl.array([hl.int32(0), hl.int32(mt.LAA[0]), hl.int32(mt.LAA[1])]))
  38. .or_missing()
  39. )
  41. # 5) Convert local PL (LPL/LA) to global diploid PLs for all alleles - For only Korean ASD WGS
  42. mt = mt.annotate_entries(PL = hl.vds.local_to_global(mt.LPL, mt.LA, n_alleles=hl.len(mt.alleles), fill_value=hl.max(mt.LPL), number='G'))
  44. # 6) Split multi-allelic sites and left-normalize to minimal representation
  45. mt = hl.split_multi_hts(mt, left_aligned=True)
  46. mt = mt.annotate_rows(min_rep = hl.min_rep(mt.locus, mt.alleles)).key_rows_by().drop('locus', 'alleles')
  47. mt = mt.annotate_rows(locus = mt.min_rep.locus, alleles = mt.min_rep.alleles).key_rows_by('locus', 'alleles')
  49. # 7) Re-define PL for hom-ref genotypes - For only Korean ASD WGS
  50. mt = mt.annotate_entries(
  51. PL = hl.case()
  52. .when(mt.GT.is_hom_ref(), [0, mt.GQ, hl.max(mt.GQ, 3*(mt.AD[0] - mt.AD[1]))])
  53. .default(mt.PL)
  54. )
  56. # 8) Annotate variant class (SNP / indel) and compute variant-level QC metrics
  57. mt = mt.annotate_rows(variant_class = hl.case().when(hl.is_snp(mt.alleles[0], mt.alleles[1]), 'snp').when(hl.is_indel(mt.alleles[0], mt.alleles[1]), 'indel').or_missing())
  58. mt = hl.variant_qc(mt)
  60. # 9) Compute allelic balance (AB) from AD
  61. mt = mt.annotate_entries(AB = mt.AD[1] / hl.sum(mt.AD))
  63. # 10) Genotype-level QC filters (DP/GQ thresholds and AB-based filters for het/hom-var)
  64. mt = mt.filter_entries(
  65. (mt.GT.is_het() & (0.2 <= mt.AB) & (mt.AB <= 0.8) & (mt.DP >= 10) & (mt.GQ >= 20)) |
  66. (mt.GT.is_hom_ref() & (mt.DP >= 10) & (mt.GQ >= 20)) |
  67. (mt.GT.is_hom_var() & (mt.AB >= 0.95) & (mt.DP >= 10) & (mt.GQ >= 20))
  68. )
  70. # 11) Run variant_qc after genotype filtering and apply variant-level QC filters
  71. mt = hl.variant_qc(mt)
  72. mt = mt.filter_rows((mt.variant_qc.call_rate >= 0.1) & (mt.variant_qc.p_value_hwe > 1e-12))
  74. # 12) Write QCed MatrixTable to disk
  75. mt.write(os.path.join(output_dir, f'WGS_chr{chr_name}_QC.mt'), overwrite=True)
  76. print(f'Done QC for chr{chr_name} : Execution time = {time.time() - start} sec')
  77. return mt
  82. def call_denovo_wgs(mt, ped_path, gnomad_ht_path, output_dir, chr_name):
  83. start = time.time()
  85. # 1) Read external allele frequency resource (gnomAD) and annotate AF to rows
  86. gnomad_ht = hl.read_table(gnomad_ht_path)
  87. mt = mt.annotate_rows(gnomAD_AF = gnomad_ht[mt.locus, mt.alleles].AF)
  89. # 2) Restrict to very rare (or absent) variants based on gnomAD AF (< 0.01%)
  90. mt_dnv = mt.filter_rows((mt.gnomAD_AF < 0.0001) | hl.is_missing(mt.gnomAD_AF))
  92. # 3) Load pedigree information and construct trio relationships
  93. trio_fam = hl.Pedigree.read(ped_path)
  95. # 4) Run Hail's trio-based de novo caller with depth/AB/GQ-based thresholds
  96. # dnv_ht = modified_de_novo( # - For only Korean ASD WGS
  97. dnv_ht = hl.de_novo(
  98. mt = mt_dnv,
  99. pedigree = trio_fam,
  100. pop_frequency_prior= mt.gnomAD_AF,
  101. max_parent_ab = 0.1,
  102. min_child_ab = 0.3,
  103. min_dp_ratio = 0.3,
  104. min_gq = 20
  105. #min_p = 0.05 # - For only Korean ASD WGS
  106. )
  108. # 5) Keep only HIGH and MEDIUM confidence de novo candidates
  109. dnv_ht = dnv_ht.filter((dnv_ht.confidence == "HIGH") | (dnv_ht.confidence == "MEDIUM"))
  111. # 6) Write de novo results as Hail Table
  112. dnv_ht.write(os.path.join(output_dir, f'WGS_chr{chr_name}_DNV.ht'), overwrite=True)
  113. print(f'Done DNV calling for chr{chr_name} : Execution time = {time.time() - start} sec')
  114. return dnv_ht
  119. def process_rare_inherited_wgs(mt, gnomad_ht_path, ped_path, sample_info_path, internal_ac_ht_path, output_dir, chr_name):
  120. start = time.time()
  122. # 1) Read external AF resource (gnomAD) and annotate allele frequency to rows
  123. gnomad_ht = hl.read_table(gnomad_ht_path)
  124. mt = mt.annotate_rows(gnomAD_AF = gnomad_ht[mt.locus, mt.alleles].AF)
  126. # 2) Genotype-level QC for inherited variants (AB, PL, DP, GQ filters by genotype class)
  127. mt = mt.filter_entries(
  128. hl.case()
  129. .when(mt.GT.is_het(), (0.22 <= mt.AB) & (mt.AB <= 0.78) & (mt.PL[0] >= 25) & (mt.DP >= 10) & (mt.GQ >= 20))
  130. .when(mt.GT.is_hom_var(), (mt.AB >= 0.95) & (mt.PL[0] >= 25) & (mt.DP >= 10) & (mt.GQ >= 20))
  131. .default( (mt.DP >= 10) & (mt.GQ >= 20))
  132. )
  134. # 3) Variant-level QC (call rate and HWE)
  135. mt = mt.filter_rows((mt.variant_qc.call_rate >= 0.1) & (mt.variant_qc.p_value_hwe > 1e-12))
  137. # 4) Define rare and ultra-rare variant subsets based on external AF
  138. mt_rare = mt.filter_rows((mt.gnomAD_AF < 0.001) | hl.is_missing(mt.gnomAD_AF))
  139. mt_ultra_rare = mt.filter_rows((mt.gnomAD_AF < 0.00001) | hl.is_missing(mt.gnomAD_AF))
  141. # 5) Annotate sample-level metadata (role, family ID)
  142. info = hl.import_table(sample_info_path, delimiter='\t', key='s')
  143. mt_rare = mt_rare.annotate_cols(role = info[mt_rare.s].role,
  144. fam_id = info[mt_rare.s].fam_id)
  146. # 6) Annotate internal allele count (AC) from external cohort resource - For only EAS samples
  147. internal_ac_ht = hl.read_table(internal_ac_ht_path)
  148. mt_rare = mt_rare.annotate_rows(
  149. internal_AC = hl.or_else(internal_ac_ht[mt_rare.locus, mt_rare.alleles].internal_AC, 0))
  150. mt_ultra_rare = mt_ultra_rare.annotate_rows(
  151. internal_AC = hl.or_else(internal_ac_ht[mt_ultra_rare.locus, mt_ultra_rare.alleles].internal_AC, 0))
  153. # 7) Apply cohort-specific frequency filters using internal AC from external resource - For only EAS samples
  154. mt_rare = mt_rare.filter_rows(mt_rare.internal_AC < 18)
  155. mt_ultra_rare = mt_ultra_rare.filter_rows(mt_ultra_rare.internal_AC < 2)
  157. # 8) Checkpoint rare / ultra-rare sets BEFORE Mendelian error filtering
  158. mt_rare.write(
  159. os.path.join(output_dir, f'WGS_chr{chr_name}_rare_pre_mendelian.mt'),
  160. overwrite=True
  161. )
  162. mt_ultra_rare.write(
  163. os.path.join(output_dir, f'WGS_chr{chr_name}_ultra_rare_pre_mendelian.mt'),
  164. overwrite=True
  165. )
  167. # 9) Restrict to trio samples for inheritance pattern analysis - For only trio-based dataset (excluding non-trio cohorts)
  168. pedigree = hl.Pedigree.read(ped_path)
  169. trios = hl.array(sum([[x.s, x.mat_id, x.pat_id] for x in pedigree.complete_trios()], []))
  171. # --------------------------
  172. # 9-1) Mendelian filtering for rare variants
  173. # --------------------------
  174. mt_trio_rare = mt_rare.filter_cols(trios.contains(mt_rare.s))
  176. tm_rare = hl.trio_matrix(mt_trio_rare, pedigree, complete_trios=True)
  177. p_tm_rare = hl.experimental.phase_trio_matrix_by_transmission(tm_rare)
  179. p_tm_rare = p_tm_rare.annotate_entries(
  180. transmission_pattern = (
  181. hl.case()
  182. # transmission-based patterns from phased proband genotype
  183. .when(hl.is_defined(p_tm_rare.proband_entry.PBT_GT) & (p_tm_rare.proband_entry.PBT_GT == hl.call(1, 0, phased=True)), 'father')
  184. .when(hl.is_defined(p_tm_rare.proband_entry.PBT_GT) & (p_tm_rare.proband_entry.PBT_GT == hl.call(0, 1, phased=True)), 'mother')
  185. .when(hl.is_defined(p_tm_rare.proband_entry.PBT_GT) & (p_tm_rare.proband_entry.PBT_GT == hl.call(1, 1, phased=True)), 'both')
  186. # parental-only carrier pattern (no phased transmission available for proband)
  187. .when(
  188. hl.is_missing(p_tm_rare.proband_entry.PBT_GT) &
  189. (
  190. (hl.is_defined(p_tm_rare.father_entry.GT) & p_tm_rare.father_entry.GT.is_non_ref()) |
  191. (hl.is_defined(p_tm_rare.mother_entry.GT) & p_tm_rare.mother_entry.GT.is_non_ref())
  192. ),
  193. 'Parental'
  194. )
  195. .or_missing()
  196. )
  197. )
  199. valid = ['father', 'mother', 'both', 'Parental']
  200. mt_rare_filtered = p_tm_rare.filter_entries(hl.array(valid).contains(p_tm_rare.transmission_pattern))
  202. # 9-2) Mendelian filtering for ultra-rare variants (same logic)
  203. mt_trio_ultra = mt_ultra_rare.filter_cols(trios.contains(mt_ultra_rare.s))
  205. tm_ultra = hl.trio_matrix(mt_trio_ultra, pedigree, complete_trios=True)
  206. p_tm_ultra = hl.experimental.phase_trio_matrix_by_transmission(tm_ultra)
  208. p_tm_ultra = p_tm_ultra.annotate_entries(
  209. transmission_pattern = (
  210. hl.case()
  211. .when(hl.is_defined(p_tm_ultra.proband_entry.PBT_GT) & (p_tm_ultra.proband_entry.PBT_GT == hl.call(1, 0, phased=True)), 'father')
  212. .when(hl.is_defined(p_tm_ultra.proband_entry.PBT_GT) & (p_tm_ultra.proband_entry.PBT_GT == hl.call(0, 1, phased=True)), 'mother')
  213. .when(hl.is_defined(p_tm_ultra.proband_entry.PBT_GT) & (p_tm_ultra.proband_entry.PBT_GT == hl.call(1, 1, phased=True)), 'both')
  214. .when(
  215. hl.is_missing(p_tm_ultra.proband_entry.PBT_GT) &
  216. (
  217. (hl.is_defined(p_tm_ultra.father_entry.GT) & p_tm_ultra.father_entry.GT.is_non_ref()) |
  218. (hl.is_defined(p_tm_ultra.mother_entry.GT) & p_tm_ultra.mother_entry.GT.is_non_ref())
  219. ),
  220. 'Parental'
  221. )
  222. .or_missing()
  223. )
  224. )
  226. mt_ultra_rare_filtered = p_tm_ultra.filter_entries(hl.array(valid).contains(p_tm_ultra.transmission_pattern))
  228. # 10) Write rare / ultra-rare inherited variant MatrixTables to disk
  229. mt_rare_filtered.write(
  230. os.path.join(output_dir, f'WGS_chr{chr_name}_rare_inherited.mt'),
  231. overwrite=True
  232. )
  233. mt_ultra_rare_filtered.write(
  234. os.path.join(output_dir, f'WGS_chr{chr_name}_ultra_rare_inherited.mt'),
  235. overwrite=True
  236. )
  238. print(f'Done rare & ultra-rare inherited processing for chr{chr_name} : Execution time = {time.time() - start} sec')
  239. return mt_rare_filtered
  244. def prepare_for_prs_wgs(mt, gwas_sum_path, liftover_chain_path, output_dir, chr_name):
  245. start = time.time()
  247. # 1) Variant-level QC for common variants
  248. mt = mt.filter_rows(
  249. (mt.variant_qc.call_rate >= 0.95) &
  250. (mt.variant_qc.p_value_hwe >= 1e-6) &
  251. (mt.variant_qc.AF[1] > 0.05))
  253. # 2) Register liftover chain and perform GRCh38 → GRCh37 coordinate liftOver
  254. rg37 = hl.get_reference('GRCh37')
  255. rg38 = hl.get_reference('GRCh38')
  256. rg38.add_liftover(liftover_chain_path, rg37)
  258. mt = mt.annotate_rows(new_locus = hl.liftover(mt.locus, 'GRCh37', include_strand=True))
  259. mt = mt.filter_rows(hl.is_defined(mt.new_locus) & ~mt.new_locus.is_negative_strand)
  260. mt = mt.key_rows_by(locus = mt.new_locus.result, alleles = mt.alleles)
  262. # 3) Reformat long alleles (>13bp) for compatible representation
  263. mt = mt.annotate_rows(
  264. new_alleles = hl.array([
  265. (mt.alleles[0][:13] + '+' + hl.str(mt.alleles[0].length() - 13)) if (mt.alleles[0].length() > 13) else mt.alleles[0],
  266. (mt.alleles[1][:13] + '+' + hl.str(mt.alleles[1].length() - 13)) if (mt.alleles[1].length() > 13) else mt.alleles[1],
  267. ]))
  269. # 4) Remove strand-ambiguous variants (A/T, C/G) to avoid strand issues
  270. mt = mt.filter_rows(~hl.is_strand_ambiguous(mt.new_alleles[0], mt.new_alleles[1]))
  272. # 5) Read GWAS summary statistics (on GRCh37) with locus + allele keys
  273. gwas = hl.import_table(
  274. gwas_sum_path,
  275. types={'locus': hl.tlocus(reference_genome='GRCh37')},
  276. impute=True,
  277. delimiter='\t',
  278. force_bgz=True,
  279. key=['locus', 'A1', 'A2'])
  281. # 6) Compute cohort AC/AN and align with GWAS AF for frequency harmonization
  282. rows = mt.rows().key_by('locus', 'alleles')
  283. rows = rows.annotate(
  284. AC1 = rows.variant_qc.AC[0],
  285. AC2 = rows.variant_qc.AC[1],
  286. N = rows.variant_qc.AN,
  287. gwas_AF = gwas[rows.locus, rows.alleles[0], rows.alleles[1]].F)
  289. # 7) Calculate chi-square statistic to flag AF-discordant variants
  290. rows = rows.annotate(
  291. chi_stat = (
  292. (rows.AC1 - rows.N * rows.gwas_AF) ** 2 / (rows.N * rows.gwas_AF)
  293. ) + (
  294. (rows.AC2 - rows.N * (1 - rows.gwas_AF)) ** 2 / (rows.N * (1 - rows.gwas_AF))
  295. ))
  297. # 8) Remove variants with outlier AF discrepancy (chi-square > mean + 1 SD)
  298. stats = rows.aggregate(hl.agg.stats(rows.chi_stat))
  299. threshold = stats['mean'] + stats['stdev']
  300. mt = mt.filter_rows(rows[mt.row_key].chi_stat < threshold)
  302. # 9) Export PRS-ready genotype data in PLINK format (for PRScs / PRS pipeline)
  303. hl.export_plink(
  304. mt,
  305. os.path.join(output_dir, f"WGS_chr{chr_name}_PRS_ready"),
  306. varid = mt.SNP,
  307. ind_id = mt.s)
  309. print(f'Done PRS preparation for chr{chr_name} : Execution time = {time.time() - start} sec')
  310. return mt
  315. def annotate_and_prioritize_variants(variant_table, vep_config_path, mpc_ht_path, constraint_ht_path, output_dir):
  316. # 1) Run VEP to obtain functional annotations for all variants
  317. vt = hl.vep(variant_table, vep_config_path)
  319. # 2) Extract key transcript-level metadata (gene symbol, transcript ID, gene ID)
  320. vt = vt.annotate(
  321. gene_symbol = vt.vep.transcript_consequences.gene_symbol[0],
  322. transcript_id = vt.vep.transcript_consequences.transcript_id[0],
  323. gene_id = vt.vep.transcript_consequences.gene_id[0])
  325. # 3) Annotate variant-level MPC scores by locus / alleles / transcript
  326. mpc = hl.read_table(mpc_ht_path).key_by('locus', 'alleles', 'ENST')
  327. vt = vt.key_by(vt.locus, vt.alleles, vt.transcript_id)
  328. vt = vt.annotate(MPC = mpc[vt.locus, vt.alleles, vt.transcript_id].MPC)
  330. # 4) Annotate gene-level constraint metrics (e.g. pLI, LOEUF) from external constraint resource
  331. cons = hl.read_table(constraint_ht_path).key_by('gene', 'transcript')
  332. vt = vt.key_by(vt.gene_symbol, vt.transcript_id)
  333. vt = vt.annotate(constraint = cons[vt.gene_symbol, vt.transcript_id])
  334. vt = vt.key_by(vt.locus, vt.alleles)
  336. # 5) Define protein-truncating variants (PTV) using consequence + LOF annotation from VEP
  337. ptv = (
  338. hl.array(['frameshift_variant', 'splice_acceptor_variant', 'splice_donor_variant', 'stop_gained'])
  339. .contains(vt.vep.most_severe_consequence) &
  340. (vt.vep.transcript_consequences.lof[0] == 'HC') &
  341. (
  342. (vt.vep.transcript_consequences.lof_flags[0] == 'SINGLE_EXON') |
  343. hl.is_missing(vt.vep.transcript_consequences.lof_flags[0])
  344. ))
  346. # 6) Group remaining functional classes: missense, inframe indel, synonymous
  347. missense = hl.array(['missense_variant', 'stop_lost', 'start_lost', 'protein_altering_variant']).contains(vt.vep.most_severe_consequence)
  348. inframe_indel = hl.array(['inframe_deletion', 'inframe_insertion']).contains(vt.vep.most_severe_consequence)
  349. synonymous = hl.array(['synonymous_variant', 'stop_retained_variant', 'incomplete_terminal_codon_variant']).contains(vt.vep.most_severe_consequence)
  351. # 7) Assign a coarse variant category label (VariantType) for downstream prioritization
  352. vt = vt.annotate(
  353. VariantType = hl.case()
  354. .when(ptv, 'protein_truncating')
  355. .when(missense, 'missense')
  356. .when(inframe_indel, 'inframe_indel')
  357. .when(synonymous, 'synonymous')
  358. .or_missing())
  360. # 8) Restrict to protein-coding transcripts with a defined variant category
  361. vt = vt.filter(
  362. (vt.vep.transcript_consequences.biotype[0] == 'protein_coding') &
  363. hl.is_defined(vt.VariantType))
  365. # 9) Write annotated and categorized variant table to disk
  366. vt.write(os.path.join(output_dir, 'WGS_annotated.ht'), overwrite=True)
  367. return vt
  370. #########################################################
  371. # Driver script
  372. #########################################################
  374. if __name__ == "__main__":
  375. hl.init(log="/tmp/hail_pipeline.log")
  377. vcf_path = "/path/to/joint_called.vcf.bgz"
  378. gnomad_ht_path = "/path/to/gnomad.af.ht"
  379. lcr_bed_path = "/path/to/LCR-hs38.bed"
  380. ped_path = "/path/to/family.ped"
  381. sample_info_path = "/path/to/sample_info.txt"
  382. internal_ac_ht_path = "/path/to/EAS_internal_AC.ht"
  383. liftover_chain_path = "/path/to/grch38_to_grch37.over.chain.gz"
  384. gwas_sum_path = "/path/to/gwas_sumstats.txt.bgz"
  385. vep_config_path = "/path/to/vep_config.json"
  386. mpc_ht_path = "/path/to/MPC38.ht"
  387. constraint_ht_path = "/path/to/constraint_metrics.ht"
  389. output_dir = "/path/to/output_dir/"
  390. chr_name = "1"
  392. # QC
  393. mt_qc = quality_control_wgs(
  394. vcf_path = vcf_path,
  395. output_dir = output_dir,
  396. chr_name = chr_name,
  397. lcr_bed_path = lcr_bed_path
  398. )
  400. # De novo variants
  401. dnv_ht = call_denovo_wgs(
  402. mt = mt_qc,
  403. ped_path = ped_path,
  404. gnomad_ht_path= gnomad_ht_path,
  405. output_dir = output_dir,
  406. chr_name = chr_name
  407. )
  409. # Rare / ultra-rare inherited variants
  410. mt_inherited_rare = process_rare_inherited_wgs(
  411. mt = mt_qc,
  412. gnomad_ht_path = gnomad_ht_path,
  413. ped_path = ped_path,
  414. sample_info_path = sample_info_path,
  415. internal_ac_ht_path= internal_ac_ht_path,
  416. output_dir = output_dir,
  417. chr_name = chr_name
  418. )
  420. # PRS preparation
  421. mt_prs = prepare_for_prs_wgs(
  422. mt = mt_qc,
  423. gwas_sum_path = gwas_sum_path,
  424. liftover_chain_path= liftover_chain_path,
  425. output_dir = output_dir,
  426. chr_name = chr_name
  427. )
  429. # Functional annotation and prioritization (example: de novo variants)
  430. annotated_ht = annotate_and_prioritize_variants(
  431. variant_table = dnv_ht,
  432. vep_config_path = vep_config_path,
  433. mpc_ht_path = mpc_ht_path,
  434. constraint_ht_path=constraint_ht_path,
  435. output_dir = output_dir
  436. )
  438. print("Pipeline completed.")

Variants_filtering.py at commit e0e7f6a, under MIT · at the source

Overview

Authors: Hyeji Lee1,2,3, Kahee Ko4, Seoyeon Kim1,2, Ganghee Lee1,2, Soowhee Kim1,2,3, Jihae Lee5, Da-Yea Song6,7, Guiyoung Bong6, Jae Hyun Han6,7, Jeewon Lee8, Ye Rim Kim6,7, Yoojeong Lee6,7, Eunjoon Kim9,10, Anders D. Børglum11,12,13, Jakob Grove11,12,13,14, So Hyun Kim15, Woong Sun4, Hee Jeong Yoo6,7, Joon-Yong An1,2,3,5
ORCID iDs: Jakob Grove, Woong Sun, Joon-Yong An
15 affiliations
  1. Department of Integrated Biomedical and Life Science, Korea University,Seoul, 02841 Republic of Korea
  2. L-HOPE Program for Community-Based Total Learning Health Systems, Korea University,Seoul, 02841 Republic of Korea
  3. National Research Laboratory for Convergence Degradation Biology, Korea University,Seoul, 02841 Republic of Korea
  4. Department of Anatomy, Korea University College of Medicine,Seoul, 02841 Republic of Korea
  5. School of Biosystems and Biomedical Science, College of Health Science, Korea University,Seoul, 02841 Republic of Korea
  6. Department of Psychiatry, Seoul National University Bundang Hospital,Seongnam, 13620 Republic of Korea
  7. Department of Psychiatry, Seoul National University College of Medicine,Seoul, 03080 Republic of Korea
  8. Department of Psychiatry, Soonchunhyang University Bucheon Hospital,Bucheon-si, Gyeonggi-do Republic of Korea
  9. Department of Biological Sciences, Korea Advanced Institute of Science and Technology,Daejeon, Republic of Korea
  10. Center for Synaptic Brain Dysfunctions, Institute for Basic Science,Daejeon, Republic of Korea
  11. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH,Aarhus, Denmark
  12. Center for Genomics and Personalized Medicine, Aarhus, Denmark
  13. Department of Biomedicine, Aarhus University,Aarhus, Denmark
  14. Bioinformatics Research Centre, BiRC, Aarhus University,Aarhus, Denmark
  15. School of Psychology, Korea University,Seoul, Republic of Korea
Institutions: Korea University (South Korea); Seoul National University Bundang Hospital (South Korea); Seoul National University (South Korea); Soonchunhyang University Hospital Bucheon (South Korea); Korea Advanced Institute of Science and Technology (South Korea); Institute for Basic Science (South Korea); Lundbeck Foundation (Denmark); Aarhus University (Denmark)
Journal: Genome biology, volume 27, issue 1, article 147
Dates: received 14 May 2025; accepted 11 March 2026; published online 23 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s13059-026-04041-x · PMID 41872878 · PMCID PMC13134247 · OpenAlex W7140140243
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), autism (population), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Connectivity
Keywords: Whole-genome sequencing, Whole-exome sequencing, ASD, Rare variants, Gene combinations, Cytoskeleton pathway, Cell-type specificity, Phenotypic heterogeneity
MeSH: Autism Spectrum Disorder*, Cytoskeleton*, Genetic Variation*, Female, Genetic Predisposition to Disease, Humans, Male, Phenotype (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2024-00411597, RS-2023-00209635, RS-2021-NR058507, NRF-2021M3E5D9021878, RS-2025-16652968); Institute for Basic Science (IBS-R002-D1); Lundbeckfonden (R102-A9118); national institutes of health (1R01NS131433-01); Korea University Research Grant (K2420391)
Citations: cited by 1 paper (Europe PMC); 102 references in the paper

Abstract

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Leehyeji789/Co-occurrence-analysis-in-ASD

License: MIT
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Commit: e0e7f6aaf44990220c3a15ca7166f37ab4550eb5, 24 November 2025
Languages: R (15), Python (2), Shell (1)
Size: 21 files, 18 scripts
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Holds: README, license file
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Tools: tidyverse (15 files), data.table (9 files), broom (5 files), ggpubr (4 files), cowplot (3 files), ggplot2 (3 files), clusterProfiler (2 files), reticulate (2 files), patchwork (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
20 files
At the source: github.com/Leehyeji789/Co-occurrence-analysis-in-ASD

Zenodo 17695517

License: MIT
State: the link answers, verified on 30 September 2026
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Size: 1 file
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Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
20 files
At the source: doi.org/10.5281/zenodo.17695517

The paper's code and data availability statement is in the Data section.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 8 keywords, 8 MeSH terms, 5 funders, 92 references.

Cite

This paper

Lee, H., Ko, K., Kim, S., Lee, G., Kim, S., Lee, J., Song, D.-Y., Bong, G., Han, J. H., Lee, J., Kim, Y. R., Lee, Y., Kim, E., Børglum, A. D., Grove, J., Kim, S. H., Sun, W., Yoo, H. J., & An, J.-Y. (2026). Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder. Genome biology, 27(1), 147. https://doi.org/10.1186/s13059-026-04041-x

BibTeX

@article{lee2026co,
author = {Lee, Hyeji and Ko, Kahee and Kim, Seoyeon and Lee, Ganghee and Kim, Soowhee and Lee, Jihae and Song, Da-Yea and Bong, Guiyoung and Han, Jae Hyun and Lee, Jeewon and Kim, Ye Rim and Lee, Yoojeong and Kim, Eunjoon and Børglum, Anders D. and Grove, Jakob and Kim, So Hyun and Sun, Woong and Yoo, Hee Jeong and An, Joon-Yong},
title = {{Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder}},
journal = {Genome biology},
year = {2026},
month = mar,
volume = {27},
number = {1},
pages = {147},
publisher = {BMC},
issn = {1474-7596},
doi = {10.1186/s13059-026-04041-x},
url = {https://doi.org/10.1186/s13059-026-04041-x},
pmid = {41872878},
pmcid = {PMC13134247}
}

RIS

TY - JOUR
AU - Lee, Hyeji
AU - Ko, Kahee
AU - Kim, Seoyeon
AU - Lee, Ganghee
AU - Kim, Soowhee
AU - Lee, Jihae
AU - Song, Da-Yea
AU - Bong, Guiyoung
AU - Han, Jae Hyun
AU - Lee, Jeewon
AU - Kim, Ye Rim
AU - Lee, Yoojeong
AU - Kim, Eunjoon
AU - Børglum, Anders D.
AU - Grove, Jakob
AU - Kim, So Hyun
AU - Sun, Woong
AU - Yoo, Hee Jeong
AU - An, Joon-Yong
TI - Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder
T2 - Genome biology
J2 - Genome Biol
PY - 2026
DA - 2026/03/23
VL - 27
IS - 1
SP - 147
SN - 1474-7596
PB - BMC
DO - 10.1186/s13059-026-04041-x
UR - https://doi.org/10.1186/s13059-026-04041-x
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13059-026-04041-x",
"type": "article-journal",
"title": "Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder",
"container-title": "Genome biology",
"author": [
{
"family": "Lee",
"given": "Hyeji"
},
{
"family": "Ko",
"given": "Kahee"
},
{
"family": "Kim",
"given": "Seoyeon"
},
{
"family": "Lee",
"given": "Ganghee"
},
{
"family": "Kim",
"given": "Soowhee"
},
{
"family": "Lee",
"given": "Jihae"
},
{
"family": "Song",
"given": "Da-Yea"
},
{
"family": "Bong",
"given": "Guiyoung"
},
{
"family": "Han",
"given": "Jae Hyun"
},
{
"family": "Lee",
"given": "Jeewon"
},
{
"family": "Kim",
"given": "Ye Rim"
},
{
"family": "Lee",
"given": "Yoojeong"
},
{
"family": "Kim",
"given": "Eunjoon"
},
{
"family": "Børglum",
"given": "Anders D."
},
{
"family": "Grove",
"given": "Jakob"
},
{
"family": "Kim",
"given": "So Hyun"
},
{
"family": "Sun",
"given": "Woong"
},
{
"family": "Yoo",
"given": "Hee Jeong"
},
{
"family": "An",
"given": "Joon-Yong"
}
],
"container-title-short": "Genome Biol",
"volume": "27",
"issue": "1",
"page": "147",
"DOI": "10.1186/s13059-026-04041-x",
"PMID": "41872878",
"PMCID": "PMC13134247",
"ISSN": "1474-7596",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s13059-026-04041-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
23
]
]
}
}

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