mQTLs in ART

parents vs children

Julia Romanowska

2024-08-09

Photo by Sergei Gussev on Unsplash

OUTLINE

mQTLs

DNAm reprogramming

methods

some results

discussion

mQTL

SNP–CpG pair

• cis-mQTLs and trans-mQTLs

• indicating a functional connection between
  methylation status of that CpG and
  allele at that SNP

DNAm reprogramming

wave of demethylation followed by rapid re-methylation

• paternal DNA is demethylated faster than maternal

• at blastocyst stage, DNAm level is lowest (ca.20% of all CpGs)

• freezing of eggs could influence de-methylation and differentiation

[src: Albert and Greenberg, Development (2023); Greenberg, Front.Cell Develop.Biol. (2021)]

Blastocyst = naive pluripotency stage; only imprinted regions and transpose elements (TEs) are methylated!

However, a lot of signalling is also done by methylation of histones!

Aim

to check for differences in existence of mQTLs between:

• parents vs children

• ART vs non-ART

methods

data

• genetic data - MoBa

• epigenetic data - START

methods

tools

• PLINK

• (MatrixEQTL package)

• snakemake

• JuliaLang

methods

1. agnostic search for mQTLs in parents

2. select the significant mQTLs

3. check for association in children
   using two models:

• DNAm \(\sim\) SNP \(+\) case_control_status
• DNAm \(\sim\) SNP \(\times\) case_control_status

Results

mQTLs in parents

How many mQTL pairs?

for chromosome 20 only!

	# tests	# all mQTLs	# signif mQTLs[*]
cis	107225852	107225852	6175
trans	3191581264	256233	9105

[*] significance for cis based on FDR, for trans, based on Bonferroni

Where are the significant mQTL pairs?

• Genomic position
• Relative distance

How many connections?

• Per SNP
• Per CpG

Results

mQTLs in children

Where are the confirmed mQTLs?

model: additive

296 mQTLs (out of 6175) were confirmed in children

Where are the mQTLs with interaction?

model: interaction

314 mQTLs (out of 6175) had significant interaction with ART-status in children

Zooming in…

• Part 1
• Part 2
• Part 3

Part1

Part2

Part3

Discussion

DNAmethylation depends on a lot of variables! e.g., folate supplement or vitamin supplement during pregnancy

Extra slides

‘Strongest’ associations, additive model

           SNP       gene   term    estimate   std.error statistic      p.value
        <char>     <char> <char>       <num>       <num>     <num>        <num>
1: rs142527558 cg10797920   SNP1  0.03434030 0.007438696  4.616441 4.187951e-06
2:  rs79359164 cg26680502   SNP1  0.01845339 0.006365479  2.898980 3.790711e-03
3:  rs79033469 cg00794607   SNP1 -0.01758346 0.008407823 -2.091322 3.664345e-02
4:  rs35849074 cg26680502   SNP1  0.01739812 0.006135291  2.835745 4.624445e-03
5: rs188100838 cg26680502   SNP1  0.01596453 0.005673459  2.813898 4.949335e-03
6:   rs8126087 cg20937230   SNP1 -0.01574302 0.007539033 -2.088202 3.692445e-02
       conf.low     conf.high  SNP_pos  CpG_pos
          <num>         <num>    <int>    <int>
1:  0.019750642  0.0489299635 61586712 62495449
2:  0.005968599  0.0309381864 37764667 37433765
3: -0.034073923 -0.0010930005 30953516 30143151
4:  0.005364851  0.0294313831 37659385 37433765
5:  0.004837043  0.0270920202 37462351 37433765
6: -0.030529526 -0.0009565226   976128   725968

           SNP       gene   term     estimate   std.error statistic
        <char>     <char> <char>        <num>       <num>     <num>
1: rs142892531 cg04446876   SNP1 -0.009995664 0.001500043 -6.663587
2:  rs41278236 cg11145914   SNP1 -0.010154342 0.001545462 -6.570425
3:  rs16987038 cg20606255   SNP1  0.011110467 0.001986969  5.591665
4:  rs45555040 cg20606255   SNP1  0.011083456 0.001986446  5.579541
5: rs144117172 cg10817792   SNP1 -0.005553789 0.001184940 -4.686979
6: rs142527558 cg10797920   SNP1  0.034340303 0.007438696  4.616441
        p.value     conf.low    conf.high  SNP_pos  CpG_pos
          <num>        <num>        <num>    <int>    <int>
1: 3.577903e-11 -0.012937746 -0.007053582 59699754 60630424
2: 6.595683e-11 -0.013185485 -0.007123199 62641605 62564447
3: 2.604532e-08  0.007213384  0.015007551 36551349 36839442
4: 2.789096e-08  0.007187401  0.014979512 36794916 36839442
5: 2.988150e-06 -0.007877844 -0.003229734 57292298 57004379
6: 4.187951e-06  0.019750642  0.048929963 61586712 62495449

Volcano plot, additive model

‘Strongest’ associations, interaction model

            SNP       gene                   term    estimate   std.error
         <char>     <char>                 <char>       <num>       <num>
 1: rs142892531 cg04446876 SNP1:case_controlSTART  0.01944484 0.002965239
 2:  rs16987038 cg20606255 SNP1:case_controlSTART -0.02364613 0.003937080
 3:  rs45555040 cg20606255 SNP1:case_controlSTART -0.02352914 0.003936437
 4: rs117654917 cg12204262 SNP1:case_controlSTART  0.02264140 0.004103064
 5: rs144117172 cg10817792 SNP1:case_controlSTART -0.01298584 0.002358332
 6:  rs41278236 cg11145914 SNP1:case_controlSTART -0.01432727 0.003105520
 7: rs142527558 cg10797920 SNP1:case_controlSTART -0.06688452 0.014945962
 8: rs117226334 cg19122601 SNP1:case_controlSTART  0.01394780 0.003153570
 9: rs117696074 cg09453598 SNP1:case_controlSTART  0.01940239 0.004410773
10: rs182484229 cg06448079 SNP1:case_controlSTART  0.01415210 0.003286212
    statistic      p.value     conf.low    conf.high  SNP_pos  CpG_pos
        <num>        <num>        <num>        <num>    <int>    <int>
 1:  6.557597 7.194759e-11  0.013629022  0.025260666 59699754 60630424
 2: -6.006009 2.307551e-09 -0.031368012 -0.015924256 36551349 36839442
 3: -5.977269 2.743626e-09 -0.031249754 -0.015808525 36794916 36839442
 4:  5.518168 3.939374e-08  0.014593963  0.030688837  2106677  1249928
 5: -5.506367 4.209472e-08 -0.017611304 -0.008360381 57292298 57004379
 6: -4.613485 4.247084e-06 -0.020418188 -0.008236356 62641605 62564447
 7: -4.475089 8.130124e-06 -0.096198338 -0.037570694 61586712 62495449
 8:  4.422859 1.034044e-05  0.007762630  0.020132964 55517482 56254598
 9:  4.398864 1.154167e-05  0.010751414  0.028053370 38891003 39288278
10:  4.306509 1.750905e-05  0.007706764  0.020597434 57327746 57565177