Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period

Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genomewide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received >= 2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs

Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period

Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genomewide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received >= 2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs

Polygenic risk score, parental socioeconomic status, family history of psychiatric disorders, and the risk for schizophrenia: a Danish population-based study and meta-analysis

IMPORTANCE Schizophrenia has a complex etiology influenced both by genetic and nongenetic factors but disentangling these factors is difficult. OBJECTIVE To estimate (1) how strongly the risk for schizophrenia relates to the mutual effect of the polygenic risk score, parental socioeconomic status, and family history of psychiatric disorders; (2) the fraction of cases that could be prevented if no one was exposed to these factors; (3) whether family background interacts with an individual's genetic liability so that specific subgroups are particularly risk prone; and (4) to what extent a proband's genetic makeup mediates the risk associated with familial background. DESIGN, SETTINGS, AND PARTICIPANTS We conducted a nested case-control study based onDanish population-based registers. The study consisted of 866 patients diagnosed as having schizophrenia between January 1, 1994, and December 31, 2006, and 871 matched control individuals. Genome-wide data and family psychiatric and socioeconomic background information were obtained from neonatal biobanks and national registers. Results from a separate meta-analysis (34 600 cases and 45 968 control individuals) were applied to calculate polygenic risk scores. EXPOSURES Polygenic risk scores, parental socioeconomic status, and family psychiatric history. MAIN OUTCOMES AND MEASURES Odds ratios (ORs), attributable risks, liability R2 values, and proportions mediated. RESULTS Schizophrenia was associated with the polygenic risk score (OR, 8.01; 95%CI, 4.53-14.16 for highest vs lowest decile), socioeconomic status (OR, 8.10; 95%CI, 3.24-20.3 for 6 vs no exposures), and a history of schizophrenia/psychoses (OR, 4.18; 95%CI, 2.57-6.79). The R2 values were 3.4%(95%CI, 2.1-4.6) for the polygenic risk score, 3.1%(95%CI, 1.9-4.3) for parental socioeconomic status, and 3.4%(95%CI, 2.1-4.6) for family history. Socioeconomic status and psychiatric history accounted for 45.8% (95%CI, 36.1-55.5) and 25.8% (95%CI, 21.2-30.5) of cases, respectively. There was an interaction between the polygenic risk score and family history (P = .03). A total of 17.4%(95%CI, 9.1-26.6) of the effect associated with family history of schizophrenia/psychoses was mediated through the polygenic risk score. CONCLUSIONS AND RELEVANCE Schizophrenia was associated with the polygenic risk score, family psychiatric history, and socioeconomic status. Our study demonstrated that family history of schizophrenia/psychoses is partly mediated through the individual's genetic liability

Genome-wide association study of febrile seizures implicates fever response and neuronal excitability genes

Febrile seizures represent the most common type of pathological brain activity in young children and are influenced by genetic, environmental and developmental factors. In a minority of cases, febrile seizures precede later development of epilepsy. We conducted a genome-wide association study of febrile seizures in 7635 cases and 83 966 controls identifying and replicating seven new loci, all with P < 5 x 10(-10). Variants at two loci were functionally related to altered expression of the fever response genes PTGER3 and IL10, and four other loci harboured genes (BSN, ERC2, GABRG2, HERC1) influencing neuronal excitability by regulating neurotransmitter release and binding, vesicular transport or membrane trafficking at the synapse. Four previously reported loci (SCN1A, SCN2A, ANO3 and 12q21.33) were all confirmed. Collectively, the seven novel and four previously reported loci explained 2.8% of the variance in liability to febrile seizures, and the single nucleotide polymorphism heritability based on all common autosomal single nucleotide polymorphisms was 10.8%. GABRG2, SCN1A and SCN2A are well-established epilepsy genes and, overall, we found positive genetic correlations with epilepsies (r(g) = 0.39, P = 1.68 x 10(-4)). Further, we found that higher polygenic risk scores for febrile seizures were associated with epilepsy and with history of hospital admission for febrile seizures. Finally, we found that polygenic risk of febrile seizures was lower in febrile seizure patients with neuropsychiatric disease compared to febrile seizure patients in a general population sample. In conclusion, this largest genetic investigation of febrile seizures to date implicates central fever response genes as well as genes affecting neuronal excitability, including several known epilepsy genes. Further functional and genetic studies based on these findings will provide important insights into the complex pathophysiological processes of seizures with and without fever.Peer reviewe

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.National Institutes of Health (U.S.) (Grant R03HG006731)National Institutes of Health (U.S.) (Fellowship F32GM106584

Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth

A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth

Covariate selection for association screening in multiphenotype genetic studies.

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size

Mixed model with correction for case-control ascertainment increases association power

We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-control studies and show that it has a well-controlled false-positive rate and more power than existing mixed-model methods for diseases with low prevalence. Existing mixed-model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem by using a chi(2) score statistic computed from posterior mean liabilities (PMLs) under the liability-threshold model. Each individual's PML is conditional not only on that individual's case-control status but also on every individual's case-control status and the genetic relationship matrix (GRM) obtained from the data. The PMLs are estimated with a multivariate Gibbs sampler; the liability-scale phenotypic covariance matrix is based on the GRM, and a heritability parameter is estimated via Haseman-Elston regression on case-control phenotypes and then transformed to the liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed-model methods for diseases with low prevalence, and the magnitude of the improvement depended on sample size and severity of case-control ascertainment. In a Wellcome Trust Case Control Consortium 2 multiple sclerosis dataset with >10,000 samples, LTMLM was correctly calibrated and attained a 4.3% improvement (p = 0.005) in chi(2) statistics over existing mixed-model methods at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, case-control studies of diseases with low prevalence can achieve power higher than that in existing mixed-model methods

Fraction of simulated local heritability explained in WTCCC2 genotypes.

<p>Analysis of simulated disease architecture with 180 causal 1Mbp loci yielding a true . In each locus, 1–10 causal variants were sampled from either low-frequency () of common (MAF) WTCCC2 SNPs. For each of four methods tested, the fraction of local heritability identified by the method is reported over 50 simulations (with standard error in parenthesis). Top two panels correspond to experiments with observed causal variants and bottom two panels to experiments with causal variants hidden. In A and B only (where causals are typed), bold-faced and represents significant difference from 100% by z-score at (accounting for 5 architectures tested). The ratio of to is reported in the bottom row of each panel (with bold-face indicating significance by t-test at ).</p

Local heritability around known GWAS loci.

<p>Components of heritability inferred at previously known GWAS loci. computed from leading SNP effect-size; computed from joint model of all known and significant SNPs in region; local expectation computed from and fraction of genome analyzed; and computed from LD adjusted variance component over all loci. (*) indicates statistically significant increase over expectation after accounting for nine tests. Error bars show analytical standard error of estimate. Autoimmune traits (CD, RA, T1D, UC, and MS) excluded the well-studied MHC region.</p