Case ascertainment (setting, method)
Cases (% male)
Controls (% male)
Mean (SD) age
Summary of findings
Logue et al. (2013) 
Combat trauma (most common), various
Setting: VA sample
NHW: 195 (NR)
51.5 (10.9) in trauma-exposed subsample
NHW: rs8042149 (RORA, linked to other psychiatric disorders) associated with PTSD (genome wide)
(65% male in total sample)
AA1: 41 (NR)
AA: 2 other RORA SNPs nominally significant; only the SNP in veterans survived correction
AA1: 43 (NR)
AA2: 421 (NR)
AA2: 100 (NR)
Xie et al. (2013) 
Setting: university clinics
AA : 444 (45.7)
AA: 2322 (56.9) NHW: 1278 (64.6)
AA: 41.5 (8.7) NHW: 37.7 (9.8)
N (initially; restricted later)
Genome-wide significant: rs406001 (chromosome 7p12)
Method: SSADDA, DSM-IV
NHW: 300 (39.7)
Next best association: TLL1 gene
6 TLL1 SNPs tested in independent replication sample; rs6812849 and rs7691872 were successfully replicated in EAs; none for AAs
Restricted analyses to TEs only: did not change results
Guffanti et al. (2013) 
Setting: DNHS sample; longitudinal cohort study
52.2 (13.5) [cases]; 54.3 (15.9) [controls]
Genome-wide significant: rs10170218 (lincRNA AC068718.1 gene)
Method: structured telephone interview
“Suggestive” evidence for replication in NHSII sample
Top hits enriched for telomere and immune pathways
Wolf et al. (2014) 
Setting: VA clinic
293 (65% male in total sample)
52 (R = 21–75)
No significant associations
rs263232 (ADCY8; involved in memory/LTP) most significant SNP
rs71534169 (PBB6; involved in synaptic processes) also suggestive
Nievergelt et al. (2015) 
Setting: veteran sample
PRTFDC1 (rs6482463) was genome-wide significant via meta-analysis and replicated in an independent sample
25 loci had suggestive associations
PTSD associated with BPD risk score in PRS analysis
Ashley-Koch et al. (2015) 
Setting: VA and university clinic
AA: 949 total (69.7% total)
AA: 39.06 (9.73)
See next column
No SNPs with genome-wide significance
Method: SCID, CAPS
NHW: 36.28 (10.44)
Best SNPS in AAs: rs10768747, rs17504106, rs73419609, rs2862383, rs834811
NHW: 759 total (84.9% total)
Best SNPs in NHW: rs7866350, rs1116255, rs2437772, rs61793204
Best SNPs in meta-analysis (NHW and AA combined): rs12232346, rs10762479, rs10002308
Pathway analysis: alternative splicing
Stein et al. (2016) 
Setting: army cohorts (NSS and PPDS)
NSS: 3167 (78.4)
NSS: 4507 (82.3)
20.9  in NSS; 26.5 (6.0) in PPDS
AA (NSS): ANKRD55
In the NSS sample, both rs159572 (ANKRD55) in AAs and rs11085374 (ZNF626) in EAs reached genome-wide significance. These loci were not replicated in the PPDS sample, in other ancestries, or when combined
Method: CIDI screening scales and PCL
PPDS: 947 (92.5)
PPDS: 4969 (94.7)
SNP-based heritability from GCTA was not significant
There were no significant genetic correlations between PTSD and other disorders; however there was pleiotropy between PTSD and rheumatoid arthritis/psoriasis
Logue and colleagues , in the first GWAS of PTSD in veterans, used a relatively small sample of trauma-exposed veterans and their partners and found that rs8042149, a SNP in the retinoid-related orphan receptor alpha (RORA) gene, was significantly associated with PTSD. Other SNPs in RORA were found to be nominally significant in replication samples tested within this paper. Support for the association between rs8042149 and PTSD has also been shown in other samples, such as an epidemiologic cohort with hurricane exposure . However, other studies have failed to replicate the association . Nievergelt and colleagues also used a veteran sample, the Marine Resiliency Study, for their primary analysis . With all ancestry groups combined, there was one locus that reached genome-wide significance, phosphoribosyl transferase domain containing 1 gene (PRTFDC1; rs6482463), and was replicated in an additional military sample. There were also 25 loci with suggested associations. The authors further examined the genetic architecture of PTSD by analyzing polygenic risk scores across bipolar disorder, MDD , and schizophrenia. PTSD was associated with bipolar risk scores only . Wolf and colleagues, in their analysis of veterans with PTSD and dissociative symptoms, did not find any loci that reached genome-wide significance for the dissociative PTSD phenotype, although ten loci were suggestive .
In a more recent GWAS of veterans, there were no significant associations with PTSD (even when combining samples across races to increase power), but several genes reached nominal significance. The top genes had biologically plausible mechanisms for potential involvement in PTSD (e.g., related to autism and other neurologic disorders) and may be involved in alternative splicing or immune pathways . Further, the newest genetic study of PTSD, published by Stein and colleagues , reports GWAS results from two army samples (the New Soldier Study [NSS ] and Pre/Post Deployment Study (PDDS), in addition to other advanced molecular genetic analyses. Results showed that rs159572 (in ANKRD55) was significant for an African-American [AA] subset of the NSS and rs11085374 (in/near ZNF626) was significant for the European American [EA] subset of the NSS. However, neither of these loci were significant in the PDDS sample or in further analyses. SNP-based heritability and cross-disorder correlations were also examined but not significant. However, there was evidence for pleiotropy with PTSD and rheumatoid arthritis and psoriasis, providing support for the role of the immune system in PTSD.
There are two nonveteran GWAS of PTSD. Xie et al.  found that rs406001 (on chromosome 7p12) reached genome-wide significance in a EA sample, with two other SNPs in the intron of the Tolloid-like 1 gene (TLL1) also of interest (although they did not reach genome-wide significance). The two TLL1 SNPs replicated in independent samples (EA and combined EA/AA, although not in AA only). This study used a population sample instead of a veteran sample and did not initially restrict analyses to only trauma-exposed individuals (although results were similar when this restriction was placed). The other nonveteran GWAS focused on only AA women . Guffanti and colleagues found that rs10170218, a SNP located in lincRNA AC068718.1, was significantly associated with PTSD in this population and partially replicated in another. Further, the authors also conducted pathway analyses and found that genetic variation relating to telomeres and immune function may be particularly relevant to PTSD . This aligns with other analyses of gene networks within PTSD, which implicate the immune system . Recently, a mitochondrial GWAS identified two mitochondrial variants that may be associated with PTSD risk , representing a novel approach to identifying PTSD-related genes.
Given that independent GWAS have not converged on the same SNPs and that there are so few studies to date, the field is clearly in need of more advanced genome-wide studies of PTSD with sufficient sample sizes to identify genetic loci. The Psychiatric Genetics Consortium for PTSD (PGC-PTSD) is a large collaboration created with the goal of advancing PTSD genetic research . The PGC model has been successful for other psychiatric phenotypes, such as schizophrenia . Top PTSD investigators across the world have contributed data to the PGC-PTSD, with the total sample size expected to reach approximately 50,000 individuals, half of who will be PTSD cases. While the large sample size will be an advantage in the search for genetic loci, there are also unique challenges inherent to the PTSD phenotype and the samples available. There is more racial diversity in the PGC-PTSD, with many samples from AA individuals, when compared to other PGC data, which is primarily EA. Although the genetic diversity is a strength of the PGC-PTSD, it also affords analytic challenges, including limits on statistical power, as racial/ethnic subgroups need to be analyzed separately due to potential population stratification issues. Further, trauma exposure is a requirement for the development of PTSD and thus must be considered. Despite these challenges, the PGC-PTSD represents the largest collaborative effort to date and has the potential to greatly inform our knowledge of the genetic architecture of this complex psychiatric phenotype.
Epigenetic modifications (e.g., DNA methylation changes) are of special interest to studying PTSD due to the “environment” inherent in the disorder (i.e., the traumatic event). Thus, epigenetic and gene expression approaches have become more frequent in the study of PTSD. Often the genes of interest are candidate genes, such as FKBP5 . Similar to the candidate gene literature, the literature has expanded rapidly, and there are too many methylation and expression studies to provide a thorough review, and the interested reader should refer to recent reviews for a comprehensive summary of the role of epigenetics in PTSD [26, 35, 72–74]. Notably, the PGC-PTSD also has a workgroup focused on epigenetics, where studies including genome-wide methylation data are being processed through an analytic pipeline to allow for meta-analysis.
Genetics of Sleep Phenotypes
A shared challenge for all genetic studies, behavioral or molecular, is definition of the phenotype. Sleep, and sleep difficulties, is no exception. Within the genetics of problematic sleep literature, phenotypes studied include nightmares, sleep onset latency (SOL), sleep length, sleep quality, subjective trouble falling asleep, subjective report of difficulty staying asleep, night waking, etc. Most genetic studies of insomnia, particularly those that are genome wide, have focused on insomnia symptoms, as opposed to clinical diagnoses . Another challenge in this literature is that sleep is affected by a large number of variables (e.g., medications, caffeine intake) that may provide noise when trying to examine its genetic basis. In the following section, a brief review of the behavioral and molecular genetics of relevant sleep phenotypes is provided, and the interested reader is referred to several recent reviews of the genetics of insomnia and/or other sleep disorders [75–78], as well as circadian rhythms .
There is a large twin literature for insomnia and other sleep phenotypes, with studies ranging across the lifespan from young child to adult samples (see [21, 77] for review). The estimated heritability of sleep phenotypes varies widely, with estimates of insomnia in adults ranging from 0.20 (overall insomnia factor; ) to 0.64 (insomnia; ). More recently, the longitudinal stability of insomnia heritability in adults has been demonstrated, with findings suggesting that the majority of the genetic influence on insomnia symptoms comes from latent genetic effects as opposed to time-specific sources . There are several twin studies of high relevance to this chapter. First, McCarren and colleagues  conducted the only twin study of sleep in veterans, utilizing data on four sleep-related phenotypes and a composite score from 2825 male twin pairs in the VET Registry. Heritability estimates were 0.28 (trouble falling asleep a 2 = 0.28), 0.26 (waking up several times per night a 2 = 0.26), 0.42 (trouble staying asleep), 0.21 (feeling tired after usual amount of sleep a 2 = 0.21), and 0.28 (composite ordinal score). Combined analyses also incorporated combat exposure severity, in addition to additive genetic effects, but this did not alter the genetic contributions to sleep variables. Combat exposure was weakly related to all sleep variables; however, PTSD was not examined in this study and requires future investigation.
Second, there is a subset of the literature that explores genetic overlap between sleep and common psychopathology. There is some evidence for overlap between sleep and internalizing disorders (depression, anxiety), with many studies using child and adolescent samples [84–87]. Estimates of genetic overlap with internalizing psychopathology range from partial (genetic correlations less than 1; ) to complete (i.e., the genetic contribution overlapped completely; ). None of these studies included externalizing disorders, and the literature on this overlap is limited. However, results from our group, in the first study to examine overlap between sleep and diagnoses of both types of psychopathology in an adult sample, show substantial (greater than 50%) genetic overlap between insomnia and depression, complete genetic overlap between insomnia and anxiety, and partial genetic overlap between insomnia and externalizing disorders (alcohol abuse and dependence, antisocial personality disorder) . Twin studies show that the genetic influences on PTSD, like those on insomnia, overlap significantly with internalizing disorders, with some research showing complete genetic overlap between MDD and PTSD [89–92]. Additionally, PTSD shows modest genetic overlap with externalizing psychopathology . Given this, it is reasonable to expect that there is significant genetic overlap between sleep and PTSD, although no studies to date have specifically examined this relationship.
Finally, as nightmares are a common symptom of PTSD, it is important to discuss their heritability. However, note that twin studies of nightmares do not examine them in the context of PTSD or in trauma-exposed samples. In a sample of Finnish twins, Hublin and colleagues  examined the heritability of childhood and adult nightmares (retrospectively reported). Results suggested a moderate heritable component for childhood nightmares (a 2 ~ 0.35–0.52) in both sexes, and in adulthood a sex difference was found in that the heritability estimate was higher in women than men (combined sexes: a 2 ~ 0.27–45). Another study of children and adolescents estimates nightmare heritability around 50% and shows that it does not overlap with anxiety symptoms .
Molecular Genetic Studies
Numerous thorough reviews have been dedicated to the molecular genetics of sleep and circadian rhythms [75–79]. Circadian rhythms govern fundamental physiological functions in almost all organisms, from prokaryotes to humans. The circadian clocks are intrinsic time-tracking systems with which organisms can anticipate environmental changes and adapt to the appropriate time of day. In mammals, circadian rhythms are generated in pacemaker neurons within the suprachiasmatic nuclei (SCN) and are entrained by light. Disruption of the circadian rhythms can cause depression, insomnia, jet lag, coronary heart disease, and a variety of neurodegenerative disorders (e.g., ). Interestingly, peripheral tissues have also been shown to contain independent clocks, the function of which is orchestrated by the SCN . The circadian clocks operate through transcriptional feedback autoregulatory loops that involve the products of circadian clock genes, and following, these genes have been the focus of molecular studies.
The candidate gene literature for sleep phenotypes has focused on fewer genes than that of PTSD; however, there are several candidate genes that have been examined across both phenotypes, representing possible genetic links. Additionally, there are some genes that, despite not being specifically examined in the context of both disorders, have biologically plausible connections to both sleep and PTSD. Candidate genes examined in association with insomnia include circadian genes (e.g., PER3, CLOCK, TIMELESS) and the serotonin transporter (5-HTTLPR), among others, with some success, although few replications [75, 77]. In the following section, we will review the candidate genes with potential relevance to both disorders, and then we discuss the GWAS studies of sleep phenotypes.
In discussing sleep candidate gene research, the first set of genes to consider are the circadian clock genes, given their role in establishing the body’s circadian rhythm. There are mixed results for the CLOCK variant T3111C and sleep disturbances, often studied in the context of mental disorders such as depression. Some results indicate that this polymorphism is associated with decreased sleep, while others do not demonstrate any associations with sleep in depressed individuals [97, 98]. There is also some evidence that the CLOCK gene may be involved in sleep following antidepressant treatment . Although there have not been candidate gene studies explicitly looking at CLOCK in PTSD, the circadian system does influence components of the stress response, particularly glucocorticoid levels, which have their own circadian rhythms (e.g., cortisol typically peaks in the morning) . Landgraf and colleagues , in a review of the role of the circadian system in psychopathology, hypothesize that some of the genetic contributions to PTSD vulnerability may result from alterations in CLOCK genes, which disrupt glucocorticoid signaling and in turn affect specific brain regions and alter the stress response. Other gene s involved with glucocorticoid signaling, such as FKBP5, may also be involved through this mechanism . Further, both ADCYAP1R1 and RORA, two other genes examined more thoroughly in the PTSD literature, are related mechanistically to the circadian clock . ADCYAP1R1 is released by the suprachiasmatic nucleus, which entrains the body to light , while RORA is released on a circadian rhythm, like glucocorticoids, and also modulates levels of BMAL, another important circadian gene .
Widely studied across many phenotypes within psychiatric genetics, the serotonin transporter gene, 5-HTTLPR, has also been implicated in sleep. With regard to sleep problems, carriers of the S allele who were treated with fluoxetine for depression had a higher likelihood of developing insomnia during treatment compared to L allele homozygotes . In a gene-environment interaction approach in a study of caregivers of patients with dementia and non-caregiver controls, Brummett and colleagues  found no main effect of 5-HTTLPR genotype on sleep quality but found that caregivers who were S allele carriers were more likely than their L allele caregiver counterparts to report poor sleep. Further, there is evidence that sleep quality and 5-HTTLPR genotype interact to affect temperament in children, which supports an early role of sleep in the development of emotions . There are also several studies examining sleep, depression, and serotonin. Less activity in a MAO-A (an enzyme that degrades serotonin) VNTR has been associated with sleep quality, as well as depression . The 5-HTTLPR genotype has also been shown to influence sleep latency in the elderly, including in the context of depression symptoms . In another study of college students, the triallelic S’S’ genotype was more common in individuals with both shorter sleep and more depressed mood . The serotonin transporter gene may be related to PTSD under high trauma conditions , so there is some evidence for its involvement in both sleep and PTSD phenotypes.
Serotonin is also related to BDNF (particularly in psychiatric disorders; ), another gene with evidence to suggest that it may be involved in both the development of PTSD and disturbed sleep. As discussed earlier, various studies have examined the Val88Met polymorphism in PTSD, with some showing significance . The BDNF gene may also play a role in disturbed sleep: several studies have demonstrated that individuals with insomnia or fatigue have lower serum BDNF levels, when compared to controls, and that this may even differ by insomnia severity (e.g., ). Further, the Val88Met polymorphism itself has been shown to regulate sleep intensity and EEG patterns [112, 113]. A recent review of the BDNF and sleep summarizes the literature by hypothesizing that loss of sleep may cause a decrease in BDNF levels, which in turn alters the ability to respond to stressors, eventually leading to the development of stress-related psychopathology .
Another common neurotransmitter examined in the PTSD literature is dopamine. PTSD candidate gene studies suggest that the 9′ allele of the DAT1 polymorphism is related to increased likelihood of PTSD [41, 42, 115], and one study in particular reported that the 9′ allele carriers were at risk primarily driven by the relation to DSM-5 Criterion E symptoms , which includes a sleep symptom. There is also support for dopamine receptor genes in PTSD (e.g., DRD2; ). Although prior studies of dopamine genes and sleep have mostly utilized animal sleep models (e.g., [117, 118]), current research is highlighting the emerging role of dopamine genes in the context of human sleep phenotypes. DAT1 polymorphisms were associated with higher sleepiness in a young adult sample , and dopamine transporter polymorphisms in humans have been linked to the response of the reward system following sleep deprivation . Further, a large, collaborative study of the genetics of sleep duration, which examined 50,000 SNPs from 2000 candidate genes, found evidence for the involvement of the dopamine D2 receptor gene (DRD2) in sleep duration (see  for a more thorough review of the results and replication steps).
Other neurotransmitter systems implicated in the genetics of insomnia include gamma-aminobutyric acid (GABA) and orexin/hypocretin . GABRA variants have been shown to interact with lifetime trauma history to predict PTSD , and human studies have shown decreased GABA-A receptor binding in certain brain regions of individuals with PTSD . Further, low levels of both serum and cerebrospinal fluid orexin have been shown to be related to combat-related PTSD severity . The comparisons outlined here, although speculative, suggest that based on candidate gene studies, a wide range of neurotransmitter systems may play a role in the genetic overlap between sleep and PTSD and thus warrant future empirical study. A summary can be found in Table 9.2. However, it is important to note that this list is not exhaustive and that many findings have not been replicated.
Hypothesized candidate genes related to both sleep and PTSD
Gene or neurotransmitter system
Studied as a candidate for PTSD?
Studied as a candidate for sleep?
CLOCK genes may disrupt glucocorticoid signaling and alter the stress response 
Many studies, mixed results; see  for review
May be related through involvement in the circadian regulation of glucocorticoid receptors 
Considered a clock gene; affects SCN, phase reset 
Considered a clock gene; regulates BMAL (circadian) and expressed in a circadian fashion 
Meta-analysis shows that 5-HTLLPR (SS genotype) may be associated with PTSD in highly TE individuals 
Val88Met meta-analyzed in PTSD may be associated with PTSD when using trauma-exposed controls 
Sleep meds (benzodiazepines) act on GABA, patient with mutation in GABA-A receptor 
Low serum/CSF orexin related to combat PTSD severity 
Insomnia phenotype in zebrafish with overexpression 
Genome-Wide Association Studies
Similar to PTSD, genome-wide studies of insomnia and sleep-related phenotypes such as sleep quality and sleep duration do exist, although the sleep literature lags behind other psychiatric phenotypes. While there have been eight sleep GWAS to date (excluding sleep disorders other than insomnia, caffeine, and chronotype), there are only two that examine insomnia-related outcomes [125, 126] and one that used actigraphy data  and will be discussed in detail here (see Table 9.3). The remaining five focus on sleep duration [128–132], which may be less relevant to PTSD phenotypes: the sleep and PTSD literature indicates that sleep quality is the most relevant sleep outcomes for veterans . Ban and colleagues  conducted the first GWAS of an insomnia phenotype using a large Korean sample. There were no loci that passed genome-wide significance, but the most significant SNPs included rs11208305 (ROR1, previously implicated in bipolar disorder) and rs718712 (PLCB1, shown to have associations with schizophrenia). SNPs in CACNA1A, GNAS, NOS3, and ADCY8 were also of interest. Later, Byrne and colleagues  conducted a GWAS examining multiple sleep phenotypes (sleep latency, sleep quality, sleep depth, sleep time, sleep duration, insomnia factor score) in an Australian sample. There were no genome-wide significant loci for the insomnia factor score (or other sleep phenotypes), but rs11174478 (in SLC2A13) had the most significant association with the insomnia factor score. The most interesting results were for CACNAIC (a gene previously implicated in bipolar disorder), which had some evidence for an association with both sleep quality and sleep latency in this sample but was not significant in their replication sample. There were no significant results from pathway analysis. However, note that the CACNA1C and sleep quality finding was later replicated by Parsons and colleagues in a British sample . A recent sleep GWAS used a set of objective sleep phenotypes from actigraphy data (as opposed to self-report) . Results, although not corrected for analysis of multiple phenotypes, revealed several genes potentially implicated in regulating sleep efficiency on weekdays (ULF1, which is a circadian gene) and sleep latency (DMRT1). This GWAS is novel in its approach to sleep phenotypes and represents a useful next direction for phenotypes used in genome-wide studies of sleep.
Genome-wide association studies of insomnia-related phenotypes
Mean (SD) age
Summary of key findings
Ban et al. (2011) 
Korean epidemiological study
8179 (7280 controls, 1429 cases)
Self-report; categories of overall, onset, middle, and late insomnia
A total of 3354 SNPs had p < 0.005
Most significantly associated with insomnia
PCLB1 (previously associated with schizophrenia)
ROR1 (previously associated with bipolar)
Byrne et al. (2013) 
Self-report; insomnia factor score, duration, depth quality, latency, sleep time
No significant SNPs within GWAS for insomnia factor score or any other phenotypes
Top sleep latency SNPs found in third intron of CACNA1C (previously associated with bipolar disorder and schizophrenia); did not replicate in Chronogen Consortium sample
Other top SNPs for sleep latency: rs7316184, rs7304986, rs7301906, rs16929275, rs16929276, rs16929278, and rs2051990
Top SNP for sleep quality: rs2302729
Spada et al. (2016) 
LIFE adult study, epidemiologic
14 actigraphy parameters (but only reporting on sleep, not daytime results here)
UFL1 – sleep efficiency on weekdays (significant; circadian rhythm gene)
DMRT1 – sleep latency (significant)
SMYD1 – sleep offset (significant)
CSNK2A1 – sleep latency (nominally significant)
ZMYM4 – sleep latency (nominally significant)
However, correction was not done for the analysis of multiple phenotypes
Overall, the insomnia genetic literature suffers from inconsistent phenotypes and small sample sizes [75, 78], which will need to be addressed in future studies. There are few GWAS of insomnia, even compared to PTSD. Results show no genome-wide significant hits for this insomnia or related phenotypes like sleep quality (only GWAS of sleep duration have generated hits). This is in contrast to PTSD, where multiple significant loci have been documented (although not consistently across studies). Thus, it is unsurprising that there have been no common genes identified through GWAS .
Epigenetic modifications are important for the regulation and function of genes related to sleep, as the circadian system is constantly interacting with and receiving feedback from the environment. DNA methylation, histone acetylation and deacetylation, and RNA modification, among other epigenetic process, have all been described in relation to circadian gene expression [135, 136]. There is a small literature on epigenetic changes in disturbed sleep and related disorders . Further, epigenetic regulation of clock genes may influence their role in psychiatric disorders . The reader is directed to several current reviews for more details about sleep and circadian epigenetics [135, 136].
Although the candidate gene literature for PTSD has grown exponentially in recent years, and GWAS of both sleep and PTSD have emerged, there still remains much to learn about the genetic architecture of these traits individually, and even more with regard to their overlap, in trauma-exposed populations. There are many limitations to the candidate gene approach, with inconsistencies in genetic associations across both phenotypes discussed, likely due to differences in phenotypes, ascertainment, and other methodology. The extant GWAS, although successful in identifying some polymorphisms (particularly for PTSD, but less so for sleep), still suffer from small sample sizes and lack of replication across other samples and racial/ethnic populations. Additionally, GWAS investigations as a whole for psychiatric genetics have yet to account for the total variance demonstrated in twin studies. Collaborative approaches, such as the PGC-PTSD, will be essential to collect large enough sample sizes to identify additional loci via GWAS studies and begin to address challenges such as incorporating ancestry appropriately and tackling heterogeneity across phenotypes (which is particularly important for genetic studies of insomnia ). The post-GWAS era work will also be critical to taking the knowledge from these studies and understanding the downstream molecular effects of genes of interest from GWAS designs.