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Geneticist krystal tsosie advocates for indigenous data sovereignty, more stories in genetics.

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How ancient herders rewrote northern Europeans’ genetic story

New DNA analyses show the extent of the Yamnaya people’s genetic reach starting 5,000 years ago and how it made descendants prone to diseases like MS.

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research articles about genes

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Genetics 78 articles archived since 1845

This genetically engineered petunia glows in the dark and could be yours for $29.

The engineered “firefly petunia” emits a continuous green glow thanks to genes from a light-up mushroom

research articles about genes

Why Do Dogs Wag Their Tail?

Is your dog’s tail-wagging a side effect of domestication, or did humans select for it?

research articles about genes

Sperm Cell Powerhouses Contain Almost No DNA

Scientists discover why fathers usually don’t pass on their mitochondria’s genome

research articles about genes

Meet ReTro, the First Cloned Rhesus Monkey to Reach Adulthood

A method that provides cloned embryos with a healthy placenta has led to the first cloned rhesus monkey that has survived to adulthood and could pave the way for more research involving the primates...

research articles about genes

Ancient DNA Reveals Origins of Multiple Sclerosis in Europe

A huge cache of ancient genomes spanning tens of thousands of years reveals the roots of traits in modern Europeans

research articles about genes

How Supergenes Shape Evolution

By locking together traits that work well together, supergenes provide striking evolutionary advantages. But they can also be costly because they make it nearly impossible to purge bad mutations...

research articles about genes

The Genetics of Why You Look Like Your Great Aunt Mildred

Untangling the genetics that underlie facial features

research articles about genes

Kākāpō Parrots Are Flightless, Adorable and Making a Comeback

DNA sequencing, GPS tracking and tailored diets are slowly restoring New Zealand's endangered kākāpō

research articles about genes

Lost ‘Woolly Dog’ Genetics Highlight Indigenous Science

“Woolly dogs” that were kept by the Coast Salish peoples are now extinct, but researchers were able to see their importance written in the genome of the only known pelt

research articles about genes

These Male Stick Insects Aren’t ‘Errors’ After All

Some female stick insects can reproduce without males—but they have a secret

research articles about genes

U.K. Becomes First Country to Approve a CRISPR Disease Treatment

A newly approved CRISPR therapy could transform the treatment of sickle cell disease and beta-thalassemia—but the technology is expensive

research articles about genes

Mapping the ‘Unknome’ May Reveal Critical Genes Scientists Have Ignored

Geneticists don’t know what most human genes do. A new research tool may help

research articles about genes

Newborn Genomic Screening Needs to Build the Evidence

With many large newborn genomic screening studies launching, we need to answer questions about cost, fairness and tangible benefits of a promising technology

research articles about genes

How Hot Is ‘Pepper X’? Its Creator Spent 6 Hours Recovering from Eating It

 “Pepper X” is officially the hottest pepper in the world, weighing in with 2.693 million Scoville heat units. The creator reveals his process and experience tasting the pepper...

research articles about genes

We Finally Know Where Oranges and Lemons Come From

In addition to finding where citrus come from, researchers have pinpointed the genetic origins of the fruits’ tart taste

research articles about genes

The Complete Human Y Chromosome Marks an Opportunity to Move Away from Stigma

The Y chromosome was once used to label people as criminal. A new complete Y chromosome sequence just might combat this dangerous myth

research articles about genes

AI Tool Pinpoints Genetic Mutations That Cause Disease

Researchers have adapted the AI network to search for genetic changes linked to ill health

research articles about genes

Human Ancestors Nearly Went Extinct 900,000 Years Ago

A new technique for analyzing modern genetic data suggests that prehumans survived in a group of only 1,280 individuals

research articles about genes

Ötzi the Iceman Gets a New Look from Genetic Analysis

Improved DNA analysis has updated thinking on the skin color, ancestry, and more of the alpine mummy known as Ötzi the Iceman

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Genetic determinants of depression: Recent findings and future directions

Erin c. dunn.

1 Center for Human Genetic Research, Massachusetts General Hospital

2 Department of Psychiatry, Harvard Medical School

3 Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT

Ruth C. Brown

4 Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University

Jonathan Rosand

5 Department of Neurology, Massachusetts General Hospital

6 Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT

Nicole R. Nugent

7 Department of Psychiatry and Human Behavior, Alpert Brown Medical School

Ananda B. Amstadter

Jordan w. smoller.

8 Center on the Developing Child, Harvard University

Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States. One promising avenue for preventing depression and informing its clinical treatment lies in uncovering both the genetic and environmental determinants of the disorder as well as their interaction (i.e. gene-environment intervention; GxE). The overarching goal of this review paper is to translate recent findings from studies of genetic association and GxE related to depression, particularly for readers without in-depth knowledge of genetics or genetic methods. This review is organized into three major sections. In the first section, we summarize what is currently known about the genetic determinants of depression, focusing on findings from genome-wide association studies (GWAS). In the second section, we review findings from studies of GxE, which seek to simultaneously examine the role of genes and exposure to specific environments or experiences in the etiology of depression. In the third section, we describe the challenges to genetic discovery in depression and promising strategies for making progress.

Introduction

Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States, with lifetime prevalence estimates of 11.7% among adolescents 1 and 16.6% among adults. 2 It is projected to be the leading cause of disease burden worldwide by 2030. 3 Although the impact of depression can be minimized or prevented through early detection, treatment, and ongoing care, numerous individual and structural barriers, including stigma, lack of health insurance, and other barriers to accessing mental health services, prevent many from seeking help. Indeed, only slightly more than half of all people who experience depression seek treatment and those who do tend to dropout prematurely or receive poor quality care. 4 , 5 Existing treatments for depression are also modestly effective; only about one-fifth of adults receiving cognitive behavioral therapy or psychodynamic therapy alone 6 and one-third of adults receiving antidepressant medication alone 7 , 8 will experience remission after an initial course of treatment. In children and adolescents, the efficacy of existing treatments is also limited. 9 - 11 Moreover, nearly three-quarters of people with depression will experience a relapse at some point in their life. 12 These findings underscore the urgent need to prioritize prevention, alongside treatment.

A deeper understanding of the etiology of depression, including both its genetic and environmental determinants, as well as their interplay (e.g., gene-environment interaction; GxE) will have implications for preventing depression and informing its clinical treatment. There are now numerous established environmental risk factors for depression, including poverty, 13 , 14 negative family relationships and parental divorce, 15 , 16 child maltreatment, 17 , 18 and other stressful life events more generally. 19 , 20 While the risk of depression is elevated in the immediate aftermath of experiencing these environmental adversities, the effects of adversity can persist over the lifecourse. 21 , 22

There is also now a robust literature implicating genetic factors in the etiology of depression and other psychiatric disorders. Depression is known to run in families; people with major depressive disorder are three times more likely than those without the disorder to have a first degree relative who also has depression. 23 Twin studies, which allow for simultaneous quantification of genetic and environmental influences, suggest that depression is moderately heritable. Specifically, twin studies have estimated that approximately 40% of the variation in the population risk of depression is attributable to genetic variation. 24

In recent years, the combination of advances in our understanding of human genomic variation (e.g., Human Genome Project; HapMap Project; 1,000 Genomes Project) and cost-effective genotyping techniques haveled to extraordinary growth in molecular genetic studies of depression and other “complex” psychiatric phenotypes. These studies typically examine whether specific alleles (e.g., alternative forms of DNA sequence at a specific locus) or genotypes (e.g., the combination of alleles at a given locus) are associated with the phenotype of interest. Until recently, genetic studies of depression focused largely on candidate genes, or genes hypothesized to be implicated in the neurobiology of depression. Some of the most commonly studied candidate genes have been those regulating serotonin (5-HT) and dopamine (DA) neurotransmission, given the suspected involvement of these neurotransmitters in the pathophysiology of depression and the fact that these are targets of antidepressant drugs. 25 - 27 Unfortunately, most candidate gene studies have been underpowered and replication of findings has been rare. More recently, the availability of DNA microarrays have enabled genomewide association studies (GWAS) that do not rely on prior hypotheses. The GWAS approach allows for the analysis of a million or more variants across the entire genome. The ultimate goal of these genetic association studies is to improve diagnosis, prevention, and treatment through a nuanced understanding of the genetic underpinnings of the disease.

In this paper, we review recent findings from studies of genetic association and GxE related to depression and outline areas for future research. Several excellent reviews of this literature aimed at the genetic research community have already been published (see for example 28 , 29 - 33 ). We aimed to provide a review for a broad audience of readers who may be unfamiliar with genetic concepts and methods. We organized this review into three major sections. In the first section, we describe recent findings based on GWAS of depression. We begin with GWAS, rather than older methods (i.e., linkage and candidate gene association studies), as the latter have already been extensively covered by prior reviews. We also do not review studies on genetic markers of antidepressant treatment response, or pharmacogenomics, 34 as our focus was on the genetic determinants of illness risk. In the second section, we review findings from GxE studies, which aim to simultaneously examine the role of genetic variants and environmental exposures in the etiology of depression. As described below, GxE studies have the potential to help identify genetic variants associated with risk or resilience against depression that are only revealed in specific subgroups of the population who have experienced a given environment. In the third section, we address the challenges that face genetic studies of depression and describe emerging strategies that may be useful for overcoming these challenges. We encourage readers who may be unfamiliar with basic genetic concepts to refer to the following resources 35 , 36 as well as the resources listed in Table 1 .

Findings from Genome-Wide Association Studies (GWAS)

GWAS have been one of the most widely used methods for identifying risk loci in the past decade. 37 - 40 In a typical GWAS, one million or more common variants known as single nucleotide polymorphisms (SNPs) are examined for their association to disease. “Common variants” are generally defined as those alleles that are carried by at least 5% of the population. GWAS are typically conducted using a case-control design in which allele frequencies are compared between cases with a disease to controls without the disease. Compared to candidate gene studies, GWAS provide a hypothesis free or “unbiased” approach to detecting susceptibility loci. However, to account for the large number of tests conducted, the threshold for declaring genome-wide significance in a GWAS is a p-value of less than 5×10 -8 , equivalent to a p-value of 0.05 corrected for a million independent tests (p<0.00000005). 41 Because common variant effects are typically modest, large samples (on the order of 10,000 or more cases and controls) are usually needed to have sufficient power to detect such effects at this statistical threshold.

According to the National Human Genome Research Institute (NHGRI) GWAS catalog, more than 2,000 GWAS have been published to date. 42 A total of 14 GWAS have been conducted for either major depressive disorder (MDD) or depressive symptoms. In addition, one GWAS focusing on age at onset of major depressive disorder was conducted. These 15 studies were identified by conducting a systematic search of PubMed for papers published before October 2013. We searched the PubMed database using the following MESH terms: (“Depression”[Mesh] OR “Depressive Disorder”[Mesh] OR “Depressive Disorder, Major”[Mesh] OR “Depressive Disorder, Treatment-Resistant”[Mesh]) AND “Genome-Wide Association Study”[Mesh]. We also searched for articles by examining the references pages of review articles, meta-analyses, and other empirical articles published since 2005. As shown in Table 2 , all of these studies were based on samples of European ancestry and represent a combination of population-and clinic-based samples.

BDI = Beck Depression Inventory; CESD = Center for Epidemiological Studies of Depression Scale; CIDI = Composite International Diagnostic Interview; DIGS = Diagnostic Interview for Genetic Studies; fMRI = functional magnetic resonance imaging; GAIN = Genetic Association Information Network; GCTA = genome-wide complex trait analysis; GenRED = Genetics of Recurrent Early-Onset Depression; GWS = genome wide significant; HADS = Hospital Anxiety and Depression Scale; HAM-D = Hamilton Depression Rating Scale; LD = linkage disequilibrium; POMS = Profile of Mood States; SCAN = Schedules for Clinical Assessment in Neuropsychiatry; SCI = Structured Clinical Interview; SNPs = single nucleotide polymorphism; STAR*D = Sequenced Treatments to Relieve Depression

The first GWAS of depression was published in 2009 and included 1,738 cases and 1,802 controls. Although no SNPs reached genome-wide significance, 11 of the top 200 SNPs were found in a 167 kilobase (kb) region overlapping the gene PCLO (piccolo presynaptic cytomatrix protein), which is involved in establishing active synaptic zones and synaptic vesicle tracking. 43 In several subsequent studies, 44 , 49 , 58 investigators found mixed evidence regarding the association of PCLO SNPs and MDD. In the first study to report a genome-wide significant association for depression, Kohli and colleagues 50 found support for a recessive effect of a SNP (rs1545843) in the gene SLC6A15 (solute carrier family 6, neutral amino acid transporter, member 15) that is involved in transporting neutral amino acids. They provided additional evidence in support of this association by demonstrating that risk alleles were correlated with reduced SLC6A15 expression in hippocampal tissue (taken from individuals undergoing surgery for epilepsy) and reduced hippocampal volume and neuronal integrity using neuroimaging. Mice susceptible to chronic stress were also found to have reduced hippocampal SLC6A15 expression. Of note, however, this locus has not emerged as a prominent finding in subsequent depression GWAS (described below).

One of the major lessons from these early GWAS of depression, as with other complex traits, 59 , 60 was that the effect of most SNPs was small in magnitude (allelic odd ratios of around 1.3 or less) and therefore considerably larger samples would be needed to identify genetic loci associated with depression. To enhance the power of psychiatric GWAS studies, the Psychiatric Genomics Consortium (PGC) was established in 2007 as an international collaborative effort to define the spectrum of risk variants across psychiatric disorders ( http://www.med.unc.edu/pgc ). One of the major goals of the PGC was to conduct mega-analyses for MDD in addition to schizophrenia, bipolar disorder, autism, and attention deficit hyperactivity disorder. 61 - 63 A mega-analysis pools individual-level phenotype and genotype data from across many studies; this approach differs from a meta-analysis, where the summary statistics produced by each study are analyzed. In 2012, the PGC published the results of a GWAS mega-analysis of MDD comprising 9,240 cases and 9,519 controls across 9 primary samples, all of European ancestry. 64 Although this was the largest sample to date, no SNP reached genome-wide significance. The most significant SNPs in the discovery sample were rs11579964 (p=1.0 × 10 -7 ), a variant closest to several genes ( CNIH4 , NVL , WDR26 ) and rs7647854 (p=6.5 × 10 -7 ) a variant closest to C3orf70 and EHHADH . However, these findings were not supported in a large independent replication sample.

GWAS of depressive symptoms have also been largely unrevealing. The first GWAS of depressive symptoms did not find any SNPs reaching genome-wide significance. 55 One modestly associated (p=1.59×10 -6 ) SNP (rs7582472) did show evidence of replication in two independent cohorts. However, this SNP was more than 300kb away from two genes and neither gene showed significant association to depression in a gene-based analysis. A second study of depressed mood also did not find any genome-wide significant SNP, but did find an intronic SNP (rs12912233) in RORA (retinoid related orphan receptor alpha gene) was modestly associated in the meta-analysis (p-6.3×10 -7 ). While interesting, because another RORA SNP has been linked through GWAS to post-traumatic stress disorder, 65 this result awaits replication. In the largest study, which was a meta-analysis comprising 17 population-based studies (n=34,549 individuals) as the discovery sample, no SNP reached genome-wide significance. 56 The strongest association was for rs8020095 (p=1.05 × 10 -7 ), located in the gene GPHN . When the discovery and replication samples were combined into one meta-analysis of 22 studies with 51,258 respondents, one region (indexed by the SNP rs40465) was associated with depressive symptoms at genome-wide levels of significance. 56 This variant is in a “gene desert,” an area of the genome where there are long regions without protein-coding sequences and unknown biological function.

Another major lesson from depression GWAS has been that popular candidate genes have generally not shown evidence of association. Prior to the GWAS era, meta-analyses of candidate gene studies concluded there was nominally significant evidence (at p<0.05) for six candidate genes in depression: APOE , DRD4 , GNB3 , MTHFR , SLC6A3 , and SLC6A4 . 66 , 67 However, none of these genes nor any of the more than 100 frequently examined candidate genes have shown evidence of significant association in the published GWAS of depression to date. Replication of candidate genes in GWAS is challenging, however, as several widely studied candidate gene markers, including the serotonin transporter 5-HTTLPR variable tandem number repeat (VNTR), are not directly captured by typical GWAS platform. Some groups have developed techniques to impute or derive best-guess estimates of these genetic markers using available SNP data, 68 , 69 though these efforts have not yet been widely adopted. However, the overwhelming evidence for many candidate genes has not been compelling.

Another interesting observation from GWAS has been the lack of consideration of the role of environment. As we describe below, we think GWAS may be limited by not examining how genetic influences on depression may vary among individuals with certain environmental exposures. One exception is a study by Powers and colleagues, 57 who used propensity score matching to conduct a GWAS among case-control pairs matched on exposure to recent stressful life events. Use of propensity score matching enabled them to reduce sample heterogeneity and compare cases to controls with a similar level of exposure, though they did not formally test for GxE. In their analysis, no SNPs however, were genome-wide significant or even suggestive (p<5×10 -6 ), though this was likely due to the very small sample size (n=805).

Findings from Gene-Environnent Interaction (GxE) Studies

The longstanding recognition that both genes (“nature”) and environments (“nurture”) contribute to the etiology of depression has motivated a great deal of interest in studying GxE. GxE studies examine the degree to which genetic variants modify the association between environmental factors and depression (or similarly, the extent to which environmental factors modify the association between genes and depression). 70 - 72 Typically, GxE studies have assumed a “diathesis-stress” model, where a genetic liability, also referred to as a diathesis, interacts with a stressful life event to give rise to depression. In this model, genes either exacerbate or buffer the effects of stress. 73 More recently, however, the concept of GxE has been expanded to incorporate more positive aspects of the environment, such as social support, psychosocial interventions, and other protective factors that reduce risk for disease. 74 , 75 Here, emerging work has focused on differential susceptibility to the environment, 76 , 77 or the extent to which genetic variation makes individuals more likely to respond adversely to negative environments, but more positively to salutary environments.

Research on GxE in depression was essentially launched with a publication in Science in 2003. In this study, Caspi and colleagues 78 used data from a 26-year longitudinal study in New Zealand to test whether a functional length polymorphism in the promoter region (5-HTTLPR) of the serotonin transporter gene ( SLC6A4 ) interacted with stressful life events to increase risk for depression. Results of the Caspi study suggested that individuals with at least one short (s) allele (i.e. who had the “s/s” or “s/l” genotype of the biallelic coded version) had more depression whether measured in terms of level of depressive symptoms, a depression diagnosis, or incident depression, as well as suicidality, in response to the number of stressful life events when compared to subjects who were not s allele carriers. They also found that s allele carriers had a greater probability of experiencing depression relative to those without an s allele as a result of exposure to probable or severe childhood maltreatment. The Caspi paper has become one of the most influential studies in the field, having been cited more than 5,000 times.

Since the publication of Caspi et al.'s seminal research, numerous replication attempts have been made. Most of these have also focused on 5-HTTLPR, though other genetic variants have also been studied, including variants in BDNF (brain derived neurotropic factor), MAOA (monoamine oxidase A), FKBP5 (FK506 binding protein 51), CRHR1 (corticotropin releasing hormone receptor 1), COMT (catechol- O -methyltransferase), and CREB1 (also known as CAMP or responsive element binding protein 1). Many replication attempts have focused on recent or childhood stressful life events, as well as child maltreatment, namely physical abuse, sexual abuse, or neglect. All of these are appropriate “candidate” environments to study in GxE research. Child maltreatment, for example, is one of the most potent environmental stressors in the etiology and course of depression and other types of psychopathology. Extant studies suggest that childhood maltreatment at least doubles the risk for internalizing problems, including depression. 18 , 20 , 21 , 79 , 80

The large number of empirical studies trying to replicate Caspi's GxE findings for depression have been summarized in several reviews focusing on GxE with 5-HTTLPR (see for example 72 , 81 , 82 - 88 ). These reviews ultimately fueled a heated debate regarding the plausibility of the Caspi findings. Including somewhat identical individual studies, review papers have drawn opposing conclusions about the support for GxE effects, with some studies finding consistent GxE effects and others failing to detect them. 84 , 85 Meta-analyses have provided a quantitative summary of these studies, but have also reached opposing conclusions. Specifically, the results of two meta-analyses, 82 , 86 which found evidence against a consistent GxE effect, differed from a third meta-analysis, 83 which concluded there was strong evidence to support the 5-HTTLPR GxE. These conflicting results may be explained by differences in the selection of studies for inclusion in the meta-analyses. 89 , 90 For example, the meta-analyses that used the most stringent inclusion criteria 82 , 86 failed to support the GxE association. 91 Some have also noted there is an inverse relationship between the power of the replication studies and support for the 5-HTTLPR association, precisely the opposite of what one would expect if the association is valid. 91 Moreover, the most direct replication attempt of the Caspi findings, which was not included in any prior meta-analysis, found no evidence in support of the GxE effect on depression. This was a longitudinal birth cohort study following a similar population (New Zealand residents), for a similar length of time (30 years), and using comparable phenotypic measures. 92 The authors observed no interaction between stressful life events and 5HTTLPR genotype, even after conducting 104 different regression models. 92

On the other hand, some have argued that support for the 5HTTLPR GxE has been more consistent when childhood maltreatment is the exposure variable 83 - 85 or when direct-interview assessments (as opposed to self-report questionnaires) have been used. 84 , 85 This finding is important, as there has been substantial variability in the characteristics of study populations, measurements of depression and environmental exposures, and analytic methods used across empirical studies to test for GxE in depression. 72 Some have also tried to place these individual GxE studies in the context of the broader literature examining genetic variability and stress sensitivity on depression. Here, some have appealed to the more consistent findings from animal studies showing that loss of function mutations in the serotonin gene have been associated with depressive-like behavior in rodents and that genetic variation in the serotonin transporter gene has been linked to depression among non-human primates. 93 Proponents have also noted that the results are more convincing when considered alongside experimental imaging studies showing 5-HTTLPR variation in amygdala activity, and treatment response studies showing 5-HTTLPR variation in antidepressant treatment response. 93 , 94 Overall, the validity of the influential 5-HTTLPR GxE finding remains unclear.

GxE studies focusing on other candidate genes, however, have found more consistent results. For example, studies examining FKBP5 and CHRH1 have shown that variants in these genes moderate the effect of exposure to child maltreatment, childhood adversities, or negative life events on adult depression. 95 - 98 These genes are interesting candidates because they regulate the stress response via the hypothalamic-pituitary-adrenal (HPA) axis. 99 Additional replications of these candidates would be helpful to further evaluate their role in shaping risk for depression. Evidence for other candidates, such as BDNF , has been more mixed. For instance, a recent review found stronger evidence to support interactions with the BDNF Val66Met polymorphism and stressful life events compared to childhood adversity. 100 As we later discuss, genome-wide approaches to GxE remain an important, but relatively unexplored area.

Current and Future Directions for Research

The limited success of GWAS for depression is in contrast with other psychiatric disorders, where established risk variants are accumulating through GWAS. For example, at the time of this writing, there are now more than 100 loci that have been associated with schizophrenia and bipolar disorder at stringent levels of statistical significance. 101 - 106 Despite the fact that individual risk loci have not been identified for depression, we know that such variants will be found given adequate sample sizes. For example, it is now possible to use genome-wide complex trait analysis (GCTA) to estimate the common variant contribution to depression using genome-wide SNP data (these estimates are sometimes referred to as SNP-heritability). 107 Through these methods, estimates of the common variant contribution to depression have ranged from a high of 32% 108 to a low of 21%. 109 It should be noted that these are lower bound estimates because SNP-chip heritability only reflects the effect of common variation that is captured on genotyping arrays.

Thus, the field faces two major questions: what explains the lack success of GWAS and GxE studies for depression and how can we best move forward? As described below (and summarized in Table 3 ), there are several likely explanations for the limited progress to date and several strategies that may help overcome these challenges.

Genome-Wide Association Studies = GWAS; Genome-Environment Wide Interaction Studies = GEWIS; National Institute of Mental Health Research Domain Criteria Initiative (RDoC)

Genetic Architecture and the Need for Larger Studies

The genetic architecture of depression is likely to be highly complex. Genetic architecture refers to the number of genetic loci associated with a phenotype, the effect size of each locus, and the manner in which these loci behave (e.g., whether they have additive or multiplicative effects). While all psychiatric disorders are thought to be polygenic, or influenced by multiple genes, the genetic basis of depression may reflect an even larger number of loci of individually small effect. Results from studies that have calculated polygenic risk scores (capturing aggregate effects of loci across the genome) support such a hypothesis. 64 , 110 Therefore, it is likely that much larger samples than those examined to date will be needed to detect these individually small effects. Simulations suggest that, to have comparable power to GWAS of schizophrenia or bipolar disorder, studies of depression will need to have sample sizes as much as five times larger. 52 Experience with GWAS for other disorders has established that, once a critical sample size threshold is crossed, larger and larger sample size yields more and more loci.

If depression is driven by many thousands of loci of weak effect, another strategy may be to combine genetic signals across many SNPs into functionally-defined gene sets or pathways. Pathway approaches can be considerably more powerful than single variant analyses, as the aggregation of weak signals from multiple causal variants may yield statistically significant evidence in support of a given gene or pathway. 111 , 112 Thus far, investigators have primarily examined pathways related to specific biological functions (e.g., axon guidance, cell functioning) as defined by human-curated bioinformatics resources, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) 113 or Gene Ontology. 114 Recent studies of candidate gene pathways have found evidence that genes involved in glutamatergic synaptic neurotransmission, 115 among others, 116 were significantly associated with depression. Evidence in support of gene sets or pathways also comes from several GWAS described previously and shown in Table 2 , which found significant support for some pathways. 46 , 56 One of the major drawbacks of gene-set analyses is that they require predefined sets of genes. Gene sets defined by current annotation databases, such as KEGG or GO, vary in their completeness; some pathways are more complete than others. Moreover, databases also vary in how they define gene sets. Thus, a given gene may belong to one pathway in one database and a second pathway in another. Although these challenges are not unsubstantial, we think greater use of pathway-type analyses is needed.

Understudied Components of the Genetic Architecture of Depression

A related consideration is that GWAS are designed to capture common but not rare genetic variation. Rare variants can include genetic single nucleotide variations (“SNVs” present in <1% of the population) and rare copy number variations (“CNVs,” that is, structural variations in DNA sequence that involve the duplication or deletion of thousands or more than a million base pairs). Such variants have now been shown to play a role in autism 117 , 118 and schizophrenia 119 , 120 and bipolar disorder, 121 but to date these components of the genetic architecture of depression have been largely unexplored.

Fortunately, advances in sequencing technology now provide an opportunity to address the role of rare SNVs. In recent years, the cost of direct DNA sequencing has dropped dramatically and technologic advances have facilitated the development of “high-throughput” sequencing. 122 , 123 To date, these “next generation sequencing technologies” have been largely applied to study variants in exons, which are the protein-coding regions of the genome collectively known as the “exome.” Exons comprise about 30 megabases of DNA or 1% of the total genome. Although no exome-sequencing studies of depression have been reported at the time of this writing, such studies are underway. Next generation sequencing technologies can also be applied to the entire genome (“whole genome sequencing”), enabling researchers to explore a full range of genetic variants in both coding as well as non-coding regions of the genome.

The major strength of sequencing is that it captures variants that have been previously uncharacterized by candidate gene and GWAS methods and thus may provide new insights into the genetic underpinnings of depression. Like all techniques, however, sequencing approaches face a number of challenges. For example, despite enormous reductions in the cost of sequencing, well-powered studies are still very expensive. Whole genome sequencing costs at least $1,000 US per genome, whereas exome sequencing costs several hundreds of dollars. Exome sequencing also assesses polymorphisms that by definition are rare and thus occur with much less frequently than common variants. To have sufficient statistical power to identify an association between these rare variants and depression, very large sample sizes, on the order of 10,000 or more cases, are needed. In addition, rare variant association methods are still largely under development.

Structural variation, including CNVs, are also a potential source of depression risk loci. CNVs can be inherited or spontaneous, also referred to as de novo . De novo CNVs—those that are present in offspring but not in either parent, have been shown to be important risk factors for several neuropsychiatric disorders, namely autism, 117 , 118 schizophrenia 119 , 120 and bipolar disorder. 121 After conducting a systematic literature search of PubMed for papers published by December 2013 using the MESH terms for depression described previously and the phrase “Copy Number Var*,” we identified four studies, which provide preliminary evidence implicating CNVs in depression. 124 - 127 In the largest of these studies, Glessner and colleagues found 12 CNV regions that were exclusive to cases with MDD. The region with the highest frequency in cases was a locus on chromosome 5 (5q35.1) that overlapped the genes SLIT3 , CCDC99 , and DOCK2 . The finding of a CNV overlapping the gene SLIT3 is interesting, as SLIT3 is known to play a role in axon development and neurodevelopmental disorders.

One of the major strengths of studying CNVs is that the methods for association testing are similar, by and large, to examining common variants. Simultaneous examination of SNPs and CNVs in large samples may identify whether CNVs play a significant role in depression and what their importance is relative to common variants. One of the major drawbacks of association testing with CNVs is that catalogs of these variants do not exist with the same level of number or specificity as they do for SNPs. For example, the location, size, and boundary of CNVs in these publicly available resources have been relatively imprecise. As a result, opportunities for misclassification of variants is much higher for CNVs than for SNPs. 128 Efforts are now underway to provide a more comprehensive catalog of CNVs (see for example: http://www.sanger.ac.uk/research/areas/humangenetics/cnv/ ). Moreover, until recently there has also not been a commercially-available genotyping array that could detect both SNPs and CNVs. With the advent of the “PsychChip,” a customized genotyping chip for psychiatric phenotypes, investigators will soon be able to simultaneously examine multiple genetic variants, including SNPs, CNVs, and rare variants. The importance of rare variants to depression risk remains to be seen, but large-scale studies will be needed to clarify their contribution.

Accounting for the Role of Gene-Environment Interaction

As noted previously, existing studies have not systematically addressed the possibility that a substantial proportion of the risk of depression is attributable to non-additive effects, including GxE. Moreover, GxE studies to date have focused on a limited set of candidate genes and have typically been underpowered, creating a risk of both false positive and false negative results. It is well established that environmental factors, including exposure to stressful life events and child maltreatment, are important risk factors for depression, but we still know little about whether these environmental effects are moderated by genetic variation and, if so, which genetic variants are relevant.

One approach to filling this gap may come from genome-environment wide interaction studies (GEWIS), pronounced “G-Whiz.” 129 , 130 In a GEWIS, investigators test for statistical interaction or GxE, with the “G” defined as the genetic loci (e.g., SNPs) included in a GWAS and the “E” defined as a known environmental exposure. Unlike candidate gene GxE, GEWIS offers the opportunity to conduct a genetically unbiased search—that is, one in which prior genetic or biologic hypotheses are not required. In one type of GEWIS, investigators could focus on loci for which a main effect of a genetic variant has been established by GWAS. In this scenario, loci identified by GWAS become candidates for GxE analysis, but with the advantage over traditional candidate gene studies that the locus is already known to influence the phenotype of interest.

To our knowledge, no GEWIS of depression has been published to date. Though research on GEWIS of depression and other psychiatric phenotypes is lacking, a small but emerging body of research on other complex phenotypes suggests GEWIS can yield important new gains. For example, studies have identified significant genome-wide GxE interactions in cancer, 131 , 132 diabetes 133 and insulin resistance, 134 Parkinson's disease 135 , pulmonary function 136 , and nonsyndromic cleft palate. 137 While interest in GEWIS is growing, there are several challenges to conducting this type of study. 129 The first is identifying the best methods to test for genome-wide GxE. Several methodological approaches have been developed (see for example reviews by 138 , 139 ); however there is no consensus on what methods are most ideal. Selection of a specific analytic method depends largely on whether the goal is to leverage GxE to discover novel loci or characterize the joint effect of genetic variants and environmental factors. 140

Second, the “environment” is to some extent unbounded in a way the genome is not. Both children and adults are exposed to a range of experiences across the multiple social and physical contexts in which they are embedded (e.g., families, school, neighborhoods, workplaces); all of these experiences and exposures can contribute to health. 141 Focusing on well-defined measures of environment where there has been robust and consistent evidence to support a relationship between the exposure and depression is one way to start. Such a list of measures could include in utero exposures (e.g., viruses, toxins, alcohol and drugs), social deprivation (e.g., poverty, child maltreatment), and enrichment (e.g., psychosocial interventions and treatments). However, even if we select the same environment, such as child maltreatment, there are still multiple different types of maltreatment, multiple ages to consider when the maltreatment occurred, and multiple ways to measure maltreatment (e.g., self report, administrative records, clinical interview).

Finally, and perhaps the biggest challenge, is the need to balance the trade-off between the need for large samples and identifying precise measures of environmental exposure. Large samples are needed to detect GxE (larger even than those needed in standard GWAS). However, large samples often lack the depth and breadth necessary to capture data on environmental or phenotype measures. Although smaller samples frequently have rich and repeated measures, they are underpowered to establish robust associations. Smaller samples can be combined to increase statistical power. However, challenges will arise in trying to harmonize measures of environment across these datasets. In other words, efforts to ensure adequate sample size for each unique combination of risk factors and GxE strata can lead to a “watered-down” environmental measure that lacks any meaningful variability; a classic example would be an instance where respondents are simply classified as “exposed” or “non-exposed.” Longitudinal birth cohort studies, which can include prospective measures of environmental exposures along with detailed phenotype data and genome-wide data, may be one promising avenue for conducting GEWIS in the future. Moreover, the growing interest in the concept of the “exposome,” environment-wide association studies (EWAS) and ways to systematically identify relevant environmental factors (see for example 142 , 143 ) could yield new insights to guide GEWIS in the future.

The Phenotypic Complexity of Depression

Another obstacle to progress in identifying susceptibility loci is the fact that depression is a heterogeneous phenotype. Indeed, it is possible to meet DSM-IV or DSM-5 diagnostic criteria for a major depressive episode through at least 227 different symptom combinations. 144 As currently described by DSM-5, MDD can manifest with or without: (1) anxious distress; (2) mixed features; (3) melancholic features; (4) atypical features; (5) mood-congruent psychotic features; (6) mood-incongruent psychotic features; (7) catatonia; (8) peripartum onset; and (9) a seasonal pattern. 145 These subtypes of major depressive disorder could reflect different genetic contributions. Consistent with such a hypothesis, studies suggest that depression with a history of child maltreatment has a different onset, course, and response to treatment when compared a depression that arises among individuals without a history of abuse. 146 , 147 Recent twin studies have also suggested that genetic liability to MDD reflects not one , but three distinct symptom dimensions (psychomotor/cognitive, mood, and neurovegetative symptoms). 148 Thus, GWAS that simply examine “depressed” cases versus controls may decrease the ratio of “signal to noise” by combining multiple disorder subtypes that vary in their genetic etiology. In light of evidence suggesting that there is no truly categorical threshold for depression caseness, 149 and that different lifetime prevalence estimates of depression are found when comparing cross-sectional retrospective reports to cumulative evaluations based on multiple interviews, 150 it is reasonable to posit that misclassification of individuals as cases or controls may be undermining the power of typical case-control GWAS.

We think there are several strategies to reduce the heterogeneity in depression. First, examination of the full range of variation in depression (e.g., depressive symptoms), rather than dichotomizing the phenotype (cases and controls), could be a statistically more powerful approach to identify variants associated with depression. 151 This would be consistent with evidence that the diagnostic threshold for MDD has been artificially imposed on a continuity of depression risk. 149 Second, more data-driven approaches to examine shared features or subtypes of depression through use of latent class analysis 152 may also prove helpful. Prior studies applying such methods in both adolescents and adults have found distinct subtypes that differ based on severity, symptoms, and episode length. 153 , 154 Examination of these subtypes in a genetic association study may help to identify variants that are common across or unique to specific subtypes. Third, another strategy would be to continue efforts to examine phenotypes thought to be more proximal to a genetic substrate than are clinically-defined categories. 155 Putative “intermediate” or “endophenotypes” related to depression include emotion-based attention biases, 156 , 157 impaired reward function, 158 and deficits in domains of executive functioning, such as learning and memory. 159 Investigation of endophenotypes is consistent with the National Institute on Mental Health (NIMH) Research Domain Criteria Initiative (RDoC;), 160 - 163 which aims to provide a bottom-up characterization of psychopathology incorporating genetics, neural circuitry, and behavioral phenotypes. Endophenotypes have not yet been the subject of large-scale studies that might fully evaluate their power relative. One exception is the ENIGMA consortium, through which GWAS meta-analyses of structural MRI phenotypes yielded a genome-wide significant association with hippocampal volume, 164 one of the best-established biomarkers of depression risk. However, this result still required sample sizes in the thousands, challenging the view that endophenotype-based studies will be more powerful than studies of major depressive disorder itself.

Conclusions

Research on the genetic underpinnings of depression is at an exciting, yet challenging crossroad. On the one hand, genotyping technologies have allowed for the characterization of individual and population-based genetic variation and have provided analytic tools to examine the individual and joint effects of genetic and environmental determinants. On the other hand, GWAS of depression have yet to see the same success achieved with other psychiatric or medical disorders. Moreover, studies of GxE have thus far not led to great clarity but have fueled plenty of debate. Some argue that positive findings reflect chance results among small, underpowered studies, 86 while others see consistencies when focusing on studies that are methodologically comparable. 83 - 85

We have also reviewed some of the potential explanations for the lack of success to date for GWAS and GxE studies of depression. Given the established heritability of depression, there is every reason to expect that increasingly well-powered studies will indeed identify risk loci. However, the genetic and phenotypic complexity of depression may mean that such successes will require samples on the order of tens of thousands of participants. Efforts to parse the heterogeneity of depression and validate phenotypic subtypes may also be essential to facilitate gene identification. Further, as we have noted, potentially important areas of the genetic basis of depression--specifically, rare variation and GxE--remain relatively unexplored on a large-scale. It remains to be seen how much of the “missing heritability” of depression will be revealed thorough studies of these components.

Although the path forward to detect genetic risk loci for depression remains challenging, what is certain is that a deeper understanding of the etiology of depression is needed. Existing treatments for depression are based on decades-old biology and genetic discoveries have already begun to identify promising targets for novel therapies in other disorders. Given the enormous burden of depression, identifying its genetic underpinnings may be essential to preventing the onset of this disorder and improving the lives of those who already suffer.

Acknowledgments

The authors thank Caitlin Clements, Patience Gallagher, Stephanie Kravitz, and Preetha Palasuberniam for their assistance in conducting the literature review for this paper. Dr. Dunn was supported in part by funding from the Center on the Developing Child at Harvard University. Dr. Smoller was funded in part by NIMH grant K24MH094614. Dr. Nugent was funded in part by NIMH grant K01MH087240. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

ScienceDaily

Copies of antibiotic resistance genes greatly elevated in humans and livestock

Biomedical engineers at Duke University have uncovered a key link between the spread of antibiotic resistance genes and the evolution of resistance to new drugs in certain pathogens.

The research shows bacteria exposed to higher levels of antibiotics often harbor multiple identical copies of protective antibiotic resistance genes. These duplicated resistance genes are often linked to "jumping genes" called transposons that can move from strain to strain. Not only does this provide a mechanism for resistance to spread, having multiple copies of a resistance gene can also provide a handle for evolution to generate resistance to new types of drugs.

The results appeared February 16 in the journal Nature Communications .

Earlier work by the Lingchong You lab has shown that 25% of bacterial pathogens are capable of spreading antibiotic resistance through horizontal gene transfer. They have also shown that the presence of antibiotics does not speed up the rate of horizontal gene transfer, so there must be something else happening that pushes the genes to spread.

"Bacteria are constantly evolving under many pressures, and elevated duplication of certain genes is like a fingerprint left at the crime scene that allows us to see what kinds of functions are evolving really rapidly," said Rohan Maddamsetti, a postdoctoral fellow working in the laboratory of Lingchong You, the James L. Meriam Distinguished Professor of Biomedical Engineering at Duke.

"We hypothesized that bacteria under attack from antibiotics would often have multiple copies of protective resistance genes, but until recently we didn't have the technology to find the smoking gun."

Traditional DNA-reading technology copies short snippets of genes and counts them up, making it hard to determine whether high counts of specific sequences are actually in the sample or if they are being artificially amplified by the reading process. In the past five years, however, complete genome sequencing with long-read technology has become more common, allowing researchers to spot high levels of genetic repetition.

In the study, Maddamsetti and coauthors counted the repetitions of resistance genes present in samples of bacterial pathogens taken from a variety of environments. They discovered that those living in places with higher levels of antibiotic use -- humans and livestock -- are enriched with multiple identical copies of antibiotic resistance genes, while such duplications are rare in bacteria living in wild plants, animals, soil and water.

"Most bacteria have some basic antibiotic resistance genes in them, but we rarely saw them being duplicated out in nature," You said. "By contrast, we saw lots of duplication happening in humans and livestock where we're likely hammering them with antibiotics."

The researchers also found that the levels of resistance duplication were even higher in samples taken from clinical datasets where patients are likely taking antibiotics. This is an important point, they say, because the increase in copying antibiotic resistance genes also increases the likelihood of bacteria evolving resistance to new types of treatments.

"Constantly creating copies of genes for resistance to penicillin, for example, may be the first step toward being able to break down a new kind of drug," Maddamsetti said. "It gives evolution more rolls of the dice to find a special mutation."

"Everyone recognizes there is a growing antibiotic resistance crisis, and the knee jerk reaction is to develop new antibiotics," You added. "But what we find time and again is that, if we can figure out how to use antibiotics more efficiently and effectively, we can potentially address this crisis much more effectively than simply developing new drugs."

"The majority of antibiotics used in the United States are not used on patients, they're used in agriculture," You added. "So this is an especially important message for the livestock industry, which is a major driver of why antibiotic resistance is always out there and becoming more serious."

This work was supported by the National Institutes of Health (R01AI125604, R01GM098642, R01EB031869).

  • Microbes and More
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  • Antibiotic resistance
  • Plant breeding
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Materials provided by Duke University . Note: Content may be edited for style and length.

Journal Reference :

  • Rohan Maddamsetti, Yi Yao, Teng Wang, Junheng Gao, Vincent T. Huang, Grayson S. Hamrick, Hye-In Son, Lingchong You. Duplicated antibiotic resistance genes reveal ongoing selection and horizontal gene transfer in bacteria . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-45638-9

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Research provides insight into constructing gene regulatory networks

by Higher Education Press

Gene regulatory network inference based on causal discovery integrating with graph neural network

Gene regulatory networks (GRNs) depict the regulatory mechanisms of genes within cellular systems as a network, offering vital insights for understanding cell processes and molecular interactions that determine cellular phenotypes. Transcriptional regulation, a prevalent type for regulating gene expression, involves the control of target genes (TGs) by transcription factors (TFs).

One of the major challenges in inferring GRNs is to establish causal relationships, rather than just correlation, among the various components of the system. Therefore, inferring gene regulatory networks from the perspective of causality is essential for understanding the underlying mechanisms that govern the dynamics of cellular systems.

Quantitative Biology has published an approach, titled "Gene Regulatory Network Inference based on Causal Discovery Integrating with Graph Neural Network," that leverages graph representation learning and causal asymmetric learning while taking into account both linear and non-linear regulatory relationships. GRINCD achieves superior performance in predicting the regulatory relationships of not only TF-TG but also TF-TF, where generalized correlation-based methods are unattainable.

GRINCD applies ensemble learning to predict the causal regulation of each regulator-target pair based on additive noise model (ANM) which takes high-quality representation for each gene generated by Graph Neural Network as input.

Specifically, GRINCD utilizes random walk and nodes' degree distribution to generate edge labels and feeds them to a two-layer GraphSAGE connected with a binary classifier for obtaining the representation of each node.

GRINCD achieves optimal performance on multiple datasets under various evaluation metrics. As an application, through analyzing the substantial alterations in regulatory relationships with disease progression , GRINCD identifies crucial potential regulators that drive the transition from colon inflammation to colon cancer.

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Study details the unprecedented scale, diversity, and power of the All of Us Research Program.

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Researchers have discovered more than 275 million previously unreported genetic variants, identified from data shared by nearly 250,000 participants of the National Institutes of Health’s All of Us Research Program. Half of the genomic data are from participants of non-European genetic ancestry. The unexplored cache of variants provides researchers new pathways to better understand the genetic influences on health and disease, especially in communities who have been left out of research in the past. The findings are detailed in Nature , along with three other articles in Nature journals.

Nearly 4 million of the newly identified variants are in areas that may be tied to disease risk. The genomic data detailed in the study are available to registered researchers in the Researcher Workbench , the program’s platform for data analysis.

“As a physician, I’ve seen the impact the lack of diversity in genomic research has had in deepening health disparities and limiting care for patients,” said Josh Denny, M.D., M.S., chief executive officer of the All of Us Research Program and an author of the study. “The All of Us dataset has already led researchers to findings that expand what we know about health – many that may not have been possible without our participants' contributions of DNA and other health information. Their participation is setting a course for a future where scientific discovery is more inclusive, with broader benefits for all.”

To date, more than 90% of participants in large genomics studies have been of European genetic ancestry. NIH Institute and Center directors noted in an accompanying commentary article in Nature Medicine that this has led to a narrow understanding of the biology of diseases, and impeded the development of new treatments and prevention strategies for all populations. They emphasize that many researchers are now utilizing the All of Us dataset to advance precision medicine for all.

For example, in a companion study published in Communications Biology , a research team led by Baylor College of Medicine, Houston, reviewed the frequency of genes and variants recommended by the American College of Medical Genetics and Genomics across different genetic ancestry groups in the All of Us dataset. These genes and variants mirror those in the program’s Hereditary Disease Risk research results offered to participants. The authors found significant variability in the frequency of variants associated with disease risk between different genetic ancestry groups and compared with other large genomic datasets.

While more research is needed before these findings can be used to tailor genetic testing recommendations for specific populations, researchers believe the difference in the number of these variants may be influenced by past studies’ limited diversity and their disease-focused approach to participant enrollment, rather than a difference in the prevalence of the variants.

In a separate study , investigators tapped the All of Us dataset to calibrate and implement 10 polygenic risk scores for common diseases across diverse genetic ancestry groups. These scores calculate an individual’s risk of disease by taking into account genetic and family history factors. Without accounting for diversity, polygenic risk scores could cause false results that misrepresent a person’s risk for disease and create inequitable genetic tools. Using the diversity of the All of Us data, these polygenic risk scores are applicable to a broader population.

“ All of Us values intentional community engagement to ensure that populations historically underrepresented in biomedical research can also benefit from future scientific discoveries,” said Karriem Watson, D.H.Sc., M.S., M.P.H., chief engagement officer of the All of Us Research Program. “This starts with building awareness and improving access to medical research so that everyone has the opportunity to participate.”

More than 750,000 people have enrolled in All of Us to date. Ultimately, the program plans to engage at least one million people who reflect the diversity of the United States and contribute data from DNA, electronic health records, wearable devices, surveys, and more over time. The program regularly expands and refreshes the dataset as more participants share information. 

To learn more about All of Us ’ scientific resources, visit researchallofus.org .

All of Us is a registered service mark of the U.S. Department of Health & Human Services (HHS).

About the All of Us Research Program: The mission of the All of Us Research Program is to accelerate health research and medical breakthroughs, enabling individualized prevention, treatment, and care for all of us. The program will partner with one million or more people across the United States to build the most diverse biomedical data resource of its kind, to help researchers gain better insights into the biological, environmental, and behavioral factors that influence health. For more information, visit www.ResearchAllofUs.org , https://www.joinallofus.org , and https://www.AllofUs.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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  • 19 February 2024

Ambitious survey of human diversity yields millions of undiscovered genetic variants

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The All of Us programme aims to recruit one million people from ethnic and socio-economic groups that are typically under-represented in biomedical studies. Credit: Barbara Alper/Getty

A massive US programme that aims to improve health care by focusing on the genomes and health profiles of historically underrepresented groups has begun to yield results.

Analyses of up to 245,000 genomes gathered by the All of Us programme, run by the US National Institutes of Health in Bethesda, Maryland, have uncovered more than 275 million new genetic markers, nearly 150 of which might contribute to type 2 diabetes. The work has also identified gaps in genetics research on non-white populations. The findings were published on 19 February in a package of papers in Nature 1 , 2 , Communications Biology 3 and Nature Medicine 4 .

They are a “nice distillation of the All of Us resource — what it is and what it can do”, says Michael Inouye, a computational genomicist at the University of Cambridge, UK. “This is going to be the go-to data set” for genetics researchers who want to know whether their findings are generalizable to a broad population or apply to only a limited one, he adds.

Bridging the gap

Researchers have long acknowledged the lack of diversity in the genomes available for them to study, says Jibril Hirbo, a geneticist at Vanderbilt University Medical Center in Nashville, Tennessee, who studies the genetics of health disparities. One study 5 that looked at data gathered up until January 2019 found that 78% of people in most large-scale genomic studies of disease were of European descent. This has exacerbated existing health disparities, particularly for non-white individuals, Hirbo says. When researchers choose genetic or molecular targets for new medicines or create models to predict who is at risk of developing a disease, they tend to make decisions on the basis of non-diverse data because that’s all that has been available.

research articles about genes

Facing up to injustice in genome science

The All of Us programme, which has received over US$3.1 billion to date and plans to assemble detailed health profiles for one million people in the United States by the end of 2026, aims to bridge that gap, says Andrea Ramirez, the programme’s chief data officer. It began enrolling people in 2018, and released its first tranche of data — about 100,000 whole genomes — in 2022. By April 2023, it had enrolled 413,000 anonymized participants, 46% of whom belong to a minority racial or ethnic group, and had shared nearly 250,000 genomes. By comparison, the world’s largest whole-genome data set , the UK Biobank, has so far released about half a million genomes, around 88% of which are from white people.

The All of Us data set is “a huge resource, particularly of African American, Hispanic and Latin American genomes, that’s massively missing from the vast majority of large-scale biobank resources and genomics consortia”, says Alicia Martin, a population geneticist at Massachusetts General Hospital in Boston.

In addition to the genomes, the database includes some participants’ survey responses, electronic health records and data from wearable devices, such as Fitbits, that report people’s activity, “making this one of the most powerful resources of genomic data”, Martin says.

An urgent need

A study in Nature on type 2 diabetes 2 is an example of the power of using a database that includes diverse genomes, Ramirez says. The condition, which affects about one in ten people in the United States, can be caused by many distinct biological mechanisms involving various genes. The researchers analysed genetic information from several databases, including All of Us, for a total of more than 2.5 million people; nearly 40% of the data came from individuals not of European ancestry. The team found 611 genetic markers that might drive the development and progression of the disease, 145 of which have never been reported before. These findings could be used to develop “genetically informed diabetes care”, the authors write.

research articles about genes

World’s biggest set of human genome sequences opens to scientists

In another of the studies 3 , researchers used All of Us data to examine pathogenic variants — that is, genetic differences that increase a person’s risk of developing a particular disease. They found that, among the genomes of people with European ancestry, 2.3% had a pathogenic variant. Among genomes from people with African ancestry, however, this fell to 1.6%.

Study co-author Eric Venner, a computational geneticist at Baylor College of Medicine in Houston, Texas, cautions that there should be no biological reason for the differences. He says that the disparity is probably the result of more research having been conducted on people of European ancestry; we simply know more about which mutations in this population lead to disease. In fact, the researchers found more variants of unknown risk in the genomes of people with non-European ancestry than in those with European ancestry, he adds. This underscores the urgent need to study non-European genomes in more detail, Venner says.

Updating models

Gathering and using more genomic and health data from diverse populations will be especially important for generating more accurate ‘polygenic risk scores’. These provide a picture of a person’s risk of developing a disease as a result of their genetics.

research articles about genes

US tailored-medicine project aims for ethnic balance

To calculate a score for a particular disease, researchers develop an algorithm that is trained on thousands of genomes from people who either do or don’t have the disease. A person’s own score can then be calculated by feeding their genetic data into the algorithm.

Previous research 6 has shown that the scores, which might soon be used in the clinic for personalized health care, tend to be less accurate for minority populations than for majority ones. In one of the current papers 4 , researchers used the more-inclusive All of Us data to improve the landscape: they calibrated and validated scores for 23 conditions and recommended 10 to be prioritized for use in the clinic, for conditions including coronary heart disease and diabetes. Martin applauds these efforts, but she hopes that future studies address how physicians and others in the clinic interpret these scores, and whether the scores can improve a person’s health in the long term because of the treatment decisions they elicit.

The All of Us programme plans to release a tranche of data every year, representing new enrolees and genomes, including one later in 2024, Ramirez says. It’s excellent that diverse data are coming in, Hirbo says, adding that he would like to see existing algorithms that were trained mainly on the genomes of people of European ancestry updated soon. “The models are still way behind,” he says.

doi: https://doi.org/10.1038/d41586-024-00502-0

The All of Us Research Program Genomics Investigators Nature https://doi.org/10.1038/s41586-023-06957-x (2024).

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Suzuki, K. et al. Nature https://doi.org/10.1038/s41586-024-07019-6 (2024).

Venner, E. et al. Commun. Biol . https://doi.org/10.1038/s42003-023-05708-y (2024).

Lennon, N. J. et al. Nature Med . https://doi.org/10.1038/s41591-024-02796-z (2024).

Sirugo, G., Williams, S. M. & Tishkoff, S. A. Cell 177 , 26–31 (2019).

Article   PubMed   Google Scholar  

Martin, A. R. et al. Nature Genet. 51 , 584–591 (2019).

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Scientists Find Genetic Signature of Down Syndrome in Ancient Bones

The discovery may help shed light on how prehistoric societies treated children with rare conditions.

Human bones spread out on a flat surface, in the configuration of how they would be in a human skeleton.

By Carl Zimmer

Scientists have diagnosed Down syndrome from DNA in the ancient bones of seven infants, one as old as 5,500 years. Their method, published in the journal Nature Communications, may help researchers learn more about how prehistoric societies treated people with Down syndrome and other rare conditions.

Down syndrome, which occurs in 1 in 700 babies today, is caused by an extra copy of chromosome 21. The extra chromosome makes extra proteins, which can cause a host of changes, including heart defects and learning disabilities.

Scientists have struggled to work out the history of the condition. Today, older mothers are most likely to have a child with the condition. In the past, however, women would have been more likely to die young, which might have made Down syndrome rarer, and the children born with it would have been less likely to survive without the heart surgery and other treatments that extend their lives today.

Archaeologists can identify some rare conditions, such as dwarfism, from bones alone. But Down syndrome — also known as trisomy 21 — is a remarkably variable condition.

People with it may have different combinations of symptoms, and they may have severe or milder forms. Those with the distinctive almond-shaped eyes caused by Down syndrome may have relatively ordinary skeletons, for example.

As a result, it’s hard for archaeologists to confidently diagnose ancient skeletons with Down syndrome. “You can’t say, ‘Oh, this change is there, so it’s trisomy 21,’” said Dr. Julia Gresky, an anthropologist at the German Archaeological Institute in Berlin who was not involved in the new study.

By contrast, it’s not tricky to identify Down syndrome genetically, at least in living people. In recent years, geneticists have been testing their methods on DNA preserved in ancient bones.

It’s been challenging, however, because the scientists can’t simply count full chromosomes, which fall apart after death into fragments.

In 2020, Lara Cassidy, a geneticist then at Trinity College Dublin, and her colleagues used ancient DNA for the first time to diagnose a baby with Down syndrome. They were examining genes from skeletons buried in a 5,500-year-old tomb in western Ireland. The bones of a 6-month-old boy contained unusually high amounts of DNA from chromosome 21.

Since then, Adam Rohrlach, a statistician then at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, and his colleagues have developed a new method to find the genetic signature, one that they can use to look quickly at thousands of bones.

The idea came to Dr. Rohrlach when he talked with a scientist at the institute about its procedures for searching for ancient DNA. Because high-quality DNA sequencing is very expensive, it turned out, the researchers were screening bones with a cheap test, called shotgun sequencing, before picking out a few for further investigation.

If the bone still preserved DNA, the test turned up many tiny genetic fragments. Very often, those came from microbes that grow in bones after death. But some bones also contained DNA that was recognizably human, and those with a high percentage were flagged for additional tests.

Dr. Rohrlach learned that the institute had screened almost 10,000 human bones in this way, and the results of all the shotgun sequencing were stored in a database. It occurred to Dr. Rohrlach and his colleagues that they could scan the database for extra chromosomes.

“We thought, ‘No one’s ever checked for these sorts of things,’” Dr. Rohrlach said.

He and his colleagues wrote a program that sorted fragments of the recovered DNA by chromosome. The program compared the DNA from each bone to the entire set of samples. It then pinpointed particular bones that had an unusual number of sequences coming from a particular chromosome.

Two days after their initial conversation, the computer had their results. “It turned out our hunch was right,” said Dr. Rohrlach, who is now an associate lecturer at the University of Adelaide in Australia.

They discovered that the institute’s collection included six bones with extra DNA from chromosome 21 — the signature of Down syndrome. Three belonged to babies as old as a year, and the other three to fetuses that died before birth.

Dr. Rohrlach also followed up on Dr. Cassidy’s 2020 study. He used his program to analyze the shotgun sequencing for the Irish skeleton and found that it also bore an extra chromosome 21, confirming her initial diagnosis.

In addition, Dr. Rohrlach found another skeleton with an extra copy of chromosome 18. That mutation causes a condition called Edwards syndrome, which usually leads to death before birth. The bones came from an unborn fetus that had died at 40 weeks and were severely deformed.

The new survey doesn’t let Dr. Rohrlach and his colleagues determine how common Down syndrome was in the past. Many children with the condition probably died before adulthood, and the fragile bones of children are less likely to be preserved.

“There’s so much uncertainty in the sampling, and in what we could and couldn’t find,” Dr. Rohrlach said. “I think it would be a very brave statistician who would try to make too much out of these numbers.”

But Dr. Rohrlach did find it significant that three children with Down syndrome and the one with Edwards syndrome were all buried in two neighboring cities in northern Spain between 2,800 and 2,400 years ago.

Normally, people in that culture were cremated after death, but these children were buried inside buildings, sometimes with jewelry. “It was special babies that were being buried in these homes, for reasons we just don’t understand yet,” Dr. Rohrlach speculated.

Dr. Gresky didn’t think the evidence made it possible to rule out chance instead for the cluster of cases.

“Maybe the bones there were so well preserved,” she said. “Maybe the archaeologists were so good and well-trained that they took all of them out. Maybe they were buried in a way that made it much easier to find them.”

Still, Dr. Gresky considered the new study an important advance. For one thing, it may allow archaeologists to compare remains genetically identified with Down syndrome and discover some hidden set of features common to all their skeletons.

And Dr. Gresky hoped that other researchers would use ancient DNA to illuminate the hidden histories of other rare conditions: “You just have to look for them, and you have to talk about them. Otherwise, they will stay invisible.”

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

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