14.6: Genetic Disorder and Pedigrees - Biology

14.6: Genetic Disorder and Pedigrees - Biology

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The following video provides a summary of all you have just learned about pedigrees, including differences in family inheritance patterns based on autosomal dominant, autosomal recessive, or sex-linked inheritance of a particular characteristic.

A link to an interactive elements can be found at the bottom of this page.

12.6 Disorders of the Muscular System

Figure 12.6.1 Devices can be a pain in the neck – literally.

Spending hours each day looking down at hand-held devices is a pain in the neck — literally. The weight of the head bending forward can put a lot of strain on neck muscles, and muscle injuries can be very painful. Neck pain is one of the most common of all complaints that bring people to the doctor’s office. In any given year, about one in five adults will suffer from neck pain. That’s a lot of pains in the neck! Not all of them are due to muscular disorders, but many of them are. Muscular disorders, in turn, generally fall into two general categories: musculoskeletal disorders and neuromuscular disorders .

Parental transmission and D18S37 allele sharing in bipolar affective disorder

We combined the five chromosome 18 bipolar affective disorder data sets provided by GAW10, totaling 185 families with 3,394 individuals, and performed analysis of differential parental transmission and chromosome 18 marker allele sharing in families with transmission through fathers vs those through mothers. Results indicated a significant excess of maternal transmission of bipolar disorder. All pedigrees were then broken into nuclear families and affected sib-pair linkage analyses performed on the marker, D18S37. There was significant linkage in the data overall, as well as in each subgroup of paternal, maternal and unknown parental transmission nuclear families. There were no significant differences in identical-by-descent (IBD) scores among the three transmission subgroups. These findings support an excess of maternal transmission, and linkage between bipolar disorder and marker D18S37. However, our results do not support the previous suggestion that there are differences in chromosome 18 marker allele sharing depending on the transmitting parent. © 1997 Wiley-Liss, Inc.


Given a sample of pedigrees we allow for the possibility that different families may segregate different rare variants, but assume that within a family genetic cases are due to a shared rare variant that increases disease susceptibility. We allow users to choose between two methods of rare variant introduction to the pedigree. One option is to assume that all ascertained pedigrees with genetic cases are segregating a variant that is rare enough to have been introduced by exactly one founder [6]. Alternatively, we allow users to simulate the starting founder’s rare variant status with probability equal to the carrier probability of all causal variants considered as a group. When this option is selected some ascertained pedigrees may not segregate a causal variant. In either scenario, we assume that a causal variant is introduced by at most one founder and, when it is introduced, it is transmitted from parent to offspring according to Mendel’s laws.

Starting at birth and ending with death, we simulate life events for the starting founder, censoring any events that occur after the last year of the study. We repeat this process, recursively, for all descendants of the founder allowing life events at the individual level to shape successive generations of the pedigree. To accomplish this, we condition on an individual’s age, rare-variant status and disease status, and simulate waiting times to three competing life events: reproduction (i.e. producing offspring), disease onset, and death. We select the event with the shortest waiting time, update the individual’s age by this waiting time, record the event type, and repeat this process from the new age until the individual dies or the end of the study is reached.

Simulating life events

To simulate life events SimRVPedigree users are required to specify: hazardDF , a data frame of age-specific hazard rates, where column one represents the age-specific hazard rates for the disease in the general population, column two represents the age-specific hazard rates for death in the unaffected population, and column three represents the age-specific hazard rates for death in the affected population, and partition , a discrete partition of ages over which to apply hazardDF .

Specifically, partition is a vector of ages, starting at age 0, such that hazardDF[k,] are the age-specific hazard rates for an individual whose age is contained in [ partition[k] , partition[k+1] ). At the user’s discretion, if the disease of interest is rare, the age-specific hazard rates for death in the unaffected population may be approximated by age-specific hazard rates for death in the general population. In the following subsections, we detail the procedures to simulate waiting times to onset, death, and reproductive events.

Disease onset

We model disease onset using a non-homogeneous Poisson process (e.g. [7]), conditioned on an individual’s current age, t ′ , rare-variant status, x, and disease status, δ. In this context, x=1 if the individual is a carrier of the rare variant, and 0 otherwise and δ=1 if the individual has developed disease by age t ′ , and 0 otherwise. Define κ to be the relative-risk of disease for individuals who have inherited the causal variant and λo(t) to be the baseline age-specific hazard rate of disease for an individual aged t years. That is, λo(t) is the age-specific hazard rate for individuals who do not carry a causal variant, i.e. sporadic cases. Let λonset(t|x) denote the age-specific hazard rate of disease for an individual aged t years conditioned on rare-variant status such that

If pc is the carrier probability of all causal variants considered as a group, then we can express the population age-specific hazard rate of disease, λonset(t), as

Users are expected to provide λonset(t) given pc and κ we infer λo(t) as (lambda _(t) = frac (t)><1 + p_(kappa -1)>.) We note that this method for calculating λo(t) has implications on the comparability of non-genetic individuals from studies simulated under very different κ values. For example, when pc is constant, we see that for κ1<<κ2, the age-specific hazard rate for non-carrier individuals under genetic relative-risk κ1 will be much greater than that of non-carrier individuals under genetic relative-risk κ2. As pc increases this effect is visible more quickly for differing κ values.

We note that not all individuals develop the disease however, those who do are only permitted develop the disease once in our model. Individuals who have developed disease (i.e. δ=1) do not develop disease again, but can reproduce or die. When δ=0, we use intensity function λonset(t|x) conditioned on rare-variant status, x, to simulate the waiting time to disease onset given current age, t ′ . To clarify, if we denote the waiting time to disease onset by Wonset, and condition on the current age, t ′ , the cumulative distribution function of Wonset is given by


We model death using a non-homogeneous Poisson process, conditioned on an individual’s current age, t ′ , and disease status, δ. Define δ as in the previous discussion, and let λu(t) and λa(t) denote the age-specific hazard rates of death, for individuals aged t years, in the unaffected population and the affected population, respectively. We use intensity function λdeath(t|δ) conditioned on disease status δ to simulate the waiting time to death given the current age, t ′ . In this context, λdeath(t|δ) represents the age-specific hazard rate of death for an individual aged t years conditioned on their disease status, which we model as

We do not model disease remission after an individual has developed disease we use the age-specific hazard rates for death in the affected population to model their waiting time to death.


To accommodate extra-Poisson variability in the number of human offspring, we use a negative-binomial model with number of trials n≈2 and success probability p≈4/7, as proposed by [8]. We adopt this negative-binomial model of offspring number in SimRVPedigree . We employ an equivalent Poisson-Gamma mixture model [9] to obtain the negative-binomial offspring number and to simulate the waiting time to reproduction.

Let (w_>) denote the waiting time to reproduction given an individual’s current age t ′ , and assume that simulated subjects are able to reproduce from age a1 to age a2. To mimic observed data on first-born live births (see Additional file 1: Section 6), we simulate a1 and a2 as follows: sample a1 uniformly from ages 16 to 27, and a2a1 uniformly from 10 to 18 years. At birth we simulate an individual’s lifetime birthrate by taking a random draw, γ, from a gamma distribution with shape 2 and scale 4/3. Individuals who draw large γ will have high birth rates and many children, whereas individuals who draw small γ will have low birth rates and few or no children.

For some diseases, users may want to reduce the birth rate after disease onset we allow users to achieve this through an additional parameter f, assumed to be between 0 and 1, which is used to rescale the birth rate after disease onset. By default, f=1 so that the birth rate remains unchanged after disease onset. Given an individual’s birth rate, current age, and disease status, δ, we obtain their waiting time to reproduction as follows:

Simulate the unconditional waiting time to reproduction by drawing w from an exponential distribution with rate (frac <(a_<2>-a_<1>)>) .

Condition on the current age, t ′ , to obtain the conditional waiting time to reproduction:

Pedigree simulation

To simulate all life events for a subject, starting at birth we generate waiting times to disease onset, death, and reproduction, as outlined previously and choose the event with the shortest waiting time to be the next life event. Next, we add the waiting time associated with the earliest event to the current age and either record the year of disease onset or death, or add a new offspring to the pedigree. We repeat this process from the updated age, recursively, until the individual dies or the study stop year is reached. This algorithm details the full life event procedure at the individual level. Complete details are available in Additional file 1.

To simulate a full pedigree, we recursively apply the algorithm described above, as follows:

Step 1: Simulate life events for the first founder given rare-variant status.

Step 2: Simulate life events for any new offspring given rare-variant status as outlined above.

Step 3: Repeat step 2 until life events have been simulated for all offspring.

Ascertainment features

The primary function of SimRVPedigree , sim_RVped() , simulates pedigrees ascertained for multiple disease-affected relatives. We allow users to specify family-based study features through the following arguments of sim_RVped() : num_affected : the minimum number of disease-affected relatives required for ascertainment of the pedigree. ascertain_span : the start and stop year for pedigree ascertainment. stop_year : the last year of follow-up for the pedigree. recall_probs : the proband’s recall probabilities for relatives of varying degree.

In this context, the proband is the affected family member first in contact with the study, presumably at the time of disease onset.

The ascertainment span represents the time span, in years, over which the family could be ascertained through the proband. For example, suppose that a particular study ascertained families, containing at least two affected members, from 2000 to 2010. In this scenario, the user would set ascertain_span= c(2000, 2010) and num_affected= 2 . The sim_RVped() function would then simulate families such that the proband developed disease between 2000 and 2010 and was at least the second family member to develop disease.

The study stop year represents the last year data are collected for ascertained families. Consider the previous study, and suppose that data were collected until 2016. To achieve this in simulation, users would simply specify stop_year = 2016 , which would result in sim_RVped() simulating life events for ascertained families until the year 2016.

Often researchers involved in family-based studies are confronted by incomplete ascertainment of a proband’s relatives, which could occur if the proband cannot provide a complete family history, or if he or she does not support contact of specific relatives. SimRVPedigree allows users to mimic this scenario, in simulation, by trimming relatives from a pedigree based on the proband’s probability of recalling them. To specify a proband’s recall probabilities for his or her relatives, i.e. recall_probs , the user provides a list of length q, such as p=(p1,p2. pq). In this context, pi is used to denote the proband’s recall probability for a relative of degree i when i=1,2. q−1, or the proband’s recall probability for a relative of degree q or greater when i=q. To simulate fully ascertained families, we set recall_probs = c(1) , which corresponds to p=1. Alternatively, if unspecified, recall_probs is set to four times the kinship coefficient, e.g. [10]. This default value retains the proband’s first-degree relatives (i.e. parents, siblings, and offspring) with probability 1, second-degree relatives (i.e. grandparents, grandchildren, aunts, uncles, nieces, and nephews) with probability 0.5, third-degree relatives with probability 0.25, etc.

In the event that a trimmed relative is required to fully specify the relationships among recalled family members, we include the trimmed relative, mark them as unavailable, and remove (i.e. mark as missing) any of their relevant information. That is, disease status, relative-risk of disease, and event years are all missing for any relatives not recalled by the proband. Since disease-affected relatives may be trimmed from a pedigree, trimmed pedigrees may contain fewer than num_affected disease-affected relatives. When this occurs, sim_RVped() will discard the pedigree and simulate another until all conditions specified by the user are met.


More than twin and family studies, adoption studies allow for the separation of genetic and environmental effects, because children do not share home environments with their biological parents. The major drawback to this type of design is that adoptive homes underrepresent high-risk environments, i.e., those at the extremes of poverty and deprivation (see Rutter and Silberg (4) for additional limitations). This is especially important because it has been suggested that gene-environment interactions may only exist at the extremes of genetic and environmental variation, hence adoption studies may underestimate the effects of environmental risk and protective factors and may not always detect true gene-environment interactions (40).

For the most part, adoption study investigations of gene-environment interaction have used biological family history of mental disorder as an indicator of genetic risk, and examined its relationship to psychosocial risk and protective factors in the adoptive family. Results from studies investigating the effects of family variables such as family conflict, poor cohesion, and deviant communication indicate that a wide range of mental disorders, including alcoholism, antisocial behavior (ASB), depression, and schizophrenia share these risk factors and that, for each disorder, these environmental influences interact with genetic risk to exacerbate psychiatric symptoms.

An early adoption study found that male (but not female) adoptees with an alcoholic biological parent were more likely to develop certain types of alcoholism if they were also at environmental risk, based on adoptive family characteristics, pre-placement conditions, and age at adoptive placement (41). Cutrona et al (42) found evidence for gene-environment interaction in alcoholism in a US sample of adoptees. Neither a biological background of alcoholism nor any family environmental variables increased risk for alcohol abuse or dependence in female adoptees. However, women (but not men) with at least one alcoholic biological parent who also experienced early-life family conflict and/or adoptive family psychopathology were more likely to become alcoholic than those with low levels of family conflict. In other words, neither a biological background of alcoholism nor environmental stress alone was sufficient to lead to alcoholism in the adoptees, but a combination of the two increased the risk.

Adoption studies have also found evidence for a gene-environment effect on ASB, such that individuals at high genetic risk are more sensitive to adoptive family conflict. Cloninger et al (43) found a synergistic effect for genetic and environmental risk factors in a Swedish sample, such that adoptees at both genetic risk (i.e., criminal biological parents) and environmental risk (i.e., adverse rearing experiences and poor quality adoptive placements) had significantly higher rates of petty criminality than adoptees at either biological or environmental risk alone. In other words, adoptees with genetic predispositions towards criminality also were more likely to be affected by negative environmental experiences. Rutter (44) noted that a problem with this type of study involved the use of parental criminality as a measure of genetic risk, both because it was crude, and also because it did not provide information on the mechanism of the genetic effect. Parental criminality could be an index of any of a number of psychopathological, physiological, or cognitive risk factors in the child.

Cadoret and colleagues conducted a series of adoption studies investigating ASB and consistently found evidence for an interaction between a genetic background of ASB and an adverse adoptive home environment (45-48). In the most recent study, antisocial personality disorder (ASPD) and substance abuse/dependence in the biological parent were used as indicators of genetic risk, and environmental risk was indexed by a composite measure of marital, legal, and psychological problems in the adoptive parents (48). These family environmental factors increased the risk for childhood aggression, adolescent aggression, and conduct disorder (but not adult ASB), but only in the presence of a biological background of ASPD. There was virtually no effect of the environment on those adoptees not at genetic risk. Unlike the earlier studies which combined ASB and substance abuse as an index of genetic risk (46), this study was able to separate the genetic influences associated with both. The results showed that a biological background of alcohol abuse did not interact with adverse adoptive home environment to increase risk for ASB, which demonstrates the specificity of the genetic diathesis for ASB.

Not all adoption studies, however, replicated the observed gene-environment interaction between a biological background of antisocial behavior/traits and environmental risk, in the form of adoptive parent antisocial behavior/traits (49, 50). Moreover, evidence for gene-environment correlation in adoptee ASB demonstrates that additional factors may be operating to influence child ASB, and that care must be taken when conducting studies investigating gene-environment interaction. Both Ge et al (51) and O'Connor et al (52) found an association between a biological background of antisociality and adoptive parenting behavior that was mediated by the child's behavior, such that adoptee antisociality led to harsh and inconsistent behaviors on the part of the adoptive parents, which increased the child's own antisocial behaviors.

The same disturbed adoptive parent variable examined in Cadoret et al (48) also interacts with genetic risk factors to influence MD in women. In another study, for instance, Cadoret et al (53) showed that females (but not males) with a genetic background of alcoholism are at increased risk for MD if they live in an adoptive family with a high number of disturbed behaviors. There was no effect of environmental stress in the absence of an alcoholic background. This finding is in accord with theories suggesting that alcoholism is a marker for genetic risk that leads to depression and alcoholism in females, but only alcoholism in males (54).

An adverse adoptive home environment has also been implicated as a source of potential risk for schizophrenia. Findings from the Finnish adoption studies show an increased risk for schizophrenia in the biological offspring of schizophrenic versus non-schizophrenic parents, but only for those high-risk adoptees who were also exposed to a dysfunctional family rearing environment (55, 56). Wahlberg et al (57), also using the Finnish sample, demonstrated that symptoms of thought disorder (i.e., an indicator of schizophrenia vulnerability) in offspring of schizophrenic mothers were more probable when they were raised by adoptive mothers who themselves showed elevated levels of 'communication deviance'. In contrast, offspring of schizophrenic mothers, raised by adoptive parents with low communication deviance, were less likely to show thought disorder. There was no relationship between thought disorder in control adoptees and communication deviance in the adoptive parents. In other words, this gene-environment interaction effect suggests that adoptees without a pre-existing genetic liability were not vulnerable to the effects of a disturbed family environment (at least with respect to thought disorder), and individuals with a pre-existing genetic liability expressed this liability only in the presence of additional adverse environmental factors.

Rutter and Silberg (4) suggested that results such as these from twin and adoption genetic studies, i.e., demonstrating gene-environment interaction, have so far been supportive of the hypothesis that the impact of environmental risk factors on psychopathology is slight in the absence of genetic risk. It is likely that research into gene-environment interaction will progress once genetic marker information can be incorporated into quantitative genetic studies, so that subjects with known genotypes can be exposed to environmental manipulations, allowing for a more experimental approach to the investigation of nature-nurture interplay in human beings. One method of incorporating genotypes into studies of gene-environment interaction is considered in the following section.

Genome-wide association analysis of depressive disorder

As noted above, 㸠 genes have been associated with DD onset and confirmed by meta-analyses. In most cases, these associations involve genes that are not directly linked to the general theories of depression ethnopathogenesis. Association studies appeared to be connected with transition to genome-wide methods of association analysis without any suggestions about the genetic risk factors of depression.

In the first stage of GWASs, families with members that have experienced multiple depression events, severe course of the disorder, or an early age at its clinical onset were analyzed with special interest in patients with rare monogenic forms of depression. The results of these studies are summarized in Table ​ Table3. 3 . These studies have reported associations with extended genomic regions (even as long as full-length chromosomes), and the identification of the candidate genes seems to be provisional. This identification mainly based on the DD candidate genes mapped earlier in these genome regions.

Table 3

Mapping the loci associated with predisposition to different forms of depression in family studies using SNP panels of DNA markers.

ReferenceClinical phenotypeChromosomeCandidate gene
(101)Recurrent depression with early onset15q25.3�.2NTRK3 (neurotrophin 3 receptor)
(102)Recurrent depression, depression-predominant bipolar disorder12q23NA
(103)Depression with early (31 years of age) onset depression with anxietychromosomes 3centr, 7p and 18qNA
(104)Recurrent depression without symptoms of bipolar disorder1p36, 12q23.3-q24.11 and 13q31.1-q31.3NA
(105)Depressive disorderchromosomes 17 and 8SLC6A4 (solute carrier family 6 member 4)

GWASs have been used increasingly in the past decade to identify loci that control complex traits. In this analysis, as many as hundreds of thousands to several millions of SNPs distributed over the whole genome are identified in groups of persons having a particular trait of interest. Analysis of the genotype–phenotype associations makes it possible to establish a link between the allelic variant in some particular region of the genome with the trait studied. The principal difference between GWASs and candidate gene studies using the case𠄼ontrol method is that there is no preliminary hypothesis to explain the contribution of polymorphic variants of genes to the development of a pathology of interest. However, for a study to achieve statistically significant results, its algorithm requires very large samples of both patients and healthy persons. It can be extremely difficult to achieve clinical homogeneity in very large samples, especially when studying psychiatric diseases because there is always a subjectivity factor affecting the diagnostic accuracy in the relevant international classifications with almost no instrumental methods for assessing the patient's condition.

A number of studies have searched for loci associated with MDD or individual symptoms of depression. The results are summarized in Table ​ Table4. 4 . This table focuses mainly on those studies that analyzed primarily the risk of depression as a disease and not the endophenotypes (e.g., clinical onset age, severity of particular symptoms, patients' responses to therapy). As well, Table ​ Table4 4 includes the most statistically significant results from the analyzed articles.

Table 4

Genome-wide association studies of major depressive disorders (MDDs) and recurrent depressive disorders (RDDs).

ReferenceClinical phenotypeDNA-marker with the smallest P-valueP-valueGene near SNPGene function, metabolic pathway
(106)MDDrs27151487.7 × 10 𢄧 PCLO (piccolo presynaptic cytomatrix protein)The protein encoded by this gene is part of the presynaptic cytoskeletal matrix involved in the formation of active synaptic zones and transport of synaptic vesicles
(107)RDDrs42380105.80 × 10 𢄦 CCND2 (cyclin D2)The protein encoded by this gene is involved in control of cell cycle regulation (Gl/S transition) in complex with CDK4 or CDK6 kinases
1.30 × 10 𢄧
3.1 × 10 𢄦
BICC1 (bicaudal C homolog 1)Encodes an RNA-binding protein that is involved in gene expression regulation by modulating protein translation in embryogenesis
1.66 × 10 𢄧

The first GWAS of a large representative sample (1738 DD patients, 1802 controls) was reported by Sullivan et al. (106). In this study, no association with any of SNPs achieved the value of genome wide significance. The maximum significance was found for the rs2715148 (p = 7.7 × 10 𢄧 ). Also, in this genomic region near PCLO gene 10 more SNPs were associated with DD with relatively low significance (p = 10 𢄥 -10𠄶). They were mapped to a 167 kb region where PCLO was located (106). PCLO protein localizes in the cytoplasmic matrix of the presynaptic active zone and plays a significant role in brain monoaminergic neurotransmission. A possible role of this region in depression onset was confirmed by Hek et al. (115), who showed an association between the rs2522833 SNP in PCLO and DD in a population-based study from the Netherlands (115). Aragam et al. (113) found a close statistically significant association between DD development for the rs2715148 SNP (P = 5.64 × 10 𢄧 ) in PCLO in women (113). This study found another SNP in LGSN that was associated with DD occurrence in men (rs9352774, P = 2.26 × 10 𢄤 ). This gene is actively expressed in the human crystalline lens and encodes a protein related to GS-I and, to a lesser degree, to GS-II glutamine synthetases. This protein may play a role in glutamate exchange in both the retina and the nervous system.

A role of glutamate in DD was found in a GWAS conducted by Rietschel et al. (109). They found an association between DD and the rs7713917 SNP (P = 5.87 × 10 𢄥 ) located in a putative regulatory region of HOMER1, which encodes proteins involved in glutaminergic processes via interaction with the metabotropic glutamate receptors mGluR1 and mGluR5.

We reiterate that the associations discovered in most GWASs did not attain a genome-wide significance level, primarily because of the genetic architecture of complex traits predisposing to depression. Adjustments of the genome-wide significance level are very rigorous, and we believe that SNP markers with a probability value close to the genome-wide threshold level should also be considered.

Some studies have achieved a genome-wide significance level. Kohli et al. (112) were the first to report an association between DDs and the rs1545843 SNP in SLC6A15 (solute carrier family 6, neutral amino acid transporter, member 15) in a recessive model of the effect of this polymorphism on the risk of DDs (112). This gene encodes the neutral amino acid transporter, and different rs1545843 alleles were shown to have different SLC6A15 expression levels in the hippocampus of epileptic patients. The authors presented additional evidence to support the involvement of this association and showed that the presence of the risk allele correlated with lower SLC6A15 expression in the hippocampus, smaller hippocampus volume, and neuronal integrity in vivo. Lower expression of Slc6a15 was also observed in the hippocampus of mice with elevated chronic stress susceptibility.

Kohli et al. (112) reported abundant data in support of the association between SLC6A15 and DD. However, subsequent GWAS disclosed no significant associations with this gene. The data obtained in GWASs are often not reproducible, and only one gene, PCLO, appeared to be associated with DD in two GWASs.

The Psychiatric Genomics Consortium (PGC) performed a meta-analysis of GWAS data. Unlike the conventional meta-analyses, which summarize the statistical data for each constituent analysis examined, the PGS study brought together and examined individual genotypic and phenotypic data from patients from different research centers. The PGS published the results of its genome-wide comparative analysis of 9240 samples collected from DD patients and 9519 samples from a control group of nine European populations (122). However, in the PGS analysis, none of SNPs identified in earlier studies achieved a genome-wide significance level. The SNPs with the most significant values were rs11579964 (P = 1.0 × 10 𢄧 ), which mapped near CNIH4, NVL, and WDR26, and rs7647854 (P = 6.5 × 10 𢄧 ), which mapped near C3orf70 and EHHADH. A subsequent replicative study conducted using an independent sample (6783 patients with MDD and 50,695 controls) did not confirm the associations mentioned.

Therefore, no locus has been shown to be consistently associated with a DD at a whole-genome significance level. Associations shown in independent samples have also not been reproduced. This lack of significance and reproducibility may reflect the particular features of the GWAS methodology, which has focused on polymorphic sites with a high minor allele frequency (ϥ%) in the associative analysis. These frequent polymorphic variants themselves are probably not pathogenically essential, but there may be disequilibrium linkages with rare variants of genes associated with DD pathogenesis. These rare variants may be specific for different populations. As a result, any association between the disease and a frequent polymorphic site may be found in one sample and may reflect the disequilibrium linkage of this polymorphic site with a rare, pathogenically significant variant in that sample. However, the pathogenically significant site may be missing in another sample and, as a consequence, no association of frequent polymorphism with DD occurrence will be found. In addition, the important role of rare genomic variants (a frequency ρ%) has been reported in association with other mental disorders, such as schizophrenia and autism (123, 124).

To overcome these problems, transition from the analysis of polymorphic DNA markers using microarrays to low-coverage DNA sequencing may provide a new direction for research to identify DD-associated genetic variants. The first study of this kind was conducted within the CONVERGE Project (125) and included genome sequencing with an average coverage of 1.7 × in � Chinese females 5000 females out of this group were patients with melancholic depression, which is recognized as a more severe form of depression. This study found two loci bearing an association at a 10 𢄨 significance level: one on the 5′-side of SIRT1 (SNP rs12415800) and the other in an LHPP intron (SNP rs35936514). This association was confirmed in an independent sample of melancholic Chinese women, and the significance values combined for the two samples were 2.53 × 10 � for SIRT1 and 6.45 × 10 � for LHPP. It is important to note that both associated SNPs occur frequently (e.g., the minimal allele frequencies were 45.3 and 26.2%), yet neither is included in the microarrays used widely for SNP marker typing and, therefore, may have been ignored in earlier GWASs.

Further analysis of the data in this project showed that frequent SNPs accounted for 20�% of the DD risk dispersion, which suggested that the heritability of DD is evenly distributed over all chromosomes with preferential localization of DD-associated SNPs in both the coding and the 3′-untranslated areas of genes. DD patients showed an elevated frequency of unique mutations in gene coding regions, primarily in the genes actively expressed in nervous tissue (126).

Importantly, this study included a specific ethnic group (Han Chinese), which is sufficiently homogeneous, and only females, who show a higher heritability level as mentioned above, with a severe form of DD. This design included a more rigorous approach to inclusion of samples and consideration of factors such as the patients' sex, clinical DD variation, clinical onset age, and other factors that can affect the risk of disease and its progression. However, these factors may exert no influence on the risk of DD development for example, the clinical onset age was recently shown to not affect the association analysis results in the Chinese CONVERGE sample (127).

Another study also found that ethnicity was important (128). That study included a combined analysis of the results obtained in the CONVERGE investigation of Chinese and of studies conducted by the PGC in different European populations. These studies found that some SNPs influence the risk of DD onset in both ethnic groups mentioned but, at the same time, detected a set of SNPs specific to each ethnic group. The highest contribution of genetic factors in both ethnic groups was observed in females and in recurrently depressed patients.

Powers et al. (116) attempted to include environmental factors into GWASs (116). They included as a factor stress-provoking events when including case𠄼ontrol pairs of patients in the study𠅊 method referred to as propensity score matching. This analysis allowed them to reduce the heterogeneity of the samples with regard to the stress factor and to compare DD patients and healthy controls exposed to similar stressors.

The genetic structure of depression appears to be extremely complicated and involves a large number of loci, which cause various phenotypic effects and display complex interlocus interactions. Studies of the genetic structure suggest the need for a transition from the analysis of individual SNPs to that of sets of SNPs and, finally, to include a polygenic risk score, as used in genetics research of schizophrenia (129).

To address similar problem, a strategy for studying gene networks created by uniting signals from numerous SNPs and subsequent functional analysis of the signaling and metabolic pathways have been used with success. This approach provides for an increase in the power of comparative analysis of weak signals from numerous loci. The study by Song et al. (130) is an example of such an analysis. On the basis of a GWAS of samples from European cohorts, the authors conducted a search and analysis of DD-linked SNPs and genes with these SNPs to discover signal pathways linking these genes to each other (130). Five resulting signal paths were found to play a role in DD pathogenesis. Three of them were claimed to be connected in some way with the negative regulation of gene expression (GO:0016481, GO:0045934, GO:0010629) and were related to some DD-associated SNPs: rs3213764 in ATF7IP rs2301721 in HOXA7 rs6720481 in LRRFIP1 rs2229742 in NRIP1.

Okbay et al. (118) and Hyde et al. (131) offered an alternative approach for sampling (118, 131). To diagnose DDs, they compiled a questionnaire to be completed by the respondents. Depression was diagnosed on the basis of the respondents' answers to the questionnaire with no clinical diagnosis by a psychiatrist. Although the accuracy of the diagnosis may be questioned, the questionnaire included questions on a wide range of phenotypic traits, and respondents could not associate them with any diagnoses. Data from biobanks or mass genotyping services such as 23 and Me allowed them to markedly increase the sample size. For example, the study by Hyde et al. (131) included 𾑐,000 individuals, and analysis of their questionnaire data allowed them to diagnose depression in about 120,000 participants (131). Samples of this size are an order of magnitude greater than those included in the PGC studies or CONVERGE Project, and help to minimize the problems caused by DD diagnostic errors. The authors managed to identify 17 SNP markers in 15 loci whose significance level was ϥ × 10 𢄨 , which reflects the size of the sample analyzed. The DNA markers detected differ from those associated with DD in the PGC studies, although they both analyzed samples of European origin. Therefore, the problem of the reproducibility of results obtained in the GWASs remains to be solved.

A possible way to solve this problem is to conduct a meta-analysis of GWA studies. This analysis was carried out by Wray et al. (121). This meta-analysis identified 44 independent loci that were statistically significant (P < 5 × 10 𢄨 ). Of these loci, 30 are new and 14 were significant in a prior study of MDD or depressive symptoms, and 6 shared loci with schizophrenia. Thus, the increase in sample sizes in the meta-analysis, on the one hand, allows the confirmation of the results obtained earlier with GWAS for the previously described loci associated with MDD. On the other hand, it increases the power of the study, by increasing the sample size, making it possible to identify new loci associated with the MDD.

Several methods were proposed for calculating of genetic risk score (GRS): simple count genetic risk score (SC-GRS), odds ratio weighted genetic risk score (OR-GRS), direct logistic regression genetic risk score (DL-GRS), polygenic genetic risk score (PG-GRS) and explained variance weighted genetic risk score (EV-GRS). Currently, the most widely used method is polygenic risk score (PGRS) (132). This approach has been used to obtain evidence of a genetic effect even when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models (133). Currently, alternative approaches to statistical analysis of GWASs data are proposed, where the analysis is not of individual DNA markers, but their combinations. Recently, several papers have been published using PGRS for the MDD and other psychiatric disorders (134�). The possibility of using the PGRS to evaluate the cumulative contribution of several polymorphic variants of genes to the formation of endophenotypes of MDD was demonstrated. Whalley et al. (135), using the PGRS, divided the MDD into two subtypes, one of which is close to schizophrenia (135).

Unit 6: Genetic Disorders START HERE!

Copy any names (⌘C on a Mac) you want to check out, then paste (⌘V on a Mac) into any search engine (such as Google), just to get an idea of what is involved in the disorder before you commit to it on the sign-up sheet.

Limit 2 people per topic 1st come, 1st served. Once you choose, no switching!

Angelman Syndrome

Canavan Disease

Coffin Lowry Syndrome

Cri du Chat

Cystic Fibrosis

Down Syndrome

Hemophilia A

Huntington’s Disease

Kleinfelter Syndrome

Marfan Syndrome

Menkes Syndrome

Muscular Dystrophy (Duchenne)

Phenylketonuria (PKU)


Progeria (Hutchinson-Gilford)

Proteus Syndrome

Sickle Cell Anemia

Sotos Syndrome

Turner Syndrome

Werner Syndrome

If there is another topic you are interested in, ask Mrs. A!

Most disorders have online support groups, with links to good, informative sites designed to teach you about it.

Some sites or research publications you find may be wordy and technical. Skip these sites, there are lots of other sites available that are designed for beginners.

Remember, you need 3 solid sources, including a picture.

To find PowerPoint, go to the Finder, look under Applications, and find Microsoft Office.


– Follow the guidelines in the Project description. Check off requirements as you go.

– Get all the facts in before doing any fancy designs or transitions (make sure we can still read the text after you get creative).

– Practice saying unfamiliar words before presenting them to the class.

– Help yourself and avoid plagarism by rewording some of the technical terms.

– Remember, if you don’t understand what you wrote, your audience won’t either, so simplify when possible.

– Don’t forget to SAVE OFTEN & SHARE! Ask for passes if you need time outside class.

Lost project description? A copy of it can also be downloaded from this blog.

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

Conflict of interest statement

The authors have declared that no competing interests exist.


Fig 1. Steps of the high-resolution genotypic…

Fig 1. Steps of the high-resolution genotypic data curation workflow to ensure a quick and…

Histograms of B-allele frequency (left)…

Histograms of B-allele frequency (left) and B-allele frequency for each SNP plotted against…

Feature: My Human Body

You read in this section about the many dangers of hypertension . Do you know if you have hypertension? The only way to know for sure is to have your blood pressure measured. Measuring blood pressure is quick and painless, but several measurements are needed to accurately diagnose hypertension. Some people have what is called “white coat disease.” Their blood pressure rises just because they are being examined by a physician (in a white coat). Blood pressure also fluctuates from time to time due to factors such as hydration, stress, and time of day. Repeatedly measuring and recording your own blood pressure at home can provide your doctor with valuable diagnostic data. Digital blood pressure monitors for home use, like the one in Figure 14.6.9, are relatively inexpensive, easy to use, and available at most pharmacies.

Figure 14.6.9 This personal blood pressure monitor is worn on the wrist.

If you do have high blood pressure, lifestyle changes with or without medications can usually bring it under control. A commonly recommended lifestyle change is the adoption of a healthier eating plan, such as the DASH (“Dietary Approaches to Stop Hypertension”) diet. This diet was developed specifically to lower blood pressure without medication. Numerous studies have found the DASH diet to be effective at reducing not only high blood pressure, but also the risk of coronary artery disease, heart failure, stoke, some kinds of cancer, and diabetes. This diet has also been found effective for weight loss. The DASH diet includes whole grains, fruits and vegetables, low-fat or nonfat dairy, lean meats, fish and poultry, beans, nuts, and seeds.


The authors wish to thank all of the many participants in the study, without whom this work would not have been possible.

We have used DNA samples and clinical data from the National Institute of Mental Health Genetics Initiative bipolar disorder family sample ( which is housed at the NHGRI Repository (, and thank all those involved in generating this resource, who are listed in the Supplementary Material. Genome-wide SNP genotyping of the NIMH samples was performed through the Genetic Association Information Network under the direction of The Bipolar Genome Study (BiGS) Consortium. We thank Kerrie Pierce (NeuRA) and Tamara McDonald (AGRF) for their assistance with sample preparation and genotyping.

This work was supported by the Australian National Medical and Health Research Council (program grant 1037196 project grant 1066177) and the National Institutes of Health collaborative R01s (grant nos. MH68009, MH073151, and MH068006). We gratefully acknowledge the Heinz C Prechter Bipolar Research Fund at the University of Michigan, and the Janette Mary O'Neil Research Fellowship (to JMF) for supporting this work. The funding agencies played no role in the design, analyses, or interpretation of this study.

DNA for the US participants was extracted by the Rutgers University Cell and DNA Repository (RUCDR DNA for the Australian sample was extracted by Genetic Repositories Australia (GRA, an Enabling Facility which is supported by an Australian National Health, and Medical Research Council (grant ID 401184).

Genotyping of the US sample (at-risk and bipolar family sample) was conducted at the Center for Medical Genomics at Indiana University School of Medicine. Genotyping of the Australian at-risk sample was conducted at the Australian Genome Research Facility (AGRF, which is accredited by the National Association of Testing Authorities and supported by the Commonwealth Government of Australia.

Control subjects from the National Institute of Mental Health Schizophrenia Genetics Initiative (NIMH-GI), data and biomaterials are being collected by the “Molecular Genetics of Schizophrenia II” (MGS-2) collaboration. The investigators and co-investigators are: ENH/Northwestern University, Evanston, IL, MH059571, Pablo V. Gejman, M.D. (Collaboration Coordinator PI), Alan R. Sanders, M.D. Emory University School of Medicine, Atlanta, GA, MH59587, Farooq Amin, M.D. (PI) Louisiana State University Health Sciences Center New Orleans, Louisiana, MH067257, Nancy Buccola A.P.R.N., B.C., M.S.N. (PI) University of California-Irvine, Irvine, CA, MH60870, William Byerley, M.D. (PI) Washington University, St. Louis, MO, U01, MH060879, C. Robert Cloninger, M.D. (PI) University of Iowa, Iowa, IA, MH59566, Raymond Crowe, M.D. (PI), Donald Black, M.D. University of Colorado, Denver, CO, MH059565, Robert Freedman, M.D. (PI) University of Pennsylvania, Philadelphia, PA, MH061675, Douglas Levinson, M.D. (PI) University of Queensland, Queensland, Australia, MH059588, Bryan Mowry, M.D. (PI) Mt. Sinai School of Medicine, New York, NY, MH59586, Jeremy Silverman, Ph.D. (PI).

Bipolar Genome Study (BiGS) Co-Authors

John R. Kelsoe, Tiffany A. Greenwood, Caroline M. Nievergelt, Rebecca McKinney, Paul D. Shilling—University of California, San Diego, CA, USA Nicholas J. Schork, Erin N. Smith, Cinnamon S. Bloss—Scripps Translational Science Institute, La Jolla, CA, USA John I. Nurnberger Jr., Howard J. Edenberg, Tatiana Foroud, Daniel L. Koller—Indiana University, Indianapolis, IN, USA Elliot S. Gershon, Chunyu Liu, Judith A. Badner—University of Chicago, Chicago, IL, USA William A. Scheftner—Rush University Medical Center, Chicago, IL, USA William B. Lawson, Evaristus A. Nwulia, Maria Hipolito—Howard University, Washington, D.C., USA James B. Potash, William Coryell—University of Iowa, Iowa City, IA, USA John Rice—Washington University, St. Louis, MO, USA William Byerley—University of California, San Francisco, CA, USA Francis J. McMahon, Thomas G. Schulze—National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA Wade H. Berrettini—University of Pennsylvania, Philadelphia, PA, USA Peter P. Zandi, Pamela B. Mahon—Johns Hopkins School of Medicine, Baltimore, MD, USA Melvin G. McInnis, Sebastian Zöllner, Peng Zhang—University of Michigan, Ann Arbor, MI, USA David W. Craig, Szabolcs Szelinger—The Translational Genomics Research Institute, Phoenix, AZ, USA Thomas B. Barrett—Portland Veterans Affairs Medical Center, Portland, OR, USA Thomas G. Schulze—Georg-August-University Göttingen, Göttingen, Germany.

Bipolar High Risk Study Group Co-Authors

John I. Nurnberger, M.D., Ph.D. Leslie Hulvershorn, M.D. Carrie Fisher, B.S.N.—Department of Psychiatry, Institute of Psychiatric Research, Indiana University School of Medicine, Indianapolis Hai Liu, Ph.D. Patrick O. Monahan, Ph.D.—Department of Medicine, Institute of Psychiatric Research, Indiana University School of Medicine, Indianapolis Melvin McInnis, M.D. Masoud Kamali, M.D. Christine Brucksch, R.N.—Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor Anne Glowinski, M.D., M.P.E.—Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri Holly C. Wilcox, Ph.D.—Department of Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland Philip B. Mitchell, M.B.B.S., M.D.—School of Psychiatry and Black Dog Institute, University of New South Wales, Sydney, Australia Elliot S. Gershon, M.D.—Department of Psychiatry, University of Chicago Pritzker School of Medicine, Chicago, Illinois Wade Berrettini, M.D., Ph.D.—Department of Psychiatry, University of Pennsylvania Health System, Philadelphia.

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