Age of the father has a lot to do with having an Autistic Child.

Here are a few reports on studies done by scientists on what can happen if the father waits too long to have a child. I don’t think you need a PHD to read this. Never the less these reports offer some evidence in a possible cause for Autism. It seems that the longer the father waits, the more things he becomes exposed to that cause genetic mutations. A mans sperm is completely re-manufactured every time he uses it. On the other hand the women are born with all the eggs they will ever need. I think a mans sperm are more and more likely to get exposed to something that will mutate it the longer he waits. There is a rule about copying something over and over and having it come out more and more fuzzy. The same seems to be true about sperm. Eggs on the other hand are never copied. The top 10 things that are bad for a human are as follows.organophospnate pesticides – fruit
lead – water and paint
organochlorine pesticides
automotive exhaust
polycyclic aromatic hydrocarbons – cigarettes
endocrine disruptore – Pesticides
Methylmercury – fish and Shellfish
Brominated flame retardants – Mattresses
Polychlorinated biphenyls – Coolant fluids
Perfluoriunated compounds – food packaging     This list is from a Generation Rescue List.

I am sure there are more. I myself still think a woman ingesting or injecting poisons can damage the egg and growth cycle. I have seen this myself too much to not believe it.

One On Age

So go ahead and read this. It does not mean that if you have a child at 43 instead of 23 he or her is going to have problems for sure. On the other hand the trend for people to settle down into a family after a long while did grow as the trend for Autism grew.

The data mining for this web page was done by Elizabeth Campion once more.  Good Job!

Article 1: https://archpsyc.jamanetwork.com/article.aspx?articleid=1833092  From the Journal of the American Medical Association, Psychiatry (JAMA)Paternal Age at Childbearing and Offspring Psychiatric and Academic Morbidity ONLINE FIRST

Brian M. D’Onofrio, PhD1; Martin E. Rickert, PhD1; Emma Frans, MSc2; Ralf Kuja-Halkola, MSc2; Catarina Almqvist, MD2,3; Arvid Sjölander, PhD2; Henrik Larsson, PhD2; Paul Lichtenstein, PhD2

JAMA Psychiatry. Published online February 26, 2014. doi:10.1001/jamapsychiatry.2013.4525 ABSTRACT

Importance:  Advancing paternal age is associated with increased genetic mutations during spermatogenesis, which research suggests may cause psychiatric morbidity in the offspring. The effects of advancing paternal age at childbearing on offspring morbidity remain unclear, however, because of inconsistent epidemiologic findings and the inability of previous studies to rigorously rule out confounding factors.

Objective:  To examine the associations between advancing paternal age at childbearing and numerous indexes of offspring morbidity.

Design, Setting, and Participants:  We performed a population-based cohort study of all individuals born in Sweden in 1973-2001 (N = 2 615 081), with subsets of the data used to predict childhood or adolescent morbidity. We estimated the risk of psychiatric and academic morbidity associated with advancing paternal age using several quasi-experimental designs, including the comparison of differentially exposed siblings, cousins, and first-born cousins.

Exposure:  Paternal age at childbearing.

Main Outcomes and Measures:  Psychiatric (autism, attention-deficit/hyperactivity disorder, psychosis, bipolar disorder, suicide attempt, and substance use problem) and academic (failing grades and low educational attainment) morbidity.

Results:  In the study population, advancing paternal age was associated with increased risk of some psychiatric disorders (eg, autism, psychosis, and bipolar disorders) but decreased risk of the other indexes of morbidity. In contrast, the sibling-comparison analyses indicated that advancing paternal age had a dose-response relationship with every index of morbidity, with the magnitude of the associations being as large or larger than the estimates in the entire population. Compared with offspring born to fathers 20 to 24 years old, offspring of fathers 45 years and older were at heightened risk of autism (hazard ratio [HR] = 3.45; 95% CI, 1.62-7.33), attention-deficit/hyperactivity disorder (HR = 13.13; 95% CI, 6.85-25.16), psychosis (HR = 2.07; 95% CI, 1.35-3.20), bipolar disorder (HR = 24.70; 95% CI, 12.12-50.31), suicide attempts (HR = 2.72; 95% CI, 2.08-3.56), substance use problems (HR = 2.44; 95% CI, 1.98-2.99), failing a grade (odds ratio [OR] = 1.59; 95% CI, 1.37-1.85), and low educational attainment (OR = 1.70; 95% CI, 1.50-1.93) in within-sibling comparisons. Additional analyses using several quasi-experimental designs obtained commensurate results, further strengthening the internal and external validity of the findings.

Conclusions and Relevance:  Advancing paternal age is associated with increased risk of psychiatric and academic morbidity, with the magnitude of the risks being as large or larger than previous estimates. These findings are consistent with the hypothesis that new genetic mutations that occur during spermatogenesis are causally related to offspring morbidity.

 

Article 2: http://www.nytimes.com/2014/02/27/health/mental-illness-risk-higher-for-children-of-older-parents-study-finds.html?_r=0  New York Times February 27, 2014
Mental Illness Risk Higher for Children of Older Fathers, Study Finds

By BENEDICT CAREY FEB. 26, 2014

Children born to middle-aged men are more likely than those born to younger fathers to develop any of a range of mental difficulties, including attention deficits, bipolar disorder, autism and schizophrenia, according to the most comprehensive study to date of paternal age and offspring mental health.

In recent years, scientists have debated based on mixed evidence whether a father’s age is linked to his child’s vulnerability to individual disorders like autism and schizophrenia. Some studies have found strong associations, while others have found weak associations or none at all.

The new report, which looked at many mental disorders in Sweden, should inflame the debate, if not settle it, experts said. Men have a biological clock of sorts because of random mutations in sperm over time, the report suggests, and the risks associated with later fatherhood may be higher than previously thought. The findings were published on Wednesday in the journal JAMA Psychiatry.

“This is the best paper I’ve seen on this topic, and it suggests several lines of inquiry into mental illness,” said Dr. Patrick F. Sullivan, a professor of genetics at the University of North Carolina, who was not involved in the research. “But the last thing people should do is read this and say, ‘Oh no, I had a kid at 43, the kid’s doomed.’ The vast majority of kids born to older dads will be just fine.”

Dr. Kenneth S. Kendler, a professor of psychiatry and human molecular genetics at Virginia Commonwealth University, also urged caution in interpreting the results. “This is great work from a scientific perspective,” he said. “But it needs to be replicated, and biomedical science needs to get in gear and figure out what accounts for” the mixed findings of previous studies.

The strengths of the new report are size and rigor. The research team, led by Brian M. D’Onofrio of Indiana University, analyzed medical and public records of about 2.6 million people born in Sweden from 1973 to 2001. Like many European countries, Sweden has centralized medical care and keeps detailed records, so the scientists knew the father’s age for each birth and were able to track each child’s medical history over time, as well as that of siblings and other relatives. Among other things, the analysis compared the mental health of siblings born to the same father and found a clear pattern of increased risk with increasing paternal age.

Compared with the children of young fathers, aged 20 to 24, those born to men age 45 and older had about twice the risk of developing psychosis, the signature symptom of schizophrenia; more than three times the likelihood of receiving a diagnosis of autism; and about 13 times the chance of having a diagnosis of attention deficit disorder. Children born to older fathers also tended to struggle more with academics and substance abuse.

The researchers controlled for every factor they could think of, including parents’ education and income. Older couples tend to be more stable and have more income — both protective factors that help to temper mental problems — and this was the case in the study. But much of the risk associated with paternal age remained.

“We spent months trying to make the findings go away, looking at the mother’s age, at psychiatric history, doing sub-analyses,” Dr. D’Onofrio said. “They wouldn’t go away.” Dr. D’Onofrio had seven co-authors, including Paul Lichtenstein of the Karolinska Institute in Stockholm and Dr. Catarina Almqvist of the Karolinska Institute and Astrid Lindgren Children’s Hospital in nearby Solna.

The researchers say that any increased risk due solely to paternal age is most likely a result of the accumulation of genetic mutations in sperm cells. Unlike women, who age with a limited number of eggs, men have to replenish their supply of sperm cells. Studies suggest that the cells’ repeated reproductions lead to the accumulation of random errors over time, called de novo mutations. Most such mutations are harmless, geneticists say, but some have been linked to mental disorders.

“It’s a plausible hypothesis at this point,” Dr. Sullivan said.

Experts say the numbers in the study look more alarming than they probably are. For example, Dr. Sullivan said, the overall prevalence of autism is 0.5 percent to 1 percent of the population, depending on the estimate and the location. But for the children of healthy parents in their 20s, the rate is perhaps one in 300, or even lower. A threefold increase would put the odds at about one in 100, still very low. The same goes for the risk of psychosis. The baseline rate is tiny for the children of young, healthy parents, and remains quite low even when doubled.

The researchers found much larger increases in risk for attention deficits (13-fold) and bipolar disorder (25-fold) associated with late fatherhood. “I don’t know what to do with those numbers,” Dr. Sullivan said, noting that two recent genetic studies found that the contribution of de novo mutations to the risk of mental disorders was “probably pretty low” compared with other factors.

“The question we now need to ask,” Dr. Kendler said, “is what else is going on with respect to older and younger siblings that could cause these differences.”

Article 3: Rate of de novo mutations and the importance of father’s age to disease risk,

Author(s):Soren Besenbacher , Michael L. Frigge , Daniel F. Gudbjartsson , Sigurjon A. Gudjonsson , Agnar Helgason and Hannes Helgason

Source:Nature. 488.7412 (Aug. 23, 2012): p471.

Document Type:Report

DOI:http://dx.doi.org/10.1038/nature11396

Copyright :COPYRIGHT 2012 Nature Publishing Group

http://www.nature.com/nature/index.html

Abstract: 

Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 Icelandic parent-offspring trios at high coverage. We show that in our samples, with an average father’s age of 29.7, the average de novo mutation rate is 1.20 x [10.sup.-8] per nucleotide per generation. Most notably, the diversity inmutation rate of single nucleotide polymorphisms is dominated by the age of the father at conception of the child. The effect is an increase of about two mutations per year. An exponential model estimates paternal mutations doubling every 16.5 years. After accounting for random Poisson variation, father’s age is estimated to explain nearly all of the remaining variation in the de novo mutation counts. These observations shed light on the importance of the father’s age on the risk of diseases such as schizophrenia and autism.

Full Text: 

The rate of de novo mutations and factors that influence it have always been a focus of genetics research (1). However, investigations of de novo mutations through direct examinations of parent-offspring transmissions were previously mostly limited to studying specific genes (2,3) or regions (4-7). Recent studies that used whole-genome sequencing (8,9) are important but too small to address the question of diversity in mutation rate adequately. To understand the nature of de novo mutationsbetter we designed and conducted a study as follows.

Samples and mutation calls

 

As part of a large sequencing project in Iceland (10-12) (Methods), we sequenced 78 trios, a total of 219 distinct individuals, to more than 30 X average coverage (Fig. 1). Forty-four of the probands (offspring) have autism spectrum disorder (ASD), and 21 are schizophrenic. The other 13 probands were included for various reasons, including the construction of multigeneration families. The probands include five cases in which at least one grandchild was also sequenced. In addition, 1,859 other Icelanders, treated as population samples, were also whole-genome sequenced (all at least 10 X, 469 more than 30 X). These were used as population samples to help to filter out artefacts. Sequence calling was performed for each individual using the Genome Analysis Toolkit (GATK) (Methods). The focus here is on single nucleotide polymorphism (SNP) mutations. The investigation was restricted to autosomal chromosomes.

Criteria for calling a de novo SNP mutation were as follows. (1) All variants that have likelihood ratio: lik(AR)/lik(RR) or lik(AA)/lik(RR) > [10.sup.4], in which R denotes the reference allele and A the alternative allele, in any of the 1,859 population samples, were excluded. Some recurrent mutations could have been filtered out, but the number should be small. The de novo mutation calls further satisfy the conditions that (2) there are at least 16 quality reads for the proband at the mutated site; (3) the likelihood ratio lik(AR)/lik(RR) is above 10 (10); and (4) for both parents, the ratio lik(RR)/lik(AR) is above 100. Applying criteria (1) to (4) gave 6,221 candidatemutations. Further examination led us to apply extra filtering (5) by including only SNPs in which the number of A allele calls is above 30% among the quality sequence reads of the proband. This was considered necessary because there was an abnormally high number of putative mutation calls in which, despite extremely high lik(AR)/lik(RR) ratios for the proband, the fraction of A calls was low (Supplementary Fig. 1). Applying (5) eliminated 1,285 candidate mutations (Supplementary Information). With high coverage, the false negatives resulting from (5) is estimated to be a modest 2% (Supplementary Information). After three more candidates were identified as false positives by Sanger sequencing (see section on validation), a total of 4,933 de novo mutations, or an average of 63.2 per trio, were called. (The denovo mutations are listed individually in Supplementary Table 1.)

[FIGURE 1 OMITTED]

Parent of origin and father’s age

For the five trios in which a child of the proband was also sequenced, the parent of origin of each de novo mutation called was determined as follows. If the paternal haplotype of the proband was transmitted to his/her child, and the child also carries the mutation, then the mutation was considered to be paternal in origin. If the child carrying the paternal haplotype ofthe parent does not have the mutation, then it is inferred that the mutation is on the maternal chromosome of the proband. Similar logic was applied when the child inherited the maternal haplotype of the proband. In the five trios, the average number of paternal and maternal mutations is 55.4 and 14.2, respectively (Table 1). If mutations were purely random with no systematic difference between trios, their number should be Poisson distributed with the variance equal to the mean. The data, however, show over-dispersion (Table 1). This is much more notable for the paternal mutations (variance = 428.8, P = 1.2 X [10.sup.-5]) than the maternal mutations (variance = 48.7, P = 0.016). Moreover, the number of paternal mutations has a monotonic relationship with the father’s age at conception of the child. Here, the mean number of paternal mutations is substantially higher than the mean number of maternal mutations (ratio = 3.9), but the difference is even greater for the variance (ratio = 8.8). Hence, variation of de novo mutation counts in these individuals is mostly driven by the paternal mutations.

 

Relationships between parents’ age and the number of mutations (paternal and maternal combined, as they could not be reliably separated without data from a grandchild) were examined using all 78 trios (Fig. 2). The number of mutations increases with father’s age (P = 3.6 X [10.sup.-19]) with an estimated effect of 2.01mutations per year (standard error = 0.17). Mother’s age is substantially correlated with father’s age (r = 0.83) and, not surprisingly, is also associated with the number of de novo mutations (P = 1.9 X [10.sup.-12]). However, when father’s age and mother’s age were entered jointly in a multiple regression, father’s age remained highly significant (P = 3.3 X [10.sup.-8]), whereas mother’s age did not (P = 0.49). On the basis of existing knowledge about the mutational mechanisms in sperm and eggs2, the results support the notion that the increase in mutations with parental age manifests itself mostly, maybe entirely, on the paternally inherited chromosome.

Given a particular mutation rate, due to random variation, the number of actual mutations is expected to have a Poisson distribution. After taking Poisson variation into account, with a linear fit (effect = 2.01 mutations per year), father’s age explains 94.0% (90% confidence interval: 80.1%, 100%) (Supplementary Information) of the remaining variation in the observed mutation counts. When an exponential model is fitted (red curve in Fig. 2), the number of paternal and maternal mutations combined is estimated to increase by 3.23% per year. This model explains 96.6% (90% confidence interval: 83.2%, 100%) of the remaining variation. A third model fitted (blue curve in Fig. 2) assumes that the maternal mutation rate is constant at 14.2 and paternal mutations increase exponentially. This explains 97.1% (90% confidence interval: 84.3%, 100%) of the remaining variation and the rate of paternal mutations is estimated to increase by 4.28% per year, which corresponds to doubling every 16.5 years and increasing by 8-fold in 50 years. Seventy-six of the 78 trios have father’s ages between 18 and 40.5, a range in which the differences between the three models are modest. Hence, although it seems that the number of paternal de novo mutations increases at a rate that accelerates with father’s age, more data at the upper agerange are needed to evaluate the nature of the acceleration better.

 

[FIGURE 2 OMITTED]

Validation and the nature of errors

Among the de novo mutations originally called, two were observed twice, both in siblings, one on chromosome 6 and one on chromosome 10. These cases were examined by Sanger sequencing. The mutation on chromosome 6 is not actually de novo as it was seen in the mother also. The one on chromosome 10 was confirmed, that is, it was observed in both siblings, who share the paternal haplotype in this region, but not the parents. This supports the theory that de novo mutations in different sperms of a man are not entirely independent2. Our trios include seven sib-pairs with 921 de novo mutations called. A false de novo mutation call for one sib resulting from a missed call in the parent would also show up in the other sib about 50% of the time. Only one such false positive was detected, indicating that this type of error accounts for a small percentage (2/(920/2) = 0.43%) of the called mutations. To evaluate the overall number of false positives, 111 called de novo mutations were randomly selected for Sanger sequencing. Eleven failed primer design. Six did not produce results of good quality in at least one member of the corresponding trio (Supplementary Information). For the remaining 94 cases, 93 were confirmed as de novo mutations–that is, the mutated allele was observed in the proband but not in the parents. One false positive, in which the putative mutation was not observed in the proband, was identified. The 17 cases that could not be verified are more likely to be located in genomic regions that are more difficult to analyse and hence probably have higher false-positive rates than average. Even so, the overall false-positive rate for the denovo mutation calls cannot be high.

The variance of the number of false positives is as important as the mean. False positives that are Poisson distributed, although adding noise, would not create bias for the effect estimates in either the linear or the exponential models for father’s age, nor would they bias the estimate of the fraction of variance explained after accounting for Poisson variation. In general, they do not create substantial bias for analyses of differences and ratios. However, if the variance of the false positives is higher than the mean, resulting from systematic effects that affect trios differently, such as DNA quality and library construction, it would increase the unexplained variance and reduce the fraction of variance explained by father’s age. The candidates filtered out by criterion (5), if kept, would have introduced false positives of this kind (Supplementary Information). Because father’s age explains such a high fraction of the systematic variance of the currently called de novo mutations, false positives with this property cannot be common. A similar discussion about false negatives (13) is in Supplementary Information.

 

Father’s age and diseases

Consistent with other epidemiological studies14,15, in Iceland, the risk of schizophrenia increases significantly with father’s age at conception (n = 569, P = 2 X [10.sup.-5]). Father’s age is also associated with the risk of ASD. The observed effect is limited to non-familial cases (n = 631, P = 5.4 X [10.sup.-4]), defined as those in which the closest ASD relative is farther than cousins. The epidemiological results, the effect of father’s age on de novo mutation rate shown here, together with other studies that have linked de novo mutations to autism and schizophrenia, including three recent studies of autism through exome sequencing (4-6), all point to the possibility that, as a man ages, the number of de novo mutations in his sperm increases, and the chance that a child would carry a deleterious mutation (not necessarily limited to SNP mutations) that could lead to autism or schizophrenia increases proportionally. However, this model does not indicate that the relationship observed here between mutation rate and father’s age would have been much different if the probands studied were chosen to be all non-ASD/schizophrenic cases instead. For example, assume that autism/schizophrenia is in each case caused by only one de novo mutation. Then autism/schizophrenia cases would on average have more de novo mutations than population samples. The magnitude could be substantial if the distribution of father’s age has a large spread in the population, but then most of the difference would be caused by the cases having older fathers. If we control for the age of the father at the conception of the individual, then this difference in the average number of de novo mutations between control individuals and those with autism/schizophrenia would be reduced to approximately one (Supplementary Information).

Mutations by type and by chromosome

Examination of the 4,933 de novo mutations showed that 73 are exonic, including two stop-gain SNPs and 60 non-synonymous SNPs (Supplementary Table 2). One non-familial schizophrenic proband carries a de novo stop-gain mutation (p.Arg113X) in the neurexin 1 (NRXN1) gene, previously associated with schizophrenia (16-20). One non-familial autistic proband has a stop-gain de novo mutation (p.R546X) in the cullin 3 (CUL3) gene. De novo loss of function mutations in CUL3 have been reported to cause hypertension and electrolyte abnormalities (21). Recently, a separate stop-gain de novo mutation (p.E246X) in CUL3 was reported in an autistic case (5). Another one of our mutations is a non-synonymous variant (p.G900S) two bases from a splice site in the EPH receptor B2 (EPHB2), a gene implicated in the development of the nervous system. A de novo stop-gain mutation (p.Q858X) in this gene has recently been described in another autistic case6. Given the small number of loss of function de novo mutations we and others have reported (approximately 70 genes in the three autism exome scans (4-6)), the overlap is unlikely to be a coincidence. Hence, CUL3 and can be added to the list of genes that are relevant for ASD. Effective genome coverage, computed by discounting regions that have either very low (less than half genome average) or very high (more than three times genome average) local coverage, the latter often a symptom of misaligning reads, was estimated to be 2.63 billion base pairs (Supplementary Information). From that, 4,933 mutations correspond to a germline mutation rate of 1.20 X [10.sup.-8] per nucleotide per generation, falling within the range between 1.1 X [10.sup.-8] and 3.8 X [10.sup.-8] previously reported (3,7,8,22,23). Tables 2 and 3 summarize the nature of the denovo mutations with respect to sequence context. Approximately two-thirds (3,344/4,933 = 67.8%) are transitions. Moreover, there is a clear difference betweenmutation rates at CpG and non-CpG sites. CpG dinucleotides are known to be mutational hotspots in mammals, ostensibly because spontaneous oxidative deamination of methylated cytosines leads to an increase in transition mutations (24). The observed rate of transitions here is 18.2 times that at non-CpG sites, higher than but not inconsistent with previous estimates of 13.3 (ref. 23) and 15.4 (ref. 3). The transversion rate is also higher at CpG sites, 2.55-fold that at non-CpG sites. Most of this increased transversion rate at CpG sites is presumably due to general mutation bias favouring mutations that decrease G1C content. The rate of mutations that change a strong (G:C) base pair to a weak (A:T) one is 2.15-times higher than mutations in the opposite direction. This mutational pressure in the direction of A1T is observed for both transitions (ratio = 2.24) and transversions (ratio = 1.82), and cannot be solely explained by CpG mutations. The father’s age does not seem to affect the ratios between the rates of these different classes of mutations, that is, as a man ages rates of all mutation types increase by a similar factor.

The average number of mutations for each chromosome separately and the effect of father’s age are displayed in Fig. 3. The effect of father’s age is significant (P < 0.05) for 14 of the 22 chromosomes when evaluated individually. The solid line in the figure corresponds to a model in which the linear effect of father’s age is proportional to the mean number of mutations on the chromosome, or that father’s age has a uniform multiplicative effect across the chromosomes. All 22 95% confidence intervals overlap the line, indicating that the results are consistent with the model.

Discussion

The recombination rate is higher for women than men, and children of older mothers have more maternal recombinations that those of young mothers (25). However, men transmit a much higher number of mutations to their children than women. Furthermore, even though our data also show some overdispersion in the number of maternalde novo mutations, it is the age of the father that is the dominant factor in determining the number of de novo mutations in the child. Seeing an association betweenfather’s age and mutation rate is not surprising (2), but the large linear effect of more than two extra mutations per year, or the estimated exponential effect of paternal mutations doubling every 16.5 years, is striking. Even more so is the fraction of the variation it explains, which limits the possible contribution by other factors, such as the environment and the genetic and non-genetic differences between individuals, to mutation rate on a population level. Given the results, it may no longer be meaningful to discuss the average mutation rate in a population without consideration of father’s age. Also, even though factors other than father’s age do not seem to contribute substantially to the mutation rate diversity in our data, it does not mean that hazardous environmental conditions could not cause a meaningful increase inmutation rate. Rather, the results indicate that, to estimate such an effect for a specific incident, it is crucial to take the father’s age into account.

[FIGURE 3 OMITTED]

It is well known that demographic characteristics shape the evolution of the gene pool through the forces of genetic drift, gene flow and natural selection. With the results here, it is now clear that demographic transitions that affect the age at which males reproduce can also have a considerable effect on the rate of genomic change through mutation. There has been a recent transition of Icelanders from a rural agricultural to an urban industrial way of life, which engendered a rapid and sequential drop in the average age of fathers at conception from 34.9 years in 1900 to 27.9 years in 1980, followed by an equally swift climb back to 33.0 years in 2011, primarily owing to the effect of higher education and the increased use of contraception (Fig. 4). On the basis of the fitted linear model, whereas individuals born in 1900 carried on average 73.7 de novo mutations, those born in 1980 carried on average only 59.7 such mutations (a decrease of 19.1%), and the mutational load of individuals born in 2011 has increased by 17.2% to 69.9. Demographic change of this kind and magnitude is not unique to Iceland, and it raises the question of whether the reported increase in ASD diagnosis lately is at least partially due to an increase in the average age of fathers at conception. Also, the observations here are likely to have important implications for the use of genetic variation to estimate divergence times between species or populations, because the mutation rate cannot be treated as a constant scaling factor, but rather must be considered along with the paternal generation interval as a time-dependent variable.

[FIGURE 4 OMITTED]

METHODS SUMMARY

Whole-genome sequence data for this study were generated using the Illumina G[All.sub.x] and HiSeq2000 instruments. The sequencing reads were aligned to the hg18 reference genome with Burrows-Wheeler aligner (BWA) (26) and duplicates were marked with Picard (http://picard.sourceforge.net/). Quality score recalibration, indel realignment and SNP/indel discovery were then performed on each sample separately, using GATK 1.2 (ref. 27). Likelihoods presented are based on the normalized Phred-scaled likelihoods that are calculated by the GATK variant calling. Statistical analysis was performed in part using the R statistical package. Estimates and confidence intervals for the fraction of variance explained after accounting for Poisson variation were calculated using Monte Carlo simulations (Supplementary Information). Variants were annotated using SNP effect predictor (snpEff2.0.5, database hg36.5) and GATK 1.4-9-g1f1233b with only the highest-impact effect (P. Cingolani, ‘snpEff:Variant effect prediction’, http://snpeff.sourceforge.net, 2012). More details are in Supplementary Information.

doi: 10.1038/nature11396

Acknowledgements This research was partly funded by The National Institutes of Health grant MH071425 (K.S.); the European Community’s Seventh Framework Programme, PsychCNVs project, grant agreement HEALTH-F2-2009-223423, and NextGene project, grant agreement IAPP-MC-251592; The European Community IMI grant EU-AIMS, grant agreement 115300.

Received 28 February; accepted 4 July 2012.

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Supplementary Information is available in the online version of the paper.

Author Contributions A.K. and K.S. planned and directed the research. A.K. wrote the first draft and together with K.S., S.B., P.S., A.H.and U.T. wrote the final version. O.T.M. and U.T. oversaw the sequencing and laboratory work. G. Masson, G. Magnusson and G.S. processed the raw sequencing data. A.K. and M.L.F. analysed the data, with W.S.W.W., H.H., G.B.W., S.S., G.T. and D.F.G. providing assistance. P.S. and S.A.G. performed functional annotations. S.B. analysed the mutations with respect to sequence content. A.S., Aslaug J. and Adalbjorg J. did the Sanger sequencing. A.H. investigated the contribution of demographics.

Author Information Reprints and permissions information is available at www.nature.com/reprints. The authors declare competing financial interests: details are available in the online version of the paper. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to A.K. (kong@decode.is) or K.S. (kari.stefansson@decode.is).

Augustine Kong [1], Michael L. Frigge [1], Gisli Masson [1], Soren Besenbacher [1’2], Patrick Sulem [1], Gisli Magnusson [1], Sigurjon A. Gudjonsson [1], Asgeir Sigurdsson [1], Aslaug Jonasdottir [1], Adalbjorg Jonasdottir [1], Wendy S. W. Wong [3], Gunnar Sigurdsson [1], G. Bragi Walters [1], Stacy Steinberg [1], Hannes Helgason [1], Gudmar Thorleifsson [1], Daniel F. Gudbjartsson [1], Agnar Helgason [1,4], Olafur Th. Magnusson [1], Unnur Thorsteinsdottir [1,5] & Kari Stefansson [1,5] [1] deCODE Genetics, Sturlugata 8, 101 Reykjavik, Iceland. [2] Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark. [3] Illumina Cambridge Ltd, Chesterford Research Park, Little Chesterford, Essex CB10 1XL, UK. [4] University of Iceland, 101 Reykjavik, Iceland. 5Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland.

Table 1 | De novo mutations observed with parental origin assigned

 

Number of de novo mutations in proband

 

Father’s   Mother’s    Paternal     Maternal

age (yr)   age (yr)   chromosome   chromosome   Combined

 

Trio 1     21.8       19.3       39           9            48

Trio 2     22.7       19.8       43           10           53

Trio 3     25.0       22.1       51           11           62

Trio 4     36.2       32.2       53           26           79

Trio 5     40.0       39.1       91           15           106

Mean       29.1       26.5       55.4         14.2         69.6

s.d.       8.4        8.8        20.7         7.0          23.5

Variance   70.2       77.0       428.8        48.7         555.3

 

Table 2 | Germline mutation rates at CpG and non-CpG sites

 

Rate per base per

Type of mutation            n         generation

 

Transition at non-CpG     2,489   6.18 X [10.sup.-9]

Transition at CpG          855    1.12 X [10.sup.-7]

Transversion at non-CpG   1,516   3.76 X [10.sup.-9]

Transversion at CpG         73    9.59 X [10.sup.-9]

All                       4,933   1.20 X [10.sup.-8]

 

Mutation rates are per generation per base. For non-CpG sites, the

effective number of bases examined is taken as 2.583 billion, whereas

for CpG sites the number is 48.8 million. These numbers take into

account the variation of local coverage in sequencing (Supplementary

Information).

 

Table 3 | Strong-to-weak and weak-to-strong mutation rates

 

Mutation type    S[right arrow]W(n)rate       W[right arrow]S(n)rate

 

Transition      (2,025)1.21 X [10.sup.-8]   (1,319)5.42 X [10.sup.-9]

Transversion    (446) 2.67 X [10.sup.-9]    (358) 1.47 X [10.sup.-9]

All             (2,471)1.48X [10.sup.-8]    (1,677) 6.89 X [10.sup.-9]

 

S[right arrow]W rate/

Mutation type      W[right arrow]rate

 

Transition                2.24

Transversion              1.82

All                       2.15

 

n denotes observed mutation counts, and mutation rates are calculated

per generation per base.For strong(S; G:C) to weak (W; A:T), the

effective number of sites examined is taken as 1.071 billion,and for

weak to strong the number is 1.56 billion.

Kong, Augustine^Frigge, Michael L.^Masson, Gisli^Besenbacher, Soren^Sulem, Patrick^Magnusson, Gisli^Gudjonsson, Sigurjon A.^Sigurdsson, Asgeir^Jonasdottir, Aslaug^Jonasdottir, Adalbjorg^Wong, Wendy S.W.^Sigurdsson, Gunnar^Walters, G. Bragi^Steinberg, Stacy^Helgason, Hannes^Thorleifsson, Gudmar^Gudbjartsson, Daniel F.^Helgason, Agnar^Magnusson, Olafur Th.^Thorsteinsdottir, Unnur^Stefansson, Kari

Source Citation   (MLA 7th Edition)

Besenbacher, Soren, et al. “Rate of de novo mutations and the importance of father’s age to disease risk.” Nature 488.7412 (2012): 471+. Academic OneFile. Web. 5 Mar. 2014.

Document URL
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Genetic Analysis of Individuals with Autism Finds Gene Deletions
http://www.mountsinai.org/about-us/newsroom/press-releases/genetic-analysis-of-individuals-with-autism-finds-gene-deletions
Deletions may be linked to miswiring of brain neurons.

NEW YORK

 – October 3, 2013 /Press Release/  –– 

Using powerful genetic sequencing technology, a team of investigators, led by researchers at the Icahn School of Medicine at Mount Sinai, scanned the genome of hundreds of individuals, and discovered those diagnosed with autism spectrum disorder (ASD) were more likely to have gene deletions than were people without the disorder.  That means those individuals — seven percent of the study group — had one copy of one or more genes when they should have had two.

 

The scientists further report, in the American Journal of Human Genetics, that their analysis suggests the deletions may result in the miswiring and altered activity of brain neurons.

“This is the first finding that small deletions impacting one or two genes appear to be common in autism,  and that these deletions contribute to risk of development of the disorder,” says the study’s lead investigator, Joseph D. Buxbaum, PhD, Professor of PsychiatryGenetics and Genomic Sciences and Neuroscience at the Icahn School of Medicine at Mount Sinai.”This conclusion needs to be expanded in other independent samples of ASD so that we can truly understand how the risk manifests,” he says.

That process is now ongoing, Dr. Buxbaum adds. The Autism Sequencing Consortium, a group of over 25 institutions, was awarded a $7 million grant from the National Institutes of Health to continue analyzing the genomes of thousands of ASD individuals at Mount Sinai.

 

First look for missing genes in autistic population

Autism, which affects about one percent of the population, is a developmental disorder thought to be caused by a complex interplay between genetic and environmental factors. Although the disorder is highly heritable, the majority of autism cases cannot be attributed to known inherited causes, Dr. Buxbaum says.

 

While research has indicated that there might be as many as 1,000 genes or genomic regions that contribute to ASD, most studies have looked for either single point mutations—a change in a single letter of DNA on a gene—or for large areas of the genome, encompassing many genes, that is altered.

 

In this study, the researchers looked for small copy number variation—deletion or duplication of genes—between ASD individuals and a “control” population without the disorder.

 

To conduct the study, they used exome sequencing to look at all 22,000 human genes in the sample set, and analyzed that data using the eXome Hidden Markov Model (XHMM) program. Together, the tools are the first that can find single gene-sized deletions or additions in the genome.

“This gives us the power, for the first time, to run one test from a blood sample and compare it to a reference genome to search for mutations and small copy number variation in patients,” Dr. Buxbaum says.

 

They applied this method to analyze a database consisting of 431 ASD cases and 379 matched controls, totaling 811 individuals. They found 803 gene deletions in the ASD group and 583 deletions in the control group, and the ASD population had a greater likelihood of having multiple small deletions.

 

Gene deletions not due to genetic inheritance

“It is now known that imperfect gene copy number is one of the major sources of variability between people. One of the reasons we are different from each other is because of gene additions or deletions which are often inherited,” he says. “But of the extra deletions we see in ASD not all are due to genetic inheritance. Some occur during the development of the egg or sperm, and deletions that develop in this way tend to be associated with the disorder.”

The researchers then examined the deletions they found in the autistic group and found that a significant proportion of them related to autophagy, a key process that keeps cells healthy by replacing membranes and organelles.

 

“There is a good reason to believe that autophagy is really important for brain development because the brain produces many more synapses than it needs, and the excess needs to be pruned back,” Dr. Buxbaum says. “Too many, or too few, synapses have the same effect of not making communication work very well. It could mean that some synaptic connections come in too late and may not solidify properly.”

 

The researchers believe the findings will have clinical significance. “Key copy number variations—those that consistently appear in an autistic population—can impact genetic testing,” Dr. Buxbaum says.

 

The research was supported by the National Institute of Mental Health, National Institutes of Health (grants MH089025, MH097849, and MH100233), and the Seaver Foundation.

Co-authors include Christopher S. Poultney, PhD,  Arthur P. Goldberg, PhD, Elodie Drapeau, PhD, Yan Kou, MSc, Hala Harony-Nicolas, PhD, Yuji Kajiwara, PhD, Silvia De Rubeis, PhD, Simon Durand, BS, Avi Ma’ayan, PhD, and Menachem Fromer, PhD from the Icahn School of Medicine at Mount Sinai; Christine Stevens, MS, Aarno Palotie, MD, PhD, and Mark J. Daly, PhD, from the Broad Institute of MIT and Harvard, and Karola Rehnström, PhD, from the University of Helsinki, Finland.

 

About the Mount Sinai Health System
The Mount Sinai Health System is an integrated health system committed to providing distinguished care, conducting transformative research, and advancing biomedical education. Structured around seven member hospital campuses and a single medical school, the Health System has an extensive ambulatory network and a range of inpatient and outpatient services—from community-based facilities to tertiary and quaternary care.

The System includes approximately 6600 primary and specialty care physicians, 12-minority-owned free-standing ambulatory surgery centers, over 45 ambulatory practices throughout the five boroughs of New York City, Westchester, and Long Island, as well as 31 affiliated community health centers. Physicians are affiliated with the Icahn School of Medicine at Mount Sinai, which is ranked among the top 20 medical schools both in National Institutes of Health funding and by U.S. News & World Report.

 

This is an abstract from the article that the above press release is referencing.
http://www.cell.com/AJHG/retrieve/pii/S000292971300414X

Identification of Small Exonic CNV from Whole-Exome Sequence Data and Application to Autism Spectrum Disorder

Christopher S. Poultney12Arthur P. Goldberg123Elodie Drapeau12Yan Kou14Hala Harony-Nicolas12Yuji Kajiwara12Silvia De Rubeis12Simon Durand12Christine Stevens5Karola Rehnström67Aarno Palotie56Mark J. Daly58Avi Ma’ayan4Menachem Fromer29 and Joseph D. Buxbaum123910

1 Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
2 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
3 Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
4 Department of Pharmacology and Systems Therapeutics and Systems Biology Center New York (SBCNY), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
5 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
6 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290 Helsinki, Finland
7 Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
8 Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
9 Department of Genetics and Genomic Sciences, Department of Neuroscience, and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
10 Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

Abstract

Copy number variation (CNV) is an important determinant of human diversity and plays important roles in susceptibility to disease. Most studies of CNV carried out to date have made use of chromosome microarray and have had a lower size limit for detection of about 30 kilobases (kb). With the emergence of whole-exome sequencing studies, we asked whether such data could be used to reliably call rare exonic CNV in the size range of 1–30 kilobases (kb), making use of the eXome Hidden Markov Model (XHMM) program. By using both transmission information and validation by molecular methods, we confirmed that small CNV encompassing as few as three exons can be reliably called from whole-exome data. We applied this approach to an autism case-control sample (n = 811, mean per-target read depth = 161) and observed a significant increase in the burden of rare (MAF ≤1%) 1–30 kb CNV, 1–30 kb deletions, and 1–10 kb deletions in ASD. CNV in the 1–30 kb range frequently hit just a single gene, and we were therefore able to carry out enrichment and pathway analyses, where we observed enrichment for disruption of genes in cytoskeletal and autophagy pathways in ASD. In summary, our results showed that XHMM provided an effective means to assess small exonic CNV from whole-exome data, indicated that rare 1–30 kb exonic deletions could contribute to risk in up to 7% of individuals with ASD, and implicated a candidate pathway in developmental delay syndromes.

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