Me: This will be the first in a series of posts I wanted to do about India. In a country of over 1 billion people and which will eventually become the most populous country in the world in just 2 decades (according to the demographic analysts) India seemed an appropriate place to find giants and people with extreme bodies.

This was a story I found from the website for the newspaper The Daily UK. Since I do have pictures posted, I understand there might be a few copyright issues with that which I will deal with if I am informed of any issues that comes up. 

From this source HERE, they say that Sharad is 2.18 m and that Sanjot is 1.90 m

From the The Daily UK website HERE the article is posted below.

Meet the Kulkarnis: India’s tallest family with a combined height of 26ft hope to set a new world record

• Family is so tall they never use public transport and ride scooters instead
• Sharad Kulkarni, 52, is 7ft 1.5ins tall and his wife Sanjot, 46, is 6ft 2.6ins tall
• Their daughters are both over 6ft and want to be models

By KRISHNA KUMAR

PUBLISHED: 21:56 GMT, 26 June 2012 | UPDATED: 23:31 GMT, 26 June 2012

The Kulkarnis from Pune tower above their countrymen. Sharad Kulkarni, 52, who works in the State Bank of India, stands 7ft 1.5in tall; his wife Sanjot, 46, is 6ft 2.6in and their daughters, Mruga, 22, and Sanya, 16, are 6ft 4in and 6ft 1in tall respectively.

Their combined height is a staggering 26ft, almost. India’s tallest family is likely to set a new Guinness record for being the world’s tallest.

Mr and Mrs Kulkarni were crowned India’s tallest couple by the Limca Book of Records a year after they married in 1988.

While the recognition did make them feel good, it came after both faced years of teasing and ridicule as they grew up. ‘In three years of college life, I was all alone.

‘I didn’t have any friends because I was so tall. It was only after I started getting publicity that people began interacting with me,’ Sharad said. When he hit 7ft as a teenager, he ploughed his energies into sports and ended up playing basketball for the country.

But Sanjot struggled to fit in her native village. The pair began to accept early on in their teens that marriage might be difficult.

‘I was a basketball player and travelled the length and breadth of India for tournaments. But I was never able to find a girl who came close to my height.

The tallest I found was around 5ft 10 in. I had even decided not to marry and was planning to adopt a child. But, as chance would have it, one of Sanjot’s relatives saw me,’ Sharad said.

It was actually Sanjot’s grandmother who spotted him walking down a street in Mumbai one evening and approached him. Sharad said: ‘This lady came up to me and asked if I was single and if I would meet her granddaughter who was over 6ft tall. I didn’t believe her and refused, but my friends persuaded me to take her number.’

A few weeks later, Sharad’s parents called the number and a meeting was arranged for the couple. ‘When I met Sanjot, I was happy. And I knew we would be happy together,’ Sharad said. But he added that even now, the family faced problems, albeit of a different kind.

They can’t use public transport such as trains and buses, and even four-wheelers are a nono because Sharad can’t accommodate his knees inside.

The family prefers scooters for road travel and when flying is necessary, they ask for a front seat or the emergency exit row. Being tall also means the four have to cope with people staring and talking behind their backs.

‘It doesn’t bother me and Sanjot anymore but our daughters get perturbed,’ Sharad said. The Kulkarnis have wardrobes full of custom-made clothes and shoes.

And their house has been adapted to meet their needs – they changed the door frames from 6ft to 8ft high and have customised the furniture. Mruga and Sanya plan to put their height to good use and are thinking of foraying in modelling.

‘We love being tall. I hear girls moan every day about their short height but we’re so content. We are studying right now but are also trying to build a portfolio.

‘We hope our height will help us get a long career as models,’ Mruga said, adding that ‘husbands are a long way away yet’.

The Guinness Book of Records does not currently have a tallest family category, but would consider it

Read more: http://www.dailymail.co.uk/news/article-2165196/Meet-Kulkarnis-Indias-tallest-family-combined-height-26ft-hope-set-new-world-record.html#ixzz29chtn4FV
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Me: I just found this website for the journal “Growth, Genetics, & Hormones” located HERE today and I am very excited to see what it can offer. I will be adding this to the resource page in the next few days. 

This new website will be critical in helping at least me understand many of the genes and hormones that are documented in the “Gene Database” and Protein and Hormone Pathway” section of the website since my knowledge on how each of them are connected to each other is still very elementary. What is really nice about the articles is that they are not too technical that it makes it impossible for the average educated laymen to read but also technical enough to give the ordinary reader possible insights and ideas on how to apply the knowledge.

If you are interested in helping out with the website and cause, I would suggest that you start from this website of journal articles and studies first. It seems to be very helpful in our research.

Me: I found this rather short article very insightful on the deeper hormonal and genetic influences that results in the variation of height. It is a very important read.

Genetics of Stature

« Back to Volume 24, Issue 2, November 2008 – Table of Contents

Adult height is primarily (approximately 80% to 90%) determined by hereditary factors. Socioeconomic status, nutrition, and disease influence only a relatively small proportion of attained stature. It has long been suspected that there are a multitude of genes that impact upon this polygenic trait, with each gene exerting an additive but only very limited effect. From genome-wide association studies employing single nucleotide polymorphism (SNP) analyses in approximately 80,000 individuals of European ancestry (UK, Scandinavia, Holland, Iceland), these 3 investigative groups have identified more than 30 chromosomal sites and the potential genes that appear to be partially involved in the regulation of adult stature in humans (Table). Gudbjartsson et al divided the candidate genes into 3 functional groups—those associated with skeletal development (eg, BMP2, BMP6), those that encode zinc-dependent metalloproteinases (ADAMTS10) and glycoproteins (eg, FBN1) that affect cartilage composition, and those that are involved with the processes of chromosome segregation and mitosis (eg, CDK6, HMGA2). The gene most frequently associated with stature in all 3 studies was ZBTB38. This zinc-finger protein binds methylated DNA—specifically the methylated allele of the differentially methylated region of H19/IGF2.¹ This is the site at which epigenetic errors of imprinting result in either the Beckwith-Wiedemann syndrome (OMIM 130650) of somatic overgrowth or the growth retardation syndrome of Russell-Silver (OMIM 180860).² ZBTB38 represses transcription of methylated regions. Thus, it is interesting to speculate that ZBTB38 might affect adult stature through regulation of the production of insulin-like growth factor (IGF)-II, perhaps during in utero development when IGF-II is known to be one of the determinants of fetal growth. Independent of its effect on methylated DNA, ZBTB38 also regulates transcription of TH, the gene encoding tyrosine hydroxylase, the rate-limiting step in catecholamine synthesis. Other commonly identified gene candidates were HMGA2 encoding a chromatin architectural factor and CDK6 encoding a cyclin dependent kinase regulator of the cell cycle.

While each of these candidate genes has only a small effect upon adult height (estimated 0.4 cm), collectively they can exert significant influence and account for only approximately 4% of adult stature. The more “tall” alleles one has, the taller the individual (Figure). In the study of Weedon et al, there was a 5 cm difference in adult stature between subjects with 17 or fewer “tall” alleles compared to those with 27 or more.

Gudbjartsson DF, Walters GB, Thorleifsson G, et al. Many sequence variants affecting diversity of adult human height. Nat Genet. 2008;40:609-15.

Lettre G, Jackson AU, Gieger C, et al. Identification of 10 loci associated with height highlights new biological pathways in human growth. Nat Genet. 2008;40:489-90.

Weedon MN, Lango H, Lindgren CM, et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet. 2008;40:573-83.

First Editor’s Comment

These reports are of great interest as they dramatically illustrate just how many genes must be involved in the determination of adult stature. They also illustrate the quantitative problem that the clinician will face in identifying the “cause” of genetic short stature in a specific patient. However, it was difficult to critically examine the data because some of it was derived by meta-analysis of previously published reports. Thus, it was unclear whether or not there may have been some overlap between analytical data utilized in the 3 reports. The reports are also difficult to interpret because the investigators employed different probes for similar or related SNP sites. For example, ZBTB38 was identified as SNP rs724016 in the report of Lettre et al, as SNP rs6440003 in the report of Weedon et al, and as SNP rs6763931 in the report of Gudbjartsson et al. [A brief expository review of genome-wide association studies and SNPs has been written by Christensen and Murray.³]

Allen W. Root, MD

Second Editor’s Comment

Fisher proposed in 1918 that many genetic factors, each having an individually small effect, explain the heritability of height.⁴ Much attention has been devoted since that time to identifying these factors. For instance, numerous genes have been identified that harbor mutations responsible for the osteochondrodysplasias and other syndromes associated with severe short stature, but in general these genes do not seem to influence the normal continuous variation in stature. Although linkage studies have elucidated chromosomal regions that affect height variation, they have not identified specific gene loci that influence height in the general population. It has not been until the recent application of genome-wide association (GWA) studies that significant headway has been made. This approach takes advantage of high-throughput analysis of single nucleotide polymorphisms (SNPs) identified through the so called HapMap project, a growing number of patient groups for whom DNA is available for analysis and advances in computational methods that enable such analysis and permit datasets to be combined. Indeed, one of the first GWA investigations of height was reviewed in GGH.⁵ This reviewed study has now been expanded substantially and joined by 3 other large GWA studies as reported in the May 2008 Nature Genetics. The new investigations have utilized more rigorous multi-stage experimental designs to analyze hundreds of thousands of SNP markers in ~63,000 individuals measured for adult height.

The report by Weedon et al identified 20 genetic variants which, in the aggregate, account for ~3% of height variation in adults of European ancestry. The identified SNP markers do not influence height per se, but they implicate genes within which or nearby to which they reside. One can envision how most of the candidate genes implicated in this manner could influence growth as they encompass growth factors and their receptors, proteins that interact with or alter the extracellular milieu of growth factors and proteins that modulate intracellular signaling or are linked to cell cycle regulation or cancer. Most notable here are Indian hedgehog (IHH), Hedgehog interacting protein (HHIP) and Patched 1 (PTCH1), which belong to the Hedgehog pathway, growth and differentiation factor 5 (GDF5), suppressor of cytokine signaling 2 (SOCS2) and cyclin-dependent kinase-6 (CDK6). The previous association with a marker near the high mobility group-A2 (HMGA2) gene locus was confirmed.

The report by Lettre et al identified 10 loci associated with height variation also in adults of European ancestry, 4 of which were the same as in the Weedon report including HHIP. These authors emphasized that 3 of the candidate genes—HMGA2, the histone methyltransferase DOT1L and the methyl-DNA-binding transcriptional repressor gene ZBT38—are involved in chromatin remodeling. They note that the 3’ untranslated region of HMGA2 contains the largest number of let-7 microRNA binding sites and that 3 of the other implicated genes, CDK6, DOT1L and LIN28B, a gene upregulated in hepatocellular carcinoma, are considered targets of let-7. MicroRNAs, such as let-7, are small, nontranslated RNAs that down regulate expression of target genes.

The report by Gudbjartsson et al detected 27 genomic regions in which SNP variants were associated with adult height. Their data came from individuals with Icelandic, Dutch, European- and African-American ancestries and results accounted for 3.7% variation in adult height. Several of the implicated genes were the same as in the other 2 reports, but a few additional genes were indentified including BMP2, BMP6 and the TGF-β and BMP inhibitor, Noggin (NOG).

In contrast to the GGH abstract⁵ describing a single SNP association with adult height published in May 2008, these new reports identify 54 gene loci that influence variation in height in adults primarily of European descent. As noted in the accompanying editorial by Visscher,⁶ it is reassuring that SNPs previously observed to associate with height were confirmed, SNPs in 3 genes were found associated with height in all 3 studies, and 7 genes were implicated in 2 of the 3 investigations. It is not surprising that variation in genes involving growth factors or modulation of growth factor signaling pathways influence height. More intriguing and novel is the implication of genes involved in chromatin remodeling and in microRNA regulation of gene expression. The papers illustrated the power of GWA studies and also the necessity of very large sample sizes creating consortia of research groups and even consortia of consortia as stated by Visscher.⁶

William A. Horton, MD

Me: This is the biggest collaborative project geneticists and scientists have ever done looking at what are the hundreds of genes which do affect height which is a polygenic trait. For me, this article is a VERY BIG DEAL. Right here is the sum of the hard work done by hundreds of researchers who have all been studying to find out the genes that cause the variation in human height.

Note: If you really are serious about finding a solution I would say it is critical to look through the supplementary material which is cited at the bottom of the article. but you can click HERE for a copy of the .DOC file.

From the Nature website source link HERE.

From PubMed website source link HERE. for the actual link that I got the Full Text from click HERE.

Nature. 2010 Oct 14;467(7317):832-8. Epub 2010 Sep 29.

Hundreds of variants clustered in genomic loci and biological pathways affect human height.

Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

In Stage 1 of our study, we performed a meta-analysis of GWA data from 46 studies, comprising 133,653 individuals of recent European ancestry, to identify common genetic variation associated with adult height. To enable meta-analysis of studies across different genotyping platforms, we performed imputation of 2,834,208 single nucleotide polymorphisms (SNPs) present in the HapMap Phase 2 European-American reference panel⁴. After applying quality control filters, each individual study tested the association of adult height with each SNP using an additive model (Supplementary Methods). The individual study statistics were corrected using the genomic control (GC) method^5,6 and then combined in a fixed effects based meta-analysis. We then applied a second GC correction on the meta-analysis statistics, although this approach may be overly conservative when there are many real signals of association (Supplementary Methods). We detected 207 loci (defined as 1Mb on either side of the most strongly associated SNP) as potentially associated with adult height (P<5×10^-6).

To identify loci robustly associated with adult height, we took forward at least one SNP (Supplementary Methods) from each of the 207 loci reaching P<5×10^-6 into an additional 50,074 samples (Stage 2) that became available after completion of our initial meta-analysis. In the joint analysis of our Stage 1 and Stage 2 studies, SNPs representing 180 loci reached genome-wide significance (P<5×10^-8;Supplementary Figures 1 and 2, Supplementary Table 1). Additional tests, including genotyping of a randomly-selected subset of 33 SNPs in an independent sample of individuals from the 5^th-10^th and 90^th-95^th percentiles of the height distribution (N=3,190)⁷, provided further validation of our results, with all but two SNPs showing consistent direction of effect (sign test P<7×10^-8) (Supplementary Methods, Supplementary Table 2).

Genome wide association studies can be susceptible to false positive associations from population stratification⁷. We therefore performed a family-based analysis, which is immune to population stratification in 7,336 individuals from two cohorts with pedigree information. Alleles representing 150 of the 180 genome-wide significant loci were associated in the expected direction (sign test P<6×10^-20;Supplementary Table 3). The estimated effects on height were essentially identical in the overall meta-analysis and the family-based sample. Together with several other lines of evidence (Supplementary Methods), this indicates that stratification is not substantially inflating the test statistics in our meta-analysis.

Common genetic variants have typically explained only a small proportion of the heritable component of phenotypic variation⁸. This is particularly true for height, where >80% of the variation within a given population is estimated to be attributable to additive genetic factors⁹, but over 40 previously published variants explain <5% of the variance^10–17. One possible explanation is that many common variants of small effects contribute to phenotypic variation, and current GWA studies remain underpowered to detect the majority of common variants. Using five studies not included in Stage 1, we found that the 180 associated SNPs explained on average 10.5% (range 7.9-11.2%) of the variance in adult height (Supplementary Methods). Including SNPs associated with height at lower significance levels (0.05>P>5×10^-8) increased the variance explained to 13.3% (range 9.7-16.8%) (Figure 1a)¹⁸. In addition, we found no evidence that non-additive effects including gene-gene interaction would increase the proportion of the phenotypic variance explained (Supplementary Methods, Supplementary Tables 5 and 6).

Figure 1

Phenotypic variance explained by common variants

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

As a separate approach, we used a recently developed method¹⁹ to estimate the total number of independent height-associated variants with effect sizes similar to the ones identified. We obtained this estimate using the distribution of effect sizes observed in Stage 2 and the power to detect an association in Stage 1, given these effect sizes (Supplementary Methods). The cumulative distribution of height loci, including those we identified and others as yet undetected, is shown in Figure 1b. We estimate that there are 697 loci (95% confidence interval (CI): 483-1040) with effects equal or greater than those identified, which together would explain approximately 15.7% of the phenotypic variation in height or 19.6% (95% CI: 16.2-25.6) of height heritability (Supplementary Table 4). We estimated that a sample size of 500,000 would detect 99.6% of these loci at P<5×10^-8. This figure does not account for variants that have effect sizes smaller than those observed in the current study and, therefore, underestimates the contribution of undiscovered common loci to phenotypic variation.

A further possible source of missing heritability is allelic heterogeneity – the presence of multiple, independent variants influencing a trait at the same locus. We performed genome-wide conditional analyses in a subset of Stage 1 studies, including a total of 106,336 individuals. Each study repeated the primary GWA analysis but additionally adjusted for SNPs representing the 180 loci associated atP<5×10^-6 (Supplementary Methods). We then meta-analysed these studies in the same way as for the primary GWA study meta-analysis. Nineteen SNPs within the 180 loci were associated with height atP<3.3×10^-7 (a Bonferroni-corrected significance threshold calculated from the ~15% of the genome covered by the conditioned 2Mb loci; Supplementary Methods, Table 1, Figure 2, Supplementary Figure 3). The distances of the second signals to the lead SNPs suggested that both are likely to be affecting the same gene, rather than being coincidentally in close proximity. At 17 of 17 loci (excluding two contiguous loci in the HMGA1 region), the second signal occurred within 500kb, rather than between 500kb and 1 Mb, of this lead SNP (binomial test P=2×10^-5). Further analyses of allelic heterogeneity may identify additional variants that increase the proportion of variance explained. For example, within the 180 2Mb loci, a total of 45 independent SNPs reached P<1×10^-5 when we would expect <2 by chance.

Table 1

Secondary signals at associated loci after conditional analysis

Figure 2

Example of a locus with a secondary signal before (a) and after (b) conditioning

Whilst GWA studies have identified many variants robustly associated with common human diseases and traits, the biological significance of these variants, and the genes on which they act, is often unclear. We first tested the overlap between the 180 height-associated variants and two types of putatively functional variants, nonsynonymous (ns) SNPs and cis-eQTLs (variants strongly associated with expression of nearby genes). Height variants were 2.4-fold more likely to overlap with cis-eQTLs in lymphocytes than expected by chance (47 variants: P=4.7×10^-11) (Supplementary Table 7) and 1.7-fold more likely to be closely correlated (r²≥0.8 in HapMap CEU) with nsSNPs (24 variants P=0.004) (Supplementary Methods, Supplementary Table 8). Although the presence of a correlated eQTL or nsSNP at an individual locus does not establish the causality of any particular variant, this enrichment shows that common functional variants contribute to the causal variants at height-associated loci. We also noted five loci where the height associated variant was strongly correlated (r²>0.8) with variants associated with other traits and diseases (P<5×10^-8), including bone mineral density, rheumatoid arthritis, type 1 diabetes, psoriasis and obesity, suggesting that these variants have pleiotropic effects on human phenotypes (Supplementary Methods; Supplementary Table 9).

We next addressed the extent to which height variants cluster near biologically relevant genes; specifically, genes mutated in human syndromes characterized by abnormal skeletal growth. We limited this analysis to the 652 genes occurring within the recombination hotspot-bounded regions surrounding each of the 180 index SNPs. We showed that the 180 loci associated with variation in normal height contained 21 of 241 genes (8.7%) found to underlie such syndromes (Supplementary Table 10), compared to a median of 8 (range 1-19) genes identified in 1,000 matched control sets of regions (P<0.001: 0 observations of 21 or more skeletal growth genes among 1,000 sets of matched SNPs). In 13 of these 21 loci the closest gene to the most associated height SNP in the region is the growth disorder gene, and in 9 of these cases, the most strongly associated height SNP is located within the growth disorder gene itself (Supplementary Methods, Supplementary Table 11). These results suggest that GWA studies may provide more clues about the identity of the functional genes at each locus than previously suspected.

We also investigated whether significant and relevant biological connections exist between the genes within the 180 loci, using two different computational approaches. We used the GRAIL text-mining algorithm to search for connectivity between genes near the associated SNPs, based on existing literature²⁰. Of the 180 loci, 42 contained genes that were connected by existing literature to genes in the other associated loci (the pair of connected genes appear in articles that share scientific terms more often than expected at P<0.01). For comparison, when we used GRAIL to score 1,000 sets of 180 SNPs not associated with height (but matched for number of nearby genes, gene proximity, and allele frequency), we only observed 16 sets with 42 or more loci with a connectivity P<0.01, thus providing strong statistical evidence that the height loci are functionally related (P=0.016) (Figure 3a). For the 42 regions with GRAIL connectivity P<0.01, the implicated genes and SNPs are highlighted in Figure 3b. The most strongly connected genes include those in the Hedgehog, TGF-beta, and growth hormone pathways.

Figure 3

Loci associated with height contain genes related to each other

As a second approach to find biological connections, we applied a novel implementation of gene set enrichment analysis (GSEA) (Meta-Analysis Gene-set Enrichment of variaNT Associations, MAGENTA²¹) to perform pathway analysis (Supplementary Methods). This analysis revealed 17 different biological pathways and 14 molecular functions nominally enriched (P<0.05) for associated genes, many of which lie within the validated height loci. These gene-sets include previously reported^11,13 (e.g. Hedgehog signaling) and novel (e.g. TGF-beta signaling, histones, and growth and development-related) pathways and molecular functions (Supplementary Table 12). Several SNPs near genes in these pathways narrowly missed genome-wide significance, suggesting that these pathways likely contain additional associated variants. These results provide complementary evidence for some of the genes and pathways highlighted in the GRAIL analysis. For instance, genes such as TGFB2 andLTBP1-3 highlight a role for the TGF-beta signaling pathway in regulating human height, consistent with the implication of this pathway in Marfan syndrome²².

Finally, to examine the evidence for the potential involvement of specific genes at individual loci, we aggregated evidence from our data (eQTLs, proximity to the associated variant, pathway-based analyses), and human and mouse genetic databases (Supplementary Table 13). Of 32 genes with highly correlated (r²>0.8) nsSNPs, several are newly identified strong candidates for playing a role in human growth. Some are in pathways enriched in our study (such as ECM2, implicated in extracellular matrix), while others have similar functions to known growth-related genes, including FGFR4 (FGFR3 underlies several classic skeletal dysplasias²³) and STAT2 (STAT5B mutations cause growth defects in humans²⁴). Interestingly, Fgfr4-/- Fgfr3-/- mice show severe growth retardation not seen in either single mutant²⁵, suggesting that the FGFR4 variant might modify FGFR3-mediated skeletal dysplasias. Other genes at associated loci, such as NPPC and NPR3 (encoding the C-type natriuretic peptide and its receptor), influence skeletal growth in mice and will likely also influence human growth¹⁷. Many of the remaining 180 loci have no genes with obvious connections to growth biology, but at some our data provide modest supporting evidence for particular genes, including C3orf63, PML, CCDC91, ZNFX1, ID4, RYBP, SEPT2,ANKRD13B, FOLH1, LRRC37B, MFAP2, SLBP, SOCS5, and ZBTB24 (Supplementary Table 13).

We have identified >100 novel loci that influence the classic polygenic trait of normal variation in human height, bringing the total to 180. Our results have potential general implications for genetic studies of complex traits. We show that loci identified by GWA studies highlight relevant genes: the 180 loci associated with height are non-randomly clustered within biologically relevant pathways and are enriched for genes that are involved in growth-related processes, that underlie syndromes of abnormal skeletal growth, and that are directly relevant to growth-modulating therapies (GH1, IGF1R, CYP19A1,ESR1). The large number of loci with clearly relevant genes suggests that the remaining loci could provide potential clues to important and novel biology.

We provide the strongest evidence yet that the causal gene will often be located near the most strongly associated DNA sequence variant. At the 21 loci containing a known growth disorder gene, that gene was on average 81 kb from the associated variant, and in over half of the loci it was the closest gene to the associated variant. Despite recent doubts about the benefits of GWA studies²⁶, this finding suggests that GWA studies are useful mapping tools to highlight genes that merit further study. The presence of multiple variants within associated loci could help localize the relevant genes within these loci.

By increasing our sample size to >100,000 individuals, we identified common variants that account for approximately 10% of phenotypic variation. Although larger than predicted by some models²⁶, this figure suggests that GWA studies, as currently implemented, will not explain a majority of the estimated 80% contribution of genetic factors to variation in height. This conclusion supports the idea that biological insights, rather than predictive power, will be the main outcome of this initial wave of GWA studies, and that new approaches, which could include sequencing studies or GWA studies targeting variants of lower frequency, will be needed to account for more of the “missing” heritability. Our finding that many loci exhibit allelic heterogeneity suggests that many as yet unidentified causal variants, including common variants, will map to the loci already identified in GWA studies, and that the fraction of causal loci that have been identified could be substantially greater than the fraction of causal variantsthat have been identified.

In our study, many associated variants are tightly correlated with common nsSNPs, which would not be expected if these associated common variants were proxies for collections of rare causal variants, as has been proposed²⁷. Although a substantial contribution to heritability by less common and/or quite rare variants may be more plausible, our data are not inconsistent with the recent suggestion²⁸ that a large number of common variants of very small effect mostly explain the regulation of height.

In summary, our findings indicate that additional approaches, including those aimed at less common variants, will likely be needed to dissect more completely the genetic component to complex human traits. Our results also strongly demonstrate that GWA studies can identify large numbers of loci that together implicate biologically relevant pathways and mechanisms. We envision that thorough exploration of the genes at associated loci through additional genetic, functional, and computational studies will lead to novel insights into human height and other polygenic traits and diseases.

Methods summary

The primary meta-analysis (Stage 1) included 46 GWA studies of 133,653 individuals. The in-silico follow up (Stage 2) included 15 studies of 50,074 individuals. All individuals were of European ancestry and >99.8% were adults. Details of genotyping, quality control, and imputation methods of each study are given in Supplementary Methods Table 1-2. Each study provided summary results of a linear regression of age-adjusted, within-sex Z scores of height against the imputed SNPs, and an inverse-variance meta-analysis was performed in METAL (http://www.sph.umich.edu/csg/abecasis/METAL/). Validation of selected SNPs was performed through direct genotyping in an extreme height panel (N=3,190) using Sequenom iPLeX, and in 492 Stage 1 samples using the KASPar SNP System. Family-based testing was performed using QFAM, a linear regression-based approach that uses permutation to account for dependency between related individuals²⁹, and FBAT, which uses a linear combination of offspring genotypes and traits to determine the test statistic³⁰. We used a previously described method to estimate the amount of genetic variance explained by the nominally associated loci (using significance threshold increments from P<5×10^-8 to P<0.05)¹⁸. To predict the number of height susceptibility loci, we took the height loci that reached a significance level of P<5×10^-8 in Stage 1 and estimated the number of height loci that are likely to exist based on the distribution of their effect sizes observed in Stage 2 and the power to detect their association in Stage 1. Gene-by-gene interaction, dominant, recessive and conditional analyses are described in Supplementary Methods. Empirical assessment of enrichment for coding SNPs used permutations of random sets of SNPs matched to the 180 height-associated SNPs on the number of nearby genes, gene proximity, and minor allele frequency. GRAIL and GSEA methods have been described previously^20,21. To assess possible enrichment for genes known to be mutated in severe growth defects, we identified such genes in the OMIM database (Supplementary Table 10), and evaluated the extent of their overlap with the 180 height-associated regions through comparisons with 1000 random sets of regions with similar gene content (±10%).

Go to:

Scientists stack up new genes for height    (source link HERE, September 2010)

NOTE: We have to find that article!! (comes online on Sept. 29, 2010 in the journal Nature)

Update: I have decided to use the internet to look up the term” Genetic Investigation of ANthropometric Traits”

Filed under: Announcement, Research

Wednesday, September 29, 2010 — Competitors become collaborators to achieve a common goal: the discovery of genes that influence height.

CHAPEL HILL, NC — An international team of researchers, including a number from the University of North Carolina at Chapel Hill schools of medicine and public health, have discovered hundreds of genes that influence human height.

Their findings confirm that the combination of a large number of genes in any given individual, rather than a simple “tall” gene or “short” gene, helps to determine a person’s stature. It also points the way to future studies exploring how these genes combine into biological pathways to impact human growth.

“While we haven’t explained all of the heritability of height with this study, we have confidence that these genes play a role in height and now can begin to learn about the pathways in which these genes play a role,” said study co-author Karen L. Mohlke, PhD, associate professor of genetics in the UNC School of Medicine.

The study, which appears online Sept. 29, 2010, in the journal Nature, is the result of the largest consortium of researchers to ever study the trait. The consortium, aptly named GIANT for Genetic Investigation of ANthropometric Traits.

Traits, brought together hundreds of investigators from dozens of countries, to identify which genes affect height in almost two hundred thousand different individuals.

“These investigators had once been competing with each other to find height genes, but then realized that the next step was to combine their samples and see what else could be found,” said Mohlke. “The competitors became collaborators to achieve a common scientific goal.”

That pooling of resources was necessary because the scientists knew that height was a complex genetic trait, with possibly a number of genes of small effect each adding up to influence whether a person would be taller or shorter. In this large study, forty-six smaller genome-wide association studies of height were combined and then analyzed statistically, yielding 180 different regions or genetic loci that influence the trait. “These common gene variants could explain as much as sixteen percent of the variation in height,” said study co-author Kari North, PhD, associate professor of epidemiology in the UNC Gillings School of Global Public Health.

The researchers looked to see whether the 180 regions contained more genes that underlie skeletal growth defects than would be expected if those regions were just chosen randomly across the genome. They found that the genes were not random and could in fact point to functional pathways important in influencing height.

Members of the consortium are working to uncover the “missing heritability” – the proportion of inherited variation in height that is still unexplained. Because this study looked for common genetic variants, the researchers are now going after rare genetic variants that may also play a role.

“This work is giving the field important insights into skeletal growth, height and growth defects,” said Mohlke. “And it is also showing us how similar approaches can be taken to look for genes underlying other common traits and diseases relevant to body size, like type 2 diabetes.”

Also from UNC is co-author Keri L. Monda, PhD, research assistant professor in epidemiology in the Gillings School of Global Public Health. The research was funded by the National Institutes of Health.

Media contact: Les Lang, (919) 966-9366 or llang@med.unc.edu

Me: I was very surprised to find this article on inforbarrel.com located HERE written by an individual who went by the name of Tarin. It seems that this Tarin person is very knowledgeable on the subject of height, and the scientific research involved with possible height increase. It is not written by me and I am not sure if it is Tyler’s writing but it is spot on many of the ideas that I have looked into. If it is Tyler’s work and he wants me to take it down, I will. I know that infobarrel used to be a very big website which used to pay writers a reasonably high rate to write very content strong and detailed websites. Pat Flynn of Smart Passive Income was the person who introduced me to Infobarrel. He used to write articles for the website and has been paid a high 3 figure sum every month for stuff he wrote years ago. Pat, me , and Tyler (also Sky) have all at one point being in the internet marketing niche so I would guess a lot of guys who read the article post would realize it is actually an internet marketing strategy.

I think for the very beginner who is just starting to learn about the science of possible height increase beyond the stuff one can find in an E-product, this article is a great starting post to read and to get caught up on the ideas floating around. 

Scientific Methods of Height Increase

By Tarin Oct 6, 2010  0  0

Read more at http://www.infobarrel.com/Scientific_Methods_of_Height_Increase#GsTV0oGEsPTXE9Tu.99