A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

GROWTH: Functional Data Analysis of the human height postnatal growth in an on-line application

Sledování individuálního růstu je v pediatrické praxi vyžadováno denně a často je doprovázeno potřebou podrobnějších analýz. Analýzu lidského růstu potřebují také sportovní antropologové a výzkumníci v oblasti biologie člověka. Přínosem by tedy byla pokročilá a zároveň snadno použitelná a bezplatná aplikace, která by pediatrům, auxologům a výzkumným pracovníkům v oblasti biologie člověka umožňovala provádět hloubkovou analýzu postnatálního růstu. Aplikace GROWTH byla vyvinuta na základě pochopení biologických procesů lidského růstu a matematických přístupů, které poskytují nejvhodnější model pro individuální (longitudinální) empirická data. Aplikace je navržena tak, aby ji bylo možné používat v každodenní pediatrické praxi. Poskytuje lékařům nástroje pro sledování růstu, předpovídání dosažené výšky a diagnostiku patologických růstových vzorců. Pokročilá analýza zahrnuje odhad časování hlavních růstových milníků. Současná verze je vyvrcholením několikastupňového vývoje aplikace a je založena na metodě FPCA (funkční analýza hlavních komponent) s numerickou optimalizací. Výstupní parametry jsou snadno použitelné a zobrazují se numericky i graficky.

Comparing Log-based and Exponent-based Functions to Predict Human Height by Foot Length

Background: Human height estimation is critical in medicolegal cases. This anthropometric measurement not only reveals racial differences but also aids police investigation to narrow down. Foot size varies in different races and ethnic groups, because of genetic influence on development height. The present study aimed to explore the relationship between foot size and the height of a person in a particular context. The current research also compared log-based function and exponent-based function to explore which best fits the relation between age, foot size, and height. Methods: This cross-sectional study included foot measurements from 240 participants of 6 different age groups in the age range of 2-21 years. Foot length and height were recorded as per standard methods after obtaining ethical approval from the Institutional Review Board of COMSATs University Islamabad Pakistan and gaining consent from the study participants. Correlation and regression analyses were performed using SPSS V 23. In this study, two mathematical functions of log-based and exponent-based were compared to more accurately predict the behavior of two variables. The appropriateness of these candidate functions was evaluated using statistical parameters, including the Sum of Squared Errors (SSE), R2, adjusted R2, and Root Mean Square Error (RMSE). Results: The mean height of male participants of the age group of 3-5 years was higher than that of the female participants. Among 6-10-year-olds and 11-15-year-olds, female participants presented a greater height than males. In the age groups of 16-20 and ≥21 years, males were taller than females. The average foot size of males and females of all age groups was 22.09 cm and 20.44 cm, respectively. The exponent-based candidate functions best fitted the relationship between age, foot size, and height, compared to the log-based candidate function. Conclusion: Foot size indicated the highest correlation with height and minimum standard error in the estimation of stature. Therefore, foot size provided the highest reliability and accuracy in estimating height.

Polygenic Scores and Parental Predictors: An Adult Height Study Based on the United Kingdom Biobank and the Framingham Heart Study

Human height is a polygenic trait, influenced by a large number of genomic loci. In the pre-genomic era, height prediction was based largely on parental height. More recent predictions of human height have made great strides by integrating genotypic data from large biobanks with improved statistical techniques. Nevertheless, recent studies have not leveraged parental height, an added feature that we hypothesized would offer complementary predictive value. In this study, we assessed the predictive power of polygenic risk scores (PRS) combined with the traditional parental height predictors. Our study analyzed genotypic data and parental height from 1,071 trios from the United Kingdom Biobank and 444 trios from the Framingham Heart Study. We explored a series of statistical models to fully evaluate the performance of several PRS constructed together with parental information and proposed a model we call PRS++ that includes gender, parental height, and PRSs of parents and proband. Our estimate of height with an R2 of ∼0.82 is, to our knowledge, the most accurate estimate yet achieved for predicting human adult height. Without parental information, the R2 from the best PRS-driven model is ∼0.73. In summary, using adult height prediction as an example, we demonstrated that traditional predictors still play important roles and merit integration into the current trends of intensive PRS approaches.