Quantifying genetic heterogeneity between continental populations for human height and body mass index
Jing Guo, Andrew Bakshi, Ying Wang, Longda Jiang, Loïc Yengo, Michael E. Goddard, Peter M. Visscher, Jian Yang
Abstract
Abstract Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ( $$r_{g}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> ) or genome-wide significant SNPs ( $$r_{{g\left( {GWS} \right)}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mrow> <mml:mi>g</mml:mi> <mml:mfenced> <mml:mrow> <mml:mi>GWS</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:msub> </mml:math> ) for height and body mass index (BMI) in samples of European (EUR; $$n = 49,839$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>49</mml:mn> <mml:mo>,</mml:mo> <mml:mn>839</mml:mn> </mml:mrow> </mml:math> ) and African (AFR; $$n = 17,426$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>17</mml:mn> <mml:mo>,</mml:mo> <mml:mn>426</mml:mn> </mml:mrow> </mml:math> ) ancestry. The $$\hat{r}_{g}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>g</mml:mi> </mml:msub> </mml:math> between EUR and AFR was 0.75 ( $${\text{s}}.{\text{e}}. = 0.035$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>s</mml:mtext> <mml:mo>.</mml:mo> <mml:mtext>e</mml:mtext> <mml:mo>.</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.035</mml:mn> </mml:mrow> </mml:math> ) for height and 0.68 ( $${\text{s}}.{\text{e}}. = 0.062$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>s</mml:mtext> <mml:mo>.</mml:mo> <mml:mtext>e</mml:mtext> <mml:mo>.</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.062</mml:mn> </mml:mrow> </mml:math> ) for BMI, and the corresponding $$\hat{r}_{{g\left( {GWS} \right)}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mrow> <mml:mi>g</mml:mi> <mml:mfenced> <mml:mrow> <mml:mi>GWS</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:msub> </mml:math> was 0.82 ( $${\text{s}}.{\text{e}}. = 0.030$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>s</mml:mtext> <mml:mo>.</mml:mo> <mml:mtext>e</mml:mtext> <mml:mo>.</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.030</mml:mn> </mml:mrow> </mml:math> ) for height and 0.87 ( $${\text{s}}.{\text{e}}. = 0.064$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>s</mml:mtext> <mml:mo>.</mml:mo> <mml:mtext>e</mml:mtext> <mml:mo>.</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.064</mml:mn> </mml:mrow> </mml:math> ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\hat{r}_{g}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>g</mml:mi> </mml:msub> </mml:math> differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.